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As is well known that the global optimization ability of the Fruit fly Optimization Algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization Algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, Role-Based Access-Control (RBAC) model is deployed in large organizations. This paper addresses the scalability problem of the online stored rules. This problem affects the performance of the access control system due to increasing number of shared resources and/or number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with Role-To-Role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed Role-Mapping algorithm. A comparative study is performed to evaluate the proposed algorithm's performance with concerning the Rule-Store size and the authorization response time. According to the results, it is found that the proposed algorithm reduces the number of stored rules which minimizes the Rule-Store size and reduces the authorization response time. Additionally, this paper proposes applying a concurrent approach on the RTR mapping model using the proposed Role-Mapping algorithm to achieve more savings in the authorization response time. Therefore, it will be suitable in highly-collaborative cloud environments. [ABSTRACT FROM AUTHOR]

The physical cloud servers consists of n number of machines for serving the requirements posted via Virtual Machines by the users. The allocation of requested Virtual Machines to appropriate Physical Servers is one among the challenging task exists in Cloud Computing. During the deployment phase several objectives needs to be considered which includes the total power consumption, the resource wastage, network propagation delay and so on. Allocating VM's to PM's with such fashion will lead the service providers financially. In this paper a swarm-based algorithm is modified for solving VM placement in terms of multi-objective perspective. Minimizing total power consumption and minimizing the resource wastage are the objectives considered in this paper. The results show the significance of the proposed model in terms of objective space and with the performance indicators such as ONGV and Spacing ( S p ). [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, a growing number of workflows are deployed on cloud platform that can dynamically provides cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow for minimizing the execution cost and the workflow completion time. Aiming at the problem that the maximum completion time and cost of multiple workflows are too high, this paper proposes a model of dynamic multi-workflow scheduling in cloud environment and a new scheduling algorithm which is named as MT (multi-workflow scheduling technology). In MT, the heterogeneity of resources is considered when calculating the priority of tasks. Then, the technique for order of preference by similarity to ideal solution (TOPSIS) method is used to rank the resources when selecting resources for tasks. Finally, MT takes the estimated minimum completion time of the workflow and the cost of the task as two attribute indexes in TOPSIS decision matrix. Also, it uses a fixed reference point instead of calculating ideal solution, which ensures the uniqueness of the evaluation criteria when there is a change in the number of resources. Simulation experiments are illustrated to verify the effectiveness of the proposed algorithm in reducing the maximum completion time and cost of multiple workflows. Compared with the state-of-the-art methods, the maximum completion time and cost can be reduced by at most 17 and 9 % , respectively. [ABSTRACT FROM AUTHOR]

The distributed architecture of cloud computing necessitates robust defense mechanisms to secure network-accessible resources against a diverse and dynamic threat landscape. A Network Intrusion Detection System (NIDS) is pivotal in this context, with its efficacy in cloud environments hinging on its adaptability to evolving threat vectors while mitigating false positives. In this paper, we present a novel NIDS algorithm, anchored in the Transformer model and finely tailored for cloud environments. Our algorithm melds the fundamental aspects of network intrusion detection with the sophisticated attention mechanism inherent to the Transformer model, facilitating a more insightful examination of the relationships between input features and diverse intrusion types, thereby bolstering detection accuracy. We provide a detailed design of our approach and have conducted a thorough comparative evaluation. Our experimental results demonstrate that the accuracy of our model is over 93%, which is comparable to that of the CNN-LSTM model, underscoring the effectiveness and viability of our Transformer-based intrusion detection algorithm in bolstering cloud security. [ABSTRACT FROM AUTHOR]

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results. [ABSTRACT FROM AUTHOR]

The attribute-based encryption (ABE) scheme is suitable for access control of ciphertext in cloud computing. Kowalczyk and Wee proposed an adaptively secure attribute-based encryption scheme that supports NC 1 circuits. However, the ciphertext length increases rapidly with the depth of the circuit, resulting in an increase of the computational complexity of the encryption and decryption algorithms. In this paper, to overcome this challenge, a ciphertext-policy ABE scheme that supports circuits with fan-in n is proposed. First, we design new pebble rules for secret sharing in circuits with fan-in n, improving the compactness of the security reduction. Then, the new secret sharing scheme is embedded in the encryption algorithm, which is the key to improving efficiency. Moreover, we prove the adaptive security of the scheme by using a piecewise guessing framework and dual-system encryption. Finally, by comparison analysis, this scheme exhibits a better performance. [ABSTRACT FROM AUTHOR]

