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In cloud computing environment, cross-cloud live migration of virtual machines (VMs) is a major concern in these days. Cloud computing provides the users with huge, versatile and on-demand access to a bulk of customizable and configurable registered physical devices or things. It helps organizations or enterprises to share data efficiently by privately owned cloud or by the third-party servers. This type of sharing of bulky data through cloud is more efficient and reliable. In an enterprise environment, one of the essential capabilities of cloud infrastructure is VM migration. VM live migration basically involves the transference of instances that includes the operating system, runtime memory pages and active CPU states from source hub to the destination hub. In this paper, we have discussed on resource allocation algorithm which performs better in utilization of CPU, time and memory. Our proposed algorithm deals with the effective utilization of unoccupied memory, and we have also measured VM memory stack flow of total memory for cloud computing architecture. [ABSTRACT FROM AUTHOR]

Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as a utility service. From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources. This paper presents two cooperative algorithms: a Smart Elastic Scheduling Algorithm (SESA) and an Adaptive Worst Fit Decreasing Virtual Machine Placement (AWFDVP) algorithm. The proposed algorithms work to dynamically distribute the cloud system's physical resources to obtain a load-balanced consolidated system with minimal used power, memory, and processing time. SESA arranges VMs in clusters based on their memory and CPU parameters' value. Then it deals with the colocated VMs that share some of their memory pages and located on the same physical machine, as a group. Then the migration decision is made based on the evaluation for the entire system by AWFDVP. This process minimizes the number of migrations among the system, saves the consumed power, and prevents performance degradation for the VM while preserving the load-balance state of the entire system. SESA reduces the power consumption in the cloud system by 28.1%, the number of migrations by 57.77%, and performance degradation by 57.1%. [ABSTRACT FROM AUTHOR]

The interconnected cloud computing paradigm is gaining considerable attention as a fundamental emerging model of cloud computing. It allows a wide range of interactions and collaborations across multiple service providers. Despite the potential advantages of interconnected clouds, establishing trust among participating parties is a challenging issue. In this paper, we introduce a lightweight trust management algorithm based on subjective logic (InterTrust) to promote trust in interconnected clouds. The experimental results demonstrate that InterTrust is capable of producing accurate trust information with significantly low execution time and high scalability compared to application of both the well-established trust management algorithm trust network analysis with subjective logic and no trust algorithm. [ABSTRACT FROM AUTHOR]

Since the emergence of the innovative field of artificial intelligence (AI) in the 1960s, the late Hubert Dreyfus insisted on the ontological distinction between man and machine, human and artificial intelligence. In the different editions of his classic and influential book What computers can't do (1972), he posits that an algorithmic machine can never fully simulate the complex functioning of the human mind—not now, nor in the future. Dreyfus' categorical distinctions between man and machine are still relevant today, but their relation has become more complex in our increasingly data-driven society. We, humans, are continuously immersed within a technological universe, while at the same time ubiquitous computing, in the words of computer scientist Mark Weiser, "forces computers to live out here in the world with people" (De Souza e Silva in Interfaces of hybrid spaces. In: Kavoori AP, Arceneaux N (eds) The cell phone reader. Peter Lang Publishing, New York, 2006, p 20). Dreyfus' ideas are therefore challenged by thinkers such as Weiser, Kevin Kelly, Bruno Latour, Philip Agre, and Peter Paul Verbeek, who all argue that humans are much more intrinsically linked to machines than the original dichotomy suggests—they have evolved in concert. Through a discussion of the classical concepts of individuum and 'authenticity' within Western civilization, this paper argues that within the ever-expanding data-sphere of the twenty-first century, a new concept of man as 'aggregate of data' has emerged, which further erodes and undermines the categorical distinction between man and machine. This raises political and ethical questions beyond the confines of technology and artificial intelligence. Moreover, this seemingly never-ending debate on what computers should (or should not) do provokes the philosophical necessity to once again define the concept of what it is to be 'human.' [ABSTRACT FROM AUTHOR]

To achieve energy efficiency in data centers, dynamic virtual machine (VM) consolidation as a key technique has become increasingly important nowadays due to the significant amounts of power needed to operate these data centers. Most of the existing works on VM consolidation have been focused only on reducing the number of active physical machines (PMs) using VM live migration to prevent inefficient usage of resources. But on the other hand, high frequency of VM consolidation has a negative effect on the system reliability. Indeed, there is a crucial trade-off between reliability and energy efficiency, and to optimize the relationship between these two metrics, further research is needed. Therefore, in this paper a novel approach is proposed that considers the reliability of each PM along with reducing the number of active PMs simultaneously. To determine the reliability of PMs, a Markov chain model is designed, and then, PMs have prioritized based on their CPU utilization level and the reliability status. In each phase of the consolidation process, a new algorithm is proposed. A target PM selection criterion is also presented that by considering both energy consumption and reliability selects the appropriate PM. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using CloudSim toolkit. The simulation results show that the proposed approach can significantly improve energy efficiency, avoid inefficient VM migrations and reduce SLA violations. [ABSTRACT FROM AUTHOR]

