Search

These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed. [ABSTRACT FROM AUTHOR]

Recently, we are witnessing an enormous burst of data due to the ever-increasing number of Internet of Things (IoT) devices. The traditional cloud computing paradigm has failed to scale; to be specific, its latency and bandwidth utilization are remarkably increased and consequently, Quality of Service (QoS) is decreased. On the other hand, the data management scope in fog computing require much more considerations in terms of performance and scalability. This is because of deploying IoT applications over fog nodes considering their resource-limited and heterogeneity. However, to the best of our knowledge, there is not any literature review that systematically categorizes these issues. In this paper, we have presented a classification of data replica placement approaches considering four main categories: framework-based, graph-based, heuristic-based, and meta-heuristic-based algorithms. To sum up, the primary contribution of this study is as follows: studying articles on data replica placement in fog computing, as well as presenting their strengths and weaknesses, providing a comprehensive systematic review of current approaches and categorizing them comprehensively, discussing research challenges, and future works to improve computing and evaluation mechanisms in the fog computing environment. This paper generally provides a classification, briefly explains the reviewed techniques, and then compares these methods in the end. [ABSTRACT FROM AUTHOR]

Offloading computation-intensive tasks of an application from a mobile device to a remote computation resource can improve the runtime of the application as well as improve the lifetime of the mobile device's battery. However, computation offloading is a complex endeavour: remote computation resources in the cloud and in the fog have different computation capabilities and costs, and the network bandwidth and latency between different resources need to be taken into account. In addition, the mobile device would ideally like to offload multiple applications simultaneously, further adding to the complexity of assigning different tasks of each application to available computation resources. In this paper, we introduce an open-source versatile computation offloading simulator (OVCOSim), capable of simulating and optimizing offloading multiple applications to different cloud and fog resources and visualizing the outcome for users. We demonstrate how OVCOSim can be used to optimize assignment of tasks to computation resources to accommodate different user objectives, runtime, battery life, cost, or a combination of these objectives, using a variety of assignment algorithms. We also provide several examples to demonstrate how OVCOSim can be used, as well as validate its functionality. [ABSTRACT FROM AUTHOR]

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]

Data mining algorithms can process target data and extract useful hidden information, which is helpful for decision making. However, current mining algorithms have some shortcomings such as time-consuming processing of big data or inability to process massive data. Since data mining technology cannot be used in the traditional cloud platform environment, it is necessary to improve the algorithm to make it more suitable for the cloud platform environment. By analyzing the actual application process of BP classification algorithm, this paper expounds the practicability of BP classification algorithm, analyzes the data mining process based on Hadoop cloud platform, and expounds the development ideas of BP classification algorithm. The source of data mining algorithm supported by cloud computing is discussed. Finally, Hadoop cloud platform. This paper designed the corresponding system architecture and data interface, and established a suitable test environment for this system, and completed the simulation experiment test through the design. The calculation time of this algorithm is proportional to the amount of data, showing a linear relationship. In data mining, the optimized BP algorithm in this paper can significantly save resources in terms of spatial features. This paper designs an optimized operating system based on Hadoop platform through comprehensive analysis and improvement algorithm. [ABSTRACT FROM AUTHOR]

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach's capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks. [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 overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. Therefore, recent literature focuses on managing cloud infrastructure with RE sources in addition to traditional non-renewable energy (NRE) sources whenever required. However, these works aim to maximize the usage of RE or minimize the cost without considering the uncertainty (UN). This paper presents three user request-based scheduling algorithms, namely UN-based future-aware best fit, UN-based round-robin, and UN-based highest available renewable first, by managing the UN of RE and NRE. These algorithms consider two types of UN, namely UN-RE and UN-NRE, concerning the user request. The proposed algorithms undergo a rigorous simulation process with 200 to 2000 user requests and 20 to 200 datacenters and are compared using overall cost, UN cost and time, and the number of used RE resources. [ABSTRACT FROM AUTHOR]

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced Marine Predator Algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into Marine Predator Algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ datasets.The experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization. [ABSTRACT FROM AUTHOR]

5G network is an inevitable trend in the development of mobile communications. Mobile cloud computing is a more promising technology for 5G networks. This paper proposes a hierarchical distributed cloud service network model, which is composed of three layers: "access cloud + distributed micro