Since scientific workflow scheduling becomes a major energy contributor in clouds, much attention has been paid to reduce the energy consumed by workflows. This paper considers a multi-objective workflow scheduling problem with the budget constraint. Most existing works of budget-constrained workflow scheduling cannot always satisfy the budget constraint and guarantee the feasibility of solutions. Instead, they discuss the success rate in the experiments. Only a few works can always figure out feasible solutions. These methods work, but they are too complicated. In workflow scheduling, it has been a trend to consider more than one objective. However, the weight selection is usually ignored in these works. The inappropriate weights will reduce the quality of solutions. In this paper, we propose an energy-aware multi-objective reinforcement learning (EnMORL) algorithm. We design a much simpler method to ensure the feasibility of solutions. This method is based on the remaining cheapest budget. Reinforcement learning based on the Chebyshev scalarization function is a new framework, which is effective in solving the weight selection problem. Therefore, we design EnMORL based on it. Our goal is to minimize the makespan and energy consumption of the workflow. Finally, we compare EnMORL with two state-of-the-art multi-objective meta-heuristics in the case of four different workflows. The results show that our proposed EnMORL outperforms these existing methods. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT. [ABSTRACT FROM AUTHOR]

Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance. [ABSTRACT FROM AUTHOR]

With the development of cloud-native technologies, Kubernetes becomes the standard of fact for container scheduling. Kubernetes provides service discovery and scheduling of containers, load balancing, service self-healing, elastic scaling, storage volumes, etc. Although Kubernetes is mature with advanced features, it does not consider reducing the cost in Kubernetes clouds using the factor of communication frequent between pods while scheduling pods, nor does it have a rescheduling mechanism to save cost. Hence, we propose a cost-efficient scheduling algorithm and a rescheduling algorithm to reduce the cost of communication-intensive and periodically changing web applications deployed in Kubernetes, respectively. Network communication-intensive pods are scheduled to the same node by the scheduling algorithm based on Improved Beetle Antennae Search. According to the changing pod intimacy relationship, the rescheduling algorithm is completed through the replacement of new and old pods to reduce the cost. In addition, this paper evaluates the proposed algorithms in terms of cost and performance on a Kubernetes cloud. The result shows that the cost consumption of Kubernetes clusters in cloud environment is reduced by 20.97% on average compared with the default Kubernetes framework. [ABSTRACT FROM AUTHOR]

Resource management is an important issue in the cloud computing paradigm. The maximum Utilization of resources leads the service providers to get maximum profit. The suitable resources in the cloud data centre are needed to satisfy the user needs. The selection of the right resource is very essential in the data centre. Metaheuristic algorithms are used to perform the task allocation in the cloud computing environment. In this paper, a novel task allocation algorithm is proposed using Cat Swarm Optimization and BAT algorithm. BAT algorithm helps CSO algorithm to escape from a pre convergence issue. The proposed HGCSBAT algorithm results are evaluated and compared with the popular algorithms CSO and BAT algorithms. The HGCSBAT results are better than Genetic algorithm, Cat Swarm Optimization, and BAT algorithms in terms of availability and throughput. [ABSTRACT FROM AUTHOR]

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%. [ABSTRACT FROM AUTHOR]

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms. [ABSTRACT FROM AUTHOR]

Workflow scheduling is one of the most popular and challenging problems in cloud computing. However, among the studies on cloud workflow scheduling, very few consider the fairness among workflow tasks which could significantly delay the workflows and hence deteriorates user satisfaction. In this paper, we propose a workflow scheduling algorithm based on stable matching game theory to minimize workflow makespan and ensure the fairness among the tasks. The local optimization methods based on critical path and task duplication are developed to improve the performance of the algorithm. In addition, a novel evaluation metric is proposed to measure the fairness among workflow tasks. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with other four representative algorithms. Experimental results demonstrate that our algorithm outperforms the other compared algorithms in terms of all three performance metrics under different workflow applications. [ABSTRACT FROM AUTHOR]