Video content delivery networks face many challenges such as scalability, quality of service and flexibility. Video suppliers address them through CDN. Cloud computing and Video content Delivery as a Service (VDaaS) plays a key role in improving the content delivery standard and makes the work of content providers, easier. By hosting video contents in the cloud, the content delivery costs are minimized and the overall content delivery performance enhanced by optimization of cloud CDN. Cost optimization of the cloud-based content delivery network requires a focus on delay or throughput, the overall performance and content delivery. The content placement and content access, the QoS and the QoE in CDN can be improved by enhancing the video content delivery performance. In this paper, a unique model for video content delivery, cloud-based is developed, titled as shared storage-based cloud CDN (SS-CCDN) to achieve the objective. This design optimizes through algorithms, the effective placement of video data and dynamic update of video data. For analysis, GA, PSO, and ACO algorithms are used. The proposed model uses direct and assisted push–pull content delivery schemes for cost-efficient content delivery. The low-cost VDaaS model reduces the storage cost, keeps the latency and the traffic cost. Experimental results validate that this model, with regard to storage, traffic, and latency generate higher performance with lower price and satisfy the QoS and QoE aspects in content delivery. [ABSTRACT FROM AUTHOR]

Cloud computing has gained enormous popularity for on-demand services on a pay-per-use basis. However, a single data center may be limited in providing such services, particularly in the peak demand time as it may not have unlimited resource capacity. Therefore, multi-cloud environment has been introduced in which multiple clouds can be integrated together to provide a unified service in a collaborative fashion. However, task scheduling in such environment is much more challenging than that is used in the single cloud environment. In this paper, we propose three allocation-aware task scheduling algorithms for a multi-cloud environment. The algorithms are based on the traditional Min-Min and Max-Min algorithm and extended for multi-cloud environment. All the algorithms undergo three common phases, namely matching, allocating and scheduling to fit them in the multi-cloud environment. We perform extensive simulations on the proposed algorithms and test with various benchmark and synthetic datasets. We evaluate the performance of the proposed algorithms in terms of makespan, average cloud utilization and throughput and compare the results with the existing algorithms in such system. The comparison results clearly demonstrate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In cloud radio access network (Cloud-RAN), most previous research aims to reduce the energy consumption of the total system and ignores specific requirements of energy-saving in mobile users. For the meantime, some efforts have been made to devise efficient signal quantization techniques for fronthaul capacity restriction to improve the performance of Cloud-RAN. To improve the user-centric energy efficiency (EE), we propose two kinds of optimization algorithms in orthogonal frequency division multiple access-based Cloud-RAN in this paper, whereby Gaussian quantization and uniform scalar quantization methods are utilized. For single-RRH scenario, alternative iterative optimization algorithms are proposed to maximize users’ EE, by jointly allocating resources. Furthermore, in more general scenario for multiple RRHs, authors investigate a modified particle swarm optimization (M-PSO) algorithm to solve more complex problems, which remain non-linear and non-convex. Simulation results demonstrate the proposed schemes in this paper outperform conventional solutions and improve EE of users. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm, a considerable progress has been made in research of different aspects of it. One of the most challenging topics of interest has been service composition and optimal selection (SCOS) problem. Since CMfg is aiming towards sharing and collaborating among distributed manufacturing resources and capabilities, selecting and combining these services into a composite service to meet the user’s requirements while keeping up the optimal service performances is gaining higher emphasis. As a result, a comprehensive survey of research to date on this NP-hard problem becomes highly desirable. In this paper, first we summarize the recent advancements in CMfg and categorize them into six main areas in a brief but concise way. Then, after a short explanation of the SCOS problem, existing research work around it has been investigated and discussed in detail from the viewpoint of selection criteria, algorithms, optimization functions, correlation consideration, mapping approaches between subtasks and services, and dynamic composition. The goal of this article is to provide a comprehensive highlight for researchers who are inspired to explore work in the related areas and acquaint them with related research work done to date. [ABSTRACT FROM AUTHOR]

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

Memory allocation has a major influence on multiuser systems, cloud-based services, virtual machines, and other computer systems. Memory allocation is a process that assigns physical or virtual memory space to programs and services as efficiently and quickly as possible. Economical memory allocation management needs allocation strategies with minimum wastage. In this paper, we introduce a new memory allocation algorithm based on sequential fits and zoning for on-demand (online) cloud services. The memory is divided into multiple zones, where a subgroup of relative request sizes compete in reverse order. We use simulation to compare our new mechanism with existing memory allocation methods that have been deployed using Amazon Elastic Compute Cloud as a test bed. The proposed algorithm is more efficient, and the average saving for the normalized revenue loss is about 7% better than best-fit and 15% better than first-fit memory allocation. In addition, we show that proposed algorithm is robust and faster and has a fairness index that is superior to that of existing techniques. [ABSTRACT FROM AUTHOR]

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement's performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications "E-PRWA" in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing. [ABSTRACT FROM AUTHOR]

Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the dynamic and heterogeneous nature of user's request and provider's resource in cloud computing, the scheduling process still needs intelligent algorithms to achieve an efficient cloud resource allocation and to guarantee a good Quality of Service (QoS) for the users and their request classes. An important aspect for meeting these objectives is to design an effective task scheduling scheme which can not only satisfy users' varying priorities and QoS requirements, but also enhance providers' profit and system performances. In this paper, we introduce a new strategy to address the priority issue in both users' requests and providers' resources. We propose an efficient priority tasks scheduling called MCPTS, where the priority is adjusted according to four tasks' parameters including length, waiting time, deadline and burst time. MCPTS scheme consists of three sub-models such as tasks priority, task queueing priority and resources priority. A new hybrid multi-criteria decision-making (MCDM) method, namely ELECTRE III, and a meta-heuristic algorithm called differential evolution are proposed to evaluate and determine tasks' priorities. Further, we introduce a novel dynamic priority-queue algorithm based on queueing model. Furthermore, we adjust dynamically the resources priority based on tasks priority model in order to design an efficient and flexible relation between both resources and tasks classes. The proposed algorithm is validated through the CloudSim simulator. The experimental results indicate the superiority of MCPTS algorithm compared to other existing algorithms. Also, it shows the effectiveness of our algorithm in providing good system performance, satisfying users' priorities as well as QoS requirements, enhancing load balancing and improving resources utilization. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of one-to-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition ( SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group ( SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise. [ABSTRACT FROM AUTHOR]

The video request service of users in 5G network will explode, and adaptive bit rate technology can provide users with reliable video response. Placing video resources on edge servers close to users can overcome the problem of excessive network load similar to traditional centralized cloud platform solutions. Moreover, multiple edge servers can provide caching and transcoding support by collaboration mechanisms, which further improves users' Quality of Experience (QoE). However, the design difficulty of video caching and content distribution strategies is increased due to the diversity of collaboration mechanisms and the competition between local and collaborative services of edge servers for computing and storage resources. In order to solve this problem, video cache and content distribution problem is modeled as random integer programming problem in the multi-edge server at most two-hop collaboration scenario. In order to improve the security of video data transmission, the video stream is encrypted using an encryption algorithm based on Logistic chaotic-Quantum-dot Cellular Automata (QCA). For improving the efficiency of solving integer programming problems, this paper uses a pyramid intelligent evolution algorithm based on optimal cooperation strategy to solve this problem. Simulation experiments show that our proposed method can obtain higher QoE value compared with several newer methods. In addition, the average access delay of proposed method is shortened by more than 27.98%, which verifies its reliability. [ABSTRACT FROM AUTHOR]

Cloud Computing has grown exponentially in the business and research community over the last few years. It is now an emerging field and becomes more popular due to recent advances in virtualization technology. In Cloud Computing, various applications are submitted to the datacenters to obtain some services on pay-per-use basis. However, due to limited resources, some workloads are transferred to other data centers to handle peak client demands. Therefore, scheduling workloads in heterogeneous multi-cloud environment is a hot topic and very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. In this paper, we present three task scheduling algorithms, called MCC, MEMAX and CMMN for heterogeneous multi-cloud environment, which aim to minimize the makespan and maximize the average cloud utilization. The proposed MCC algorithm is a single-phase scheduling whereas rests are two-phase scheduling. We perform rigorous experiments on the proposed algorithms using various benchmark as well as synthetic datasets. Their performances are evaluated in terms of makespan and average cloud utilization and experimental results are compared with that of existing single-phase and two-phase scheduling algorithms to demonstrate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Currently, mobile devices are becoming the popular instrument for accessing the cloud environment. Mobile devices are resource and energy limited; they can rely on cloud computing resource to perform computationally intensive operations such as data mining and multimedia processing. This paper presents the cost and energy aware service provisioning scheme for mobile client in mobile cloud, which includes two-stage optimization process. In the first-stage optimization process, the mobile cloud user gives the unique optimal payment to the cloud provider under the cost and energy constraint and optimizes its benefit. In the second-stage optimization process, mobile cloud provider runs multiple VMs to execute the jobs for mobile cloud users; the cloud providers also need to maximize the revenue. The cost and energy aware service provisioning algorithm in mobile cloud is proposed. The proposed algorithm is involved with the cloud datacenter provider's optimization and mobile cloud user's optimization, which are conducted by two routines. In the simulation, the proposed cost and energy aware mobile cloud service provisioning algorithm is compared with other related algorithm. [ABSTRACT FROM AUTHOR]

Network-based cloud computing has rapidly expanded as an effective way of video processing and transmission. Since packet losses or errors may frequently occur in cloud computing environment during the transmission of compressed video, error concealment is applied in the decoder to prevent significant degradation of image quality. Motion vector (MV) recovery is a widely-used temporal error concealment which shows satisfactory performance in practical application of video transmission. In this paper, a fast and effective temporal error concealment algorithm for H.264/AVC is presented, which efficiently utilizes the MVs of neighboring macroblocks (MB) which are adjacent to the lost MB under different circumstances. To ensure the precision of the MV recovery, a smallest division of $$4\times 4$$ sub-block is applied, which will not bring too much complexity in the proposed algorithm. Each MV of sub-block is restored in individual method, and the recovery information is gathered from the nearby 20 sub-blocks. Simulation results under the virtual cloud environment show that our scheme can highly improve the quality of reconstructed video and obtain a gain of about 4 dB in PSNR, compared with other temporal error concealment methods in the condition of different packet loss rates and quantization parameters. The practical simplicity ensures that the proposed method can be readily applied to real-time video applications running under cloud computing environment. [ABSTRACT FROM AUTHOR]

Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed. [ABSTRACT FROM AUTHOR]

The use of cloud computing data centers is growing rapidly to meet the tremendous increase in demand for high-performance computing (HPC), storage and networking resources for business and scientific applications. Virtual machine (VM) consolidation involves the live migration of VMs to run on fewer physical servers, and thus allowing more servers to be switched off or run on low-power mode, as to improve the energy consumption efficiency, operating cost and CO2 emission. A crucial step in VM consolidation is host overload detection, which attempts to predict whether or not a physical server will be oversubscribed with VMs. In contrast to the majority of previous work which use CPU utilization as the sole indicator for host overload, a recent study has proposed a multiple regression host overload detection algorithm, which takes multiple factors into consideration: CPU, memory and network BW utilization. This paper provides further improvement along two directions. First, we provide Multi-Dimensional Regression Host Utilization (MDRHU) algorithms that combine CPU, memory and network BW utilization via Euclidean Distance (MDRHU-ED) and absolute summation (MDRHU-AS), respectively. This leads to improved results in terms of energy consumption and service level agreement violation. Second, the study explicitly takes real-world HPC workloads into consideration. Our extensive simulation study further illustrates the superiority of our proposed algorithms over existing methods. In particular, as compared to the most recently proposed multiple regression algorithm that is based on Geometric Relation (GR), our proposed algorithms provide an improvement of at least 12% in energy consumption, and an improvement of at least 80% in a metric that combines energy consumption, service-level-violation, and number of VM migrations. [ABSTRACT FROM AUTHOR]

We study a scheduling model with speed scaling for machines and the immediate start requirement for jobs. Speed scaling improves the system performance, but incurs the energy cost. The immediate start condition implies that each job should be started exactly at its release time. Such a condition is typical for modern Cloud computing systems with abundant resources. We consider two cost functions, one that represents the quality of service and the other that corresponds to the cost of running. We demonstrate that the basic scheduling model to minimize the aggregated cost function with n jobs is solvable in O(nlogn) time in the single-machine case and in O(n2m) time in the case of m parallel machines. We also address additional features, e.g., the cost of job rejection or the cost of initiating a machine. In the case of a single machine, we present algorithms for minimizing one of the cost functions subject to an upper bound on the value of the other, as well as for finding a Pareto-optimal solution. [ABSTRACT FROM AUTHOR]

Workflow scheduling has become one of the hottest topics in cloud environments, and efficient scheduling approaches show promising ways to maximize the profit of cloud providers via minimizing their cost, while guaranteeing the QoS for users' applications. However, existing scheduling approaches are inadequate for dynamic workflows with uncertain task execution times running in cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. To cover the above issue, we introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertain factors on the workflow scheduling quality. Based on this architecture, we present a scheduling algorithm, incorporating both event-driven and periodic rolling strategies (EDPRS), for scheduling dynamic workflows. Lastly, we conduct extensive experiments to compare EDPRS with two typical baseline algorithms using real-world workflow traces. The experimental results show that EDPRS performs better than those algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively. [ABSTRACT FROM AUTHOR]

The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services' migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services. [ABSTRACT FROM AUTHOR]

This paper presents a method and system for remote online monitoring of patients' cardiac rhythm with formation of an alarm signal on detection of episodes of atrial fibrillation. The structure of the system, algorithms, and decision rule for detecting episodes and algorithms for forming alarm signals for the patient and physician are proposed, and results from testing the system using a database of confirmed traces of ECG signals are evaluated. [ABSTRACT FROM AUTHOR]

One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem. [ABSTRACT FROM AUTHOR]

Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations. [ABSTRACT FROM AUTHOR]

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This article deals with the creation of new methods and algorithms for resource scheduling in heterogeneous distributed computing systems using the example of cloud computing environments (CCEs) that reduce the execution time of many incoming tasks by using those computing resources which give the highest real performance in relation to a specific task received. To this end, it is proposed to apply a multiagent approach to organizing the scheduling process: each element of the CCE includes a software agent that has the most complete and up-to-date information about the features of its computer, and the set of these agents jointly select the most appropriate tasks and subtasks taking into account the available information. The principles of construction and the method of operation of the adaptive multiagent CCE resource manager and the algorithms for the operation of resource agents and tasks are described, and the efficiency of the algorithms developed is studied using a distributed software model. [ABSTRACT FROM AUTHOR]

Nowadays, research in the field of task offloading not only pays attention to the computing performance of computing nodes, but also pays attention to the impact of energy consumption on computing performance. The current multi-core frequency conversion processors mainly use dynamic voltage frequency scaling (DVFS) technology to reduce processor frequency and energy consumption. The most representative is ARM's big.LITTLE which is a heterogeneous processor architecture, with large cores having strong performance and small cores being more energy efficient. In this article, we aim to implement energy-saving task offloading based on the big.LITTLE processor. We regard multi-core and multi-edge clouds as multiple computing nodes with different computing capabilities and express the task offloading problem as a computing resource scheduling problem. We modeled the calculation time and energy consumption through a non-cooperative game and proved that the non-cooperative game is an accurate potential game. The theory proves that non-cooperative games have at least one pure strategy Nash equilibrium point (NEP). We designed an edge cloud multi-user computing resource scheduling algorithm based on big.LITTLE processor (MUCRS). The simulation experiment results show that the MUCRS algorithm can obtain a suitable task offloading scheme, and the time and energy cost are lower than several similar algorithms. [ABSTRACT FROM AUTHOR]

Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased. Also, performing this volume of computing operations requires high processing and storage resources leading to hardware cost increment. In such systems, one of the most critical challenges is the task scheduling problem, which tries to find the optimal allocation for maximum resource usage and reduce the response time. Therefore, the purpose of this study is to design an infrastructure for smart home energy management with minimum hardware cost using cloud and fog computing and to propose a latency-aware scheduling algorithm based on virtual machine matching using meta-heuristics. Among heuristic methods, Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Thereby, a novel algorithm based on the Tabu search is proposed that is improved using approximate nearest neighbor (ANN) and fruit fly optimization (FOA) algorithms. Finally, to validate the proposed method, a case study is simulated and the proposed algorithm is implemented considering target factors of execution time, latency, allocated memory, and cost function to illustrate the performance of the algorithm. The comparison results show that the proposed algorithm outperforms the Tabu search, genetic algorithm, PSO, and simulated annealing methods. [ABSTRACT FROM AUTHOR]

Workflow scheduling has been excessively studied in different environments like clusters, grids, and clouds. Cloud is a scalable, cost-effective environment that allows users to access an unlimited amount of resources and offers a pay-as-you-go model. An increase in the users' desire to run their workflow applications on clouds leads to the development of multi-tenant environments like workflow-as-a-service platforms (WaaS). By leveraging cloud features, WaaS offers an environment where users can submit their workflows for execution with different quality of service (QoS) attributes at different. The problem of finding an appropriate scheduling algorithm considering factors like resource heterogeneity and QoS requirements is an NP-complete problem. Most of the existing algorithms in the literature are designed to schedule a single instance of a workflow or have a static behavior. Using static scheduling in dynamic environments like WaaS can lead to a low planning success rate. Besides, while it is possible to share resources between users, for simplicity purposes a majority of proposed algorithms schedule at most one task on a computing resource at any given point in time. They also consider either the time or cost as a hard constraint during scheduling. To cover these limitations in this study, we propose DDBWS, a Dynamic, Deadline and Budget-aware, Workflow Scheduling algorithm that is designed specifically for the WaaS environments. DDBWS schedules workflows by solving a multi-resource packing problem. Unlike several existing algorithms, it considers both CPU and memory demands for tasks simultaneously. Also, it leverages containers to run multiple tasks on a VM concurrently. It uses a bi-factor to control the tradeoff between cost and resource utilization during the mapping of tasks to resources. Based on real-world workflow traces, we have generated 6 different datasets of synthetic workflows. To compare the performance of the proposed scheduling algorithm, we chose two of the state-of-the-art dynamic concurrent workflow scheduling algorithms called EPSM and MW-HBDCS. We have conducted several experiments on these datasets. The results of the performed experiments show that DDBWS achieves at least 96% planning success rate on 6 different workloads which is a comparable PSR. The proposed algorithm decreases the total leased VM numbers considerably. It also outperforms its rivals in terms of the total execution cost and decreases the overall execution cost by at least 8.03% and on average 32.08%. The 95% confidence interval for this decrease is 32.08 ± 14.1 based on 12 samples. [ABSTRACT FROM AUTHOR]

The advent of virtualization technology has created a huge potential application for cloud computing. In virtualization, a large hardware resource is often broken down into smaller virtual units. These small units are then provisioned to different clients. However, these services need to be provided in such a way that resources are properly utilized. To achieve this, many of the scheduling, allocation, and provisioning issues of data centers are formulated as optimization problems. The virtual machine placement problem (VMPP) is a typical provisioning problem of data centers. In VMPP, several virtual machine requests are to be hosted on physical machines such that a minimum number of physical machines are used. This work proposes a cuckoo search (CS) inspired algorithm for solving the VMPP. To improve the algorithm's performance, new cost and perturbation functions are developed. The proposed method was tested on two well-known benchmark datasets. It outperformed the reordered grouping genetic algorithm, best-fit decreasing, first-fit decreasing, and an earlier CS method called multiCSA. [ABSTRACT FROM AUTHOR]

The objective of cloud computing is to provide seamless services using virtualization technology over the Internet to serve the Quality of Service (QoS)-driven end users requirements. In order to provide various services like Software as a Service, Platform as a Service, Infrastructure as a Service, the cloud server's datacenters are kept active, which have large electrical consumption. Due to improper utilization of resources, optimizing the servers energy consumption becomes a significant challenge for service vendors from environmental and economic perspectives as well. The challenge to provide services with a low energy consumption profile opens up a new dimension of optimized server use for intelligent management of resources (such as CPU/disk/memory), with reduced power consumption through server consolidation. This enables fewer active physical servers to provide the required services without compromising the QoS. This article presents a narrative recent advancement comprehensive as well as systematic survey of existing energy-efficient techniques along with their limitations and the challenges associated in implementing them. [ABSTRACT FROM AUTHOR]