Many researchers have recently considered patients' health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients' condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods. [ABSTRACT FROM AUTHOR]

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment is achieved through the virtualization process. Based on system experience, this paper proposes the priority-based backfilling backpropagation neural network (PBF-NN) hybrid scheduling algorithm for measuring makespan and execution cost accurately. The backfill algorithm is used to schedule tasks to the available resources. The percentage of migration is reduced when this algorithm is used compared to the First Come First Serve algorithm. Then, the Berger model is used to measure the fairness of resource allocation. The system decides task reallocation based on the fairness value. The backpropagation neural network handles the virtual machine placement process with necessary training and testing. The proposed algorithm dynamically allocates the tasks and reduces the utilization of resources. We use an experimental study to illustrate how the proposed system enables higher efficiency in cost, makespan, and performance. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

When a conflict or plan change occurs in the process of cloud service execution, if the initial scheme is not rescheduled in time, it can lead to a delivery delay and a series of nonlinear losses. The cloud service rescheduling (CSRS) problem is considered as a particularly hard combinatorial optimization problem with multi-participants, which is significantly different from traditional flow shop rescheduling problem. It is difficult to coordinate the relationship of service suppliers and construct a reward mechanism to encourage them to participate in the rescheduling. According to the various strategies of service suppliers that do or do not participate in the rescheduling, a game theory model was constructed. The main aim of this paper is to explore how to minimize the reward of suppliers and ensure that the game model develops in a desired direction. Hence, a novel hybrid approach using shuffled frog leading algorithm (SFLA) and Nash equilibrium (NE) theory was proposed to solve the CSRS problem. In addition, we carried out a case study to demonstrate that the SFLA and NE approaches can be implemented for realistically sized problem sets and that the cost savings are significant [ABSTRACT FROM AUTHOR]

The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this paradigm for scientific applications. Such a scalable platform can be an ideal option for the execution of loosely coupled parallel applications, such as scientific workflows. Scientific workflows are regarded as one of the most important elements in different scientific fields in which a complex application may be divided into several dependent tasks. Given that the cost of leasing multicore VMs on the cloud will rise with an increase in the number of processing cores, an efficient scheduling algorithm focusing on utilizing multicore resources can significantly reduce execution costs. As an extension of its authors' previous research, the current paper proposes a heuristic scheduling algorithm, the Cluster Dividing Algorithm that concentrates on expanding the utilization of multicore resources to reduce execution costs while also meeting the user-defined deadline. To increase resource utilization, the proposed scheduling employs different techniques, such as task clustering, directed graph leveling, and task duplicating. The experimental results reveal that the presented algorithm leads to lower execution costs while complying with the deadline. [ABSTRACT FROM AUTHOR]

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

In the context of the interconnection of everything, the edge data are experiencing explosive growth, and the bandwidth and computing resources of cloud computing cannot be efficiently processed. Edge computing, with its low latency, high throughput and low network pressure, has become a very effective mode to deal with massive data. Due to the increasing number of end users, a large number of data are generated on the edge of the network, and the timeliness of users' service requirements is constantly improving, so further reducing the delay of cloud service network is still a major challenge. Cache is an effective solution to this problem. In order to make full use of the limited edge device space, a dynamic cache replacement algorithm is proposed based on edge popularity and node heat, which caches popular content in the core node and non-popular content in the secondary node, so as to improve the hit rate of the whole network and reduce the server load. In order to meet the increasing demand of data content access in the network, a cooperative caching algorithm is proposed. The idea of this algorithm is to put the cache object in the proper node, so that the user's request can get timely response. Thus, the availability of the object is improved and the network delay is reduced. In the edge computing environment of campus network, dynamic cache replacement algorithm and cooperative cache algorithm are evaluated. The experimental results show that the dynamic cache replacement algorithm proposed in this paper is better than the benchmark replacement algorithm in cache hit rate, server load, average delay and average hops, and the cooperative cache algorithm is better than the benchmark cooperative cache algorithm in node hit rate and average hops. [ABSTRACT FROM AUTHOR]

As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been studied as a core issue by industry and academia. In the existing research, the main goal is completion time or load balancing. However, as the expansion of cluster size, energy consumption becomes a problem that must be faced. In this paper, the first of maximum loss scheduling algorithm is proposed. The algorithm is a low-power algorithm that can greatly reduce the energy consumption of cloud computing clusters through loss comparison rule. The effect of this method is more obvious as the cluster size and the number of tasks increase. Experimental simulation results show that the proposed method is significantly better than the Max–Min, Min–Min, Sufferage and E-HEFT algorithms. Compared to Min–Min, Max–Min, Sufferage and E-HEFT algorithms, average completion time of the algorithm reduces 16%, 12%, 8% and 14%, respectively. At the same time, the load balancing effect is also better than Min–Min and Sufferage algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time. [ABSTRACT FROM AUTHOR]

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas of big data. The widespread popularity of big data processing platforms using MapReduce framework is the growing demand to further optimize their performance for various purposes. In particular, enhancing resources and jobs scheduling are becoming critical since they fundamentally determine whether the applications can achieve the performance goals in different use cases. Scheduling plays an important role in big data, mainly in reducing the execution time and cost of processing. This paper aims to survey the research undertaken in the field of scheduling in big data platforms. Moreover, this paper analyzed scheduling in MapReduce on two aspects: taxonomy and performance evaluation. The research progress in MapReduce scheduling algorithms is also discussed. The limitations of existing MapReduce scheduling algorithms and exploit future research opportunities are pointed out in the paper for easy identification by researchers. Our study can serve as the benchmark to expert researchers for proposing a novel MapReduce scheduling algorithm. However, for novice researchers, the study can be used as a starting point. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible and intelligent service composition algorithms are urgently need to study. Based on this, we propose a service composition strategy under the multi-cloud environment, and we propose an ant colony optimization algorithm (ACO) based on the multi-pheromone mechanism to optimize the quality of service (QoS). To avoid the occurrence of local optima, we further introduce the mutation operation of the genetic algorithm. Finally, the simulation results show that the proposed algorithm can achieve better QoS parameters such as latency and response time while ensuring the stability of services. [ABSTRACT FROM AUTHOR]

A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors' previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method. [ABSTRACT FROM AUTHOR]

The combination of cloud computing with the Internet of Things has made fundamental changes in areas from industry, healthcare, traffic, and transportation to home appliances and even personal lives. Billions of devices and users are connected through these platforms disseminating enormous amounts of data leading to performance degradation, which has generated a demand for prior application placement planning. This paper focuses on the minimization of application delay and network usage by proposing a genetic-based service placement algorithm in fog-cloud environments. Throughout this work, a penalty-based approach to target both the delay and the number of time-consuming cloud placements is introduced, which explores the solution pool as a function of generations. This helps in exploring a wider space at the beginning and gradually intensifying the effect of penalty in the next generations. In a separate phase, the proximity of the applications to the users is taken into account as well. This is done through the chromosome selection process by using a priority value that identifies the proximity of dependent modules. The results of simulations demonstrate that the proposed algorithm achieved improvements regarding delay, network usage, energy consumption, and cost. [ABSTRACT FROM AUTHOR]

This paper presents a Power and Resource Utilization-Aware Virtual Machine Scheduling (PRUVMS) algorithm for strengthening resource utilization and diminishing the energy consumption of servers in the cloud environment. The PRUVMS algorithm enhances the resource utilization by migrating the VMs from the underloaded/overloaded servers to a normal server, and it reduces the energy consumption by shutting down the underloaded servers after migrating the VMs. For selecting the suitable server for the VM placement, the ranking of the available servers is evaluated. An illustrative example is presented to validate the PRUVMS algorithm. Further, the PRUVMS algorithm is tested on the PlanetLab workload using the CloudSim simulator. The proposed PRUVMS algorithm improves resource utilization by 68.22% and 37.53% and decreases the energy consumption by 35.53% and 31.34% in comparison with PABFD and CAVMP algorithms, respectively. The improvement in computational results shows the acceptability of the proposed scheduling algorithm in the cloud environment. [ABSTRACT FROM AUTHOR]

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]