Fog computing not only executes applications in the vicinity of the IoT devices/users but also keeps transient data which removes the need for data transfer to the cloud on regular basis. For applications' placement, an orchestrator considers the requirement of the application and the current fog status to place the application on suitable fog nodes. The orchestrator itself may be placed on fog or cloud nodes. In a centralized approach, a fixed entity that works as an orchestrator is prone to single point failure, less mobility support, etc. Therefore, the literature advises for a decentralized approach in which a nearby fog node is selected to act as an orchestrator. Poor selection of Fog Orchestrator Nodes (FONs) may result in the performance degradation of the system. None of the earlier work proposed the selection of FON for the placement of the applications on the fog nodes. Towards this, a brief but latest survey of the works, to understand the FON selection problem, is done along with the importance of decentralized approach in the FON selection problem. Further, few performance metrics have been identified which helps in the FON selection. A lightweight FON Selection model (FONS) is proposed so that even the least powerful IoT devices can select a FON. The proposed work is validated by incorporating the FON selection algorithm in one state of art to observe a remarkable improvement in application placement. [ABSTRACT FROM AUTHOR]

Rise of Big Data techniques has led to the requirement for low latency analysis of high-velocity continuous data streams in real time. Several solutions, including Stream Processing Systems (SPSs), have been developed to enable real-time distributed stream processing. However, emerging application scenarios such as smart cities and wearable assistance that involve highly variable data rates keep on posing new challenges to the established stream processing engines for maintaining cost-effective executions. To cater to such scenarios, many modern SPSs have been proposed that leverage Cloud environment. The run-time scalability incorporated in these SPSs is in their early adaptations and are based on fixed scale sizes. Moreover, these scaling approaches do not adequately consider both the structure of the hosted streaming applications and the characteristic features of the underlying Cloud environment. Achieving true cost benefits of orchestrating streaming applications on Cloud-based pay-as-you-go model while maintaining the desired QoS, necessitates that both these issues are accounted in making the scaling decisions. This work presents a heterogeneity-aware, efficient auto-scaling strategy StreamScale-H which addresses both these issues. Simulation experiments, on representative stream applications, indicate that the proposed StreamScale-H auto-scaling algorithm exhibits much better performance in comparison with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A hybrid of supervised (artificial neural network), unsupervised (clustering) machine learning, and soft computing (interval type 2 fuzzy logic system)-based load balancing algorithm, i.e., clustering-based multiple objective dynamic load balancing technique (CMODLB), is introduced to balance the cloud load in the present work. Initially, our previously introduced artificial neural network-based dynamic load balancing (ANN-LB) technique is implemented to cluster the virtual machines (VMs) into underloaded and overloaded VMs using Bayesian optimization-based enhanced K-means (BOEK-means) algorithm. In the second stage, the user tasks are scheduled for underloading VMs to improve load balance and resource utilization. Scheduling of tasks is supported by multi-objective-based technique of order preference by similarity to ideal solution with particle swarm optimization (TOPSIS-PSO) algorithm using different cloud criteria. To realize load balancing among PMs, the VM manager makes decisions for VM migration. VM migration decision is done based on the suitable conditions, if a PM is overloaded, and if another PM is minimum loaded. The former condition balances load, while the latter condition minimizes energy consumption in PMs. VM migration is achieved through interval type 2 fuzzy logic system (IT2FS) whose decisions are based on multiple significant parameters. Experimental results show that the CMODLB method takes 31.067% and 71.6% less completion time than TaPRA and BSO, respectively. It has maintained 65.54% and 68.26% less MakeSpan than MaxMin and R.R algorithms, respectively. The proposed method has achieved around 75% of resource utilization, which is highest compared to DHCI and CESCC. The use of novel and innovative hybridization of machine learning, multi-objective, and soft computing methods in the proposed algorithm offers optimum scheduling and migration processes to balance PMs and VMs. [ABSTRACT FROM AUTHOR]

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm's consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human–computer interaction computing mode that can be implemented in cloud. The purposes of stabilizing the generation process and realizing the interaction between human and computing process are achieved by inputting artificial conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the "input bottleneck" of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks. [ABSTRACT FROM AUTHOR]

Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem; thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. Dragonfly metaheuristic algorithm explores and exploits a solution space by the inspiration of hunting and emigration behavior of dragonflies in nature. But it suffers from the premature convergence of the algorithm to an undesirable when explores the solution space. In this research, an improved dragonfly algorithm (applying biogeography-based algorithm, Mexican hat wavelet and dragonfly algorithm—BMDA) is presented to resolve the premature convergence by applying a mutation phase that is the combination of the biogeography-based optimization (BBO) migration process and Mexican hat wavelet transform in dragonfly algorithm. Then, it is applied for dynamically scheduling tasks under the BMDDSF framework (BBO-Mexican hat wavelet-dragonfly dynamic scheduling framework) in the cloud computing environment. The purpose is customizing a metaheuristic algorithm to be applied in the resource manager of cloud computing to improve its performance. The BMDA algorithm was firstly evaluated for the mean error in comparison with the baseline algorithms using the CEC2017 benchmark functions. Then, the performance of the BMDDSF framework in cloud computing was tested using NASA parallel workload compared with baseline methods in terms of execution time, response time and reduction of service-level agreement violation. The experiments showed that the presented method outweighed the baseline approaches. [ABSTRACT FROM AUTHOR]

Utilizing from energy-aware solutions along with maintaining service-level agreements is one of the most important research issues in cloud computing. In the proposed model, monitoring the status of resources and analysing the obtained data have led to proper placement and consolidation of virtual machines through targeted migrations at the right time. In the virtual machine placement policy, the definition of absorption mode has been used in simulated annealing algorithm in addition to the formation of virtual clusters to prevent from unlimited increase in the length of created Markov chain in any temperature while maintaining the convergence. The results of simulations obtained from various scenarios in CloudSim indicated the proposed model has led to energy savings up to 14.3%, 19% and 21% on low load, average load and high load, respectively, compared to the best understudy algorithm, while the SLA violation has also led to a decrease in all three modes. [ABSTRACT FROM AUTHOR]

Cloud computing provides on demand services to cloud users, and one among them is storage. Currently, large amount of data gets generated and demand an enormous storage. Users can avail the privilege to store their data remotely and can access them through Internet. Of course, the adoption of cloud lends the kind of storage that the user wants. Since data gets accumulated, the time it takes to store and retrieve the data is very long and difficult. Also, unfortunately the existing method of storage is to be optimized for better performance. The factors that affect the performance of cloud storage are response time, data availability and migration cost. Hence to improve these factors the data can be replicated to multiple locations. The decision on which data to be replicated, number of replicas to be created, where the replica has to be placed, management of the replicated data and the provision of optimal replica to the user are the major challenges involved in dynamic replication. We intend to propose, a novel dynamic data replication strategy with intelligent water drop (IWD) algorithm to address the challenges of replication and for the management of cloud storage. The popularity and size of the data are considered for replication. A swarm intelligence based optimization algorithm named IWD algorithm is used to optimize the process of replication and management of storage in cloud. We have compared our D2R-IWD algorithm with popular optimization techniques such as PSO, GA and found out that our methodology gives better result in terms of access efficiency for several test cases thereby improve the performance of cloud. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems(CPS) serves as an interdisciplinary effort that incorporates cyber vector as well as physical vector. The latter can generate exponentially growing amounts of data. How to process CPS big data systematically and efficiently is the key to the breakthrough of offering prospective and personalized services for each individual user and entity involved. In recent years, much research has been proactively conducted on advancing specific scenario or algorithm. However, few surveys value their integration. For good measure, the synthesis remains a fundamental challenge. Subsequently, we fill the gap in the literature by constructing cloud computing, fog computing and edge computing as a whole to inspire on new architectures and cross utilizations. Moreover, bringing the enthusiasm of traditionally solitude entities into play is crucial. In this exploratory study, we examine definitions of CPS as well as the three aforementioned computing paradigms and then shed new light on comprehensively established frameworks. We also survey on the application level of Cloud-Fog-Edge Computing in CPS respectively and dive into diversified algorithms and strategies to embed big data applications into a more intelligent and convenient society with current deficiencies and future research directions followed. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing became a very important way of processing very large and complicated jobs and services. To serve millions of users with a high satisfaction regarding cost and time requires a powerful way to schedule cloud users' jobs. Job scheduling in a cloud computing environment is an NP-hard problem. Many scheduling algorithms have been proposed by researchers to solve such a complicated problem. In this research, a hybrid Tabu–Harmony task scheduling algorithm in cloud computing is proposed, and the proposed algorithm combines the benefits of both the Tabu search and the Harmony search algorithms in order to enhance the quality of the results. The proposed algorithm is evaluated in terms of throughput, makespan and total cost and achieved a better result compared to Tabu search, Harmony search and round-robin in terms of makespan and cost. [ABSTRACT FROM AUTHOR]

Recently, Service-level agreement (SLA) is deemed to be an integral aspect for on-demand provisioning of scalable resources on Cloud. SLA defines important constraints for instance guaranteed quality of service (QoS), pricing, fault-tolerant availability, security, and period of service. Currently, there is a dire need of SLA-based scheduling that improves resources utilization on Cloud. Scheduling with reduced execution time and cost may adversely affect resource utilization. To overcome this issue, we present a cost-efficient SLA-based load balancing scheduler, namely SLA-RALBA, for heterogeneous Cloud infrastructures. The proposed technique supports three levels of SLA opted by the Cloud users. The proposed novel technique incorporates the execution cost for the successful execution of users' services to elevate the resource utilization on Cloud. The SLA-RALBA is simulated for performance analysis using the benchmark GoCJ and HCSP datasets. The performance results of the SLA-RALBA are compared with the existing schedulers, namely Execution-MCT, Profit-MCT, SLA-MCT, Execution-Min-Min, Profit-Min-Min, and SLA-Min-Min in terms of average resource utilization, execution time, and cost of the Cloud services. The obtained results reveal that SLA-RALBA provides an even trade-off between execution time and cost of the services by guaranteeing a drastic improvement in resource utilization on Cloud than existing algorithms. [ABSTRACT FROM AUTHOR]

Workload prediction is an essential prerequisite to allocate resources efficiently and maintain service level agreements in cloud computing environment. However, the best solution for a prediction task may not be a single model due to the challenge of varied characteristics of different systems. Thus, in this work, we propose an ensemble model, namely ESNemble, based on echo state network (ESN) for workload time series forecasting. ESNemble consists of four main steps, including features selection using ESN reservoirs, dimensionality reduction using kernel principal component analysis, features aggregation using matrices concatenation, and regression using least absolute shrinkage and selection operator for final predictions. In addition, necessary hyperparameters for ESNemble are optimized using genetic algorithm. For experimental evaluation, we have used ESNemble to combine five different prediction algorithms on three recent logs extracted from real-world web servers. Through our experimental results, we have shown that ESNemble outperforms all component models in terms of accuracy and resource allocation and presented the running time of our model to show the feasibility of our model in real-world applications. [ABSTRACT FROM AUTHOR]

Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) technique to realize the efficient operation of ciphertext without decryption. However, most current homomorphic encryption algorithms only support limited data types, making it difficult to be widely applied in actual environment. To address this limitation, we propose a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm not only expands the data types supported by the existing fully homomorphic encryption algorithms, but also utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speed-up exceeding 5, which not only solves the limited application problem of the existing fully homomorphic encryption algorithm, but also meets the requirements for the efficient homomorphic encryption of floating-point numbers in cloud computing environment. [ABSTRACT FROM AUTHOR]

Service interface is a critical component in a service-oriented architecture (SOA). As first-class design artifact, a service interface should be properly designed to provide best practice of third-party reuse. However, a very common bad service design practice in existing SOAs is to place semantically unrelated operations implementing several abstractions in a single interface. Poorly designed service interfaces can have a negative effect on all client applications that use these services. Indeed, services with such poor interface structure tend to be difficult to comprehend, maintain and reuse in business processes, leading to unsuccessful services. Necessarily, then, service designers should "refactor", i.e., restructure, their service interface into smaller, more cohesive interfaces, each representing a specific abstraction. To address this problem, we introduce a novel approach, namely WSIRem, to support service's developers in improving the modularization of their service interfaces. WSIRem is based on a multi-objective search-based optimization approach to find the appropriate modularization of a service interface into smaller, more cohesive and loosely coupled interfaces, each implementing a distinct abstraction. WSIRem has been empirically evaluated on a benchmark of 22 real-world Web services provided by Amazon and Yahoo. Results show that the automatically identified interfaces improved the services interface structure. Qualitative evaluation of WSIRem with developers showed the performance of WSIRem in terms of understandability, where the new WSIRem interfaces were recognized as 'relevant' from developers point of view with more than 73% of precision and 77% of recall. Overall, the obtained results show that WSIRem outperforms state-of-the-art approaches relying on traditional partitioning techniques. [ABSTRACT FROM AUTHOR]

In recent years, with the increase in users in social network, the social network has had the feature of big data. The large-scale social network has become an indispensable part in people's life. However, the traditional data mining technology cannot suit the large-scale social network. Thus, it is urgent to develop a more suitable mining technology for the large-scale social network. In this section, a crawler model based on semantic analysis and spatial clustering is proposed firstly. Then, the content extraction model based on document object model tree is built to extract the target text information from the links fetched by the proposed crawler model. The similarities between textual information in different regions are computed to choose the important information. Moreover, a two-stage topic clustering model based on time information is presented. The time information is introduced into the similarity computation between two posts or clusters. The single-pass algorithm is improved and applied in different clustering stage to improve the clustering accuracy. Finally, the proposed algorithms are evaluated on Hadoop platform. The Hadoop platform can effectively reduce the computing time and improve the server quality of users in large-scale social network. Meanwhile, the experiments demonstrate that the proposed algorithms are suitable for the data processing in large-scale social network. [ABSTRACT FROM AUTHOR]

With the development of content-sharing and collaborative computing services such as online social networks, scientific workflow, there are huge amounts of data generated. To process this tremendous amount of data, multi-cloud system that integrates multiple clouds together to provide a unified service in a collaborative manner has been introduced. However, task scheduling in such heterogeneous multi-cloud environment is very challenging. To reduce response delay caused by cross-data centers file access, we proposed a replica-aware task scheduling algorithm based on data replication. For speeding up data access in multi-cloud cooperative caches, we presented a load balanced cache placement algorithm based on Bayesian networks. In our scheduling algorithm, combined transferring computation with transferring data, resource matching is accomplished according to node locality. Only non-local unassigned and failed map tasks' input data are replicated and transferred in advance to target nodes to expedite task execution. In our cache placement method, based on Bayesian networks the next execute task is predicted. In accordance with caching profit and recycling cost, cache prefetching files are selected. For each prefetching file, according to load balancing, target placement node is determined. Extensive experimental results show that the performance of our proposed replica-aware task scheduling algorithm is better than benchmark scheduling algorithms in terms of node locality ratio and job response time, and our load balanced cache placement algorithm outperforms the baseline caching algorithms in performance of prefetching hit ratio and execution time saving ratio. [ABSTRACT FROM AUTHOR]