Task scheduling and data replication are highly coupled resource management techniques that are widely used by cloud providers to improve the overall system performance and ensure service level agreement (SLA) compliance while preserving their own economic profit. However, balancing the trade-off between system performance and provider profit is very challenging. In this paper, we propose a novel scheduling algorithm called Bottleneck and Cost Value Scheduling (BCVS) algorithm coupled with a novel dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). The main goal is to improve data access effectiveness in order to meet service level objectives in terms of response time SLORT and minimum availability SLOMA, while preserving the provider profit. The BCVS algorithm focuses on reducing system bottleneck situations caused by data transfer when the CEMR focuses on preventing future SLA violations and guaranteeing a minimum availability. An economic model is also proposed to estimate the cloud provider profit. Simulation results indicate that the proposed combination of scheduling and replication algorithms offers higher monetary profit for the cloud provider by up to 30% compared to existing strategies. Moreover, it allows better performance. [ABSTRACT FROM AUTHOR]

Scientific workflow applications have a large amount of tasks and data sets to be processed in a systematic manner. These applications benefit from cloud computing platform that offer access to virtually limitless resources provisioned elastically and on demand. Running data-intensive scientific workflow on geographically distributed data centres faces massive amount of data transfer. That affects the whole execution time and monitory cost of scientific workflows. The existing efforts on scheduling workflow concentrate on decreasing make span and budget; little concern has been paid to contemplate tasks and data sets dependency. In this paper, we introduced workflow scheduling technique to overcome data transfer and execute workflow tasks within deadline and budget constraints. The proposed techniques consist of initial data placement stage, which clusters and distributes datasets based on their dependence and replication-based partial critical path (R-PCP) technique which schedules tasks with data locality and dynamically maintains dependency matrix for the placement of generated data sets. To reduce run time datasets movement, we use interdata centre tasks replication and data sets replication to make sure data sets availability. Simulation results with four workflow applications illustrate that our strategy efficiently reduces data movement and executes all chosen workflows within user specified budget and deadline. Results reveal that R-PCP has 44.93% and 31.37% less data movement compared to random and adaptive data-aware scheduling (ADAS) techniques, respectively. R-PCP has 26.48% less energy consumption compared with ADAS technique. [ABSTRACT FROM AUTHOR]

Workload scheduling in cloud computing is currently an active research field. Scheduling plays an important role in cloud computing performance, especially when the platform is used for big data analysis and as less predictable workloads dynamically enter the clouds. Finding the optimized scheduling solution with different parameters in different environments is still a challenging issue. In dynamic environments such as cloud, scheduling strategies should feature rapid altering to be able to adapt more easily to the changes in input workloads. However, achieving an optimized solution is an important issue, which has a trade-off with the speed of finding the solution. In this article, an ordinal optimization method is proposed that considers the volume of workloads, load balancing and the volume of exchanged messages among virtual clusters, considering the replications. The algorithm in the present paper is based on ordinal optimization (OO) and evolutionary OO. In any time periods, a criterion is calculated to determine the similarity of workloads in two-consequence time periods, which is appropriate for timely changes in the scheduling procedure. In this paper, considering more than one parameter, a proper scheduling would be created for each time period. This scheduler is an organization for the number of virtual machines for each virtual cluster, but if there is a desirable similarity between workloads of two-consequence time periods, this procedure would be ignored. The results show that a more optimized solution is obtained in comparison with the rated methods, such as blind pink, OO, Monte Carlo and eOO in a reasonable time. The suggested method is flexible and it is possible to change the weight ratio of the proposed criteria in different environments to be consistent with different environmental conditions. The results show that proposed method achieved up to 28% performance improvement in comparison with eOO. [ABSTRACT FROM AUTHOR]

Cloud computing, a novel and promising methodology in the distributed computing domain, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. These workflow applications have a constraint that each of them must completed within the limited time (deadline constraint). Therefore, scheduling a workflow with deadline constraints is increasingly becoming a crucial research issue. However, many analytical reviews on scheduling problems reveal that existing solutions fail to provide cost-effective solutions and they do not consider the parameters like CPU performance variation, delay in acquisition and termination of Virtual Machines (VMs). This paper presents a Cost-Effective Firefly based Algorithm (CEFA) to solve workflow scheduling problems that can occur in an Infrastructure as a Service (IaaS) platform. The proposed CEFA uses a novel method for problem encoding, population initialization and fitness evaluation with an objective to provide cost-effective and optimized workflow execution within the time limit. The performance of the proposed CEFA is compared with the state-of-the-art algorithms such as IaaS Cloud-Partial Critical Path (IC-PCP), Particle Swarm Optimization (PSO), Robustness-Cost-Time (RCT), Robustness-Time-Cost (RTC), and Regressive Whale Optimization (RWO). Our experimental results demonstrate that the proposed CEFA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint and minimizing the cost of execution. [ABSTRACT FROM AUTHOR]

The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs' capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO's profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO's profits while guaranteeing the QoS. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-edge fusion computing for more personalized services. However, both cloud computing and edge computing suffer from high network consumption, which remains a key problem yet to be solved in cloud-edge fusion computing environments. The cost of network consumption can be divided into two parts: migration costs and communication costs. To solve the high network consumption problem, some virtual machines can be migrated from overloaded physical machines to others with the help of virtualization technology. Current network perception migration strategies focus more on the communication cost by optimizing the communication topology. Considering both communication and migration costs, this paper addresses the high network consumption problem in terms of the communication correlations of virtual machines and the network traffic of the migration process. It proposes three heuristic virtual machine migration algorithms, LM, mCaM and mCaM2, to balance communication costs and migration costs. The performance of these algorithms is compared with those of existing virtual machine migration algorithms through experiments. The experimental results show that our virtual machine migration algorithms clearly optimize the communication cost and migration cost. These three algorithms have a lower network cost than AppAware, an existing algorithm, by 20% on average. This means that these three algorithms improve the network performance and reduce the network consumption in cloud-edge fusion computing environments. They also outperform existing algorithms in terms of operation time by 70% on average. [ABSTRACT FROM AUTHOR]

Multi-objective virtual machine (VM) placement is a powerful tool, which can achieve different goals in data centers. It is an NP-hard problem, and various works have been proposed to solve it. However, almost all of them ignore the selection of weights. The selection of weights is difficult, but it is essential for multi-objective optimization. The inappropriate weights will cause the obtained solution set deviating from the Pareto optimal set. Fortunately, we find that this problem can be easily solved by using the Chebyshev scalarization function in multi-objective reinforcement learning (RL). In this paper, we propose a VM placement algorithm based on multi-objective RL (VMPMORL). VMPMORL is designed based on the Chebyshev scalarization function. We aim to find a Pareto approximate set to minimize energy consumption and resource wastage simultaneously. Compared with other multi-objective RL algorithms in the field of VM placement, VMPMORL not only uses the concept of the Pareto set but also solves the weight selection problem. Finally, VMPMORL is compared with some state-of-the-art algorithms in recent years. The results show that VMPMORL can achieve better performance than the approaches above. [ABSTRACT FROM AUTHOR]

Cloud computing becomes more sophisticated to provide different services at different levels of user access. Even though various services are accessed at a different level, the security of data being accessed is a highly challenging one. However, there is a number of encryption approaches discussed toward the problem of cloud security; they suffer to achieve higher security as required. The previous ABFD (attribute-based flexible delegation) algorithm uses a set of policies in encrypting the data with specific keys mentioned in the policy. However, the leakage of encryption policy would introduce poor security which can be overcome by adopting multitype encryption standards in different time windows. According to this, an efficient time-variant attribute-based multitype encryption algorithm (TAM) is presented in this paper. The TAM algorithm maintains a taxonomy of attributes and related keys to be used for encryption and decryption. The corresponding keys have been used to generate the ciphertext. The content of taxonomy has been dynamically changing in each time window which makes the difference in integrity management and security performance than previous algorithms. The TAM approach introduces a higher security performance up to 89.6%. The method also reduces the time complexity up to 21 s and increases the throughput performance up to 96%. [ABSTRACT FROM AUTHOR]

In cloud computing, the major task is to make efficient use of resources. The growth in cloud environment opens door for vast research. Placement of virtual machine is a main problem in cloud environment. The ideal placement of virtual machine leads to power effectiveness and asset usage in distributed cloud computing. A multi-objective krill herd technique is proposed in this paper for placement of virtual machine. The objective is to effectively acquire an arrangement of non-dominated solutions that the entire limit add up to asset wastage and power utilization. The work is analysed with the examples from the literature. The experimental outcomes are compared with the existing multi-objective genetic procedure and multi-objective ant colony algorithm. Also, the results are compared with the 2 algorithms, First Fit Decreasing algorithm and Simplified Ant Colony algorithm. The experimental results shows that the proposed method is more efficient when compared to other existing methods. [ABSTRACT FROM AUTHOR]

Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts. [ABSTRACT FROM AUTHOR]

Cloud is a multi-tenant paradigm providing resources as a service. With its easily available computing infrastructure, researchers are adopting cloud for experimental purposes. However, using the platform efficiently for parallel and distributed simulations comes with new challenges. One such challenge is that the simulations comprise logical processes executing on distributed nodes, traditionally, organized in a sequential pattern. This placement strategy leads to delays as frequently communicating processes might get placed farther from one another. In this paper, we proposed a framework to facilitate implementation and evaluation of process placement algorithms inside a three-tier cloud data center. Furthermore, we used the framework to test different process placement strategies based on classical clustering techniques, as well as, our proposed efficient locality-aware placement algorithm. Our evaluation results show a performance gain of 14.5 % for the algorithm in comparison with sequential process placement used in practice. [ABSTRACT FROM AUTHOR]

Different sets of research mainly focus on one variable time series now, while researches involving multivariate time series have been insufficient. In this paper, combined linear segments and fitting error for multivariate time series, we present a new method to reduce the time complexity of DTW distance metric algorithm. Based on the shape feature and the tilt angle, we propose a new approach for similarity matching of DTW multivariate time series. Experimental results demonstrate that this method is helpful for ensuring accuracy and for reducing the time complexity of similarity matching. [ABSTRACT FROM AUTHOR]

Big data trends have recently brought unrivalled opportunities to the cloud systems. Numerous virtual machines (VMs) have been widely deployed to enable the on-demand provisioning and pay-as-you-go services for customers. Due to the large complexity of the current cloud systems, promising VM placement algorithm are highly desirable. This paper focuses on the energy efficiency and thermal stability issues of the cloud systems. A Cross Entropy based VM Placement (CEVP) algorithm is proposed to simultaneously minimize the energy cost, total thermal cost and the number of hot spots in the data center. Simulation results indicate that the proposed CEVP algorithm can (1) achieve energy savings of 26.2 % on average, (2) efficiently reduce the temperature cost by up to 6.8 % and (3) significantly decrease the total number of the hot spots by 60.1 % on average in the cloud systems, by comparing to the Ant Colony System-based algorithm. [ABSTRACT FROM AUTHOR]

An automated submerged arc welding (SAW) platform for welding of complex seams of large-size and thick-wall workpieces is introduced in this paper. The orthogonal intersection of a cylinder and a brunch pipe is taken as an example. The mathematical models of the weld trajectory in a dynamic coordinate system and in an absolute coordinate system are built respectively. A collaborative control algorithm and control strategy for the pose of the workpieces and the position of the welding torch is proposed based on coordinate transformation and discrete approximation theory. Closed form expressions of the control laws for the workpieces and the welding torch are worked out under the constraints of SAW process. The control effect of the algorithm is simulated and the approximation error is analyzed in MATLAB. Simulation results show that the approximation error of the proposed discrete control method and the tracing accuracy of the welding torch are satisfactory. The proposed welding platform provides a practical method for automated SAW process in intersection seam welding for large-size and thick-wall workpieces, in which stabilized welding quality, high efficiency, high productivity, and good production environment are promising. In the end, the application of cloud technology in the automated welding platform is also discussed, which can further improve the applicability and flexibility of the platform. [ABSTRACT FROM AUTHOR]

Scientific workflows are used to process large amounts of data and perform complex analyses; thus, they require powerful computing resources to produce the desired results in an acceptable time and at reasonable costs. For this purpose, distributed resources such as cloud computing, with access to virtualized, infinite, and elastic resources are used to execute the workflows. For mapping tasks to computational resources, the problem must be modeled as a scheduling problem. The algorithm presented in this research is a hybrid algorithm based on a mathematical model called MHPSLP that performs the scheduling problem by breaking the problem into smaller subsets including scheduling bags of tasks, providing resources using an mixed integer linear mathematical (MILP) model. The benefit of this method against compared scheduling algorithms is reduction of executed task's cost in a deadline constraint. [ABSTRACT FROM AUTHOR]

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased. [ABSTRACT FROM AUTHOR]

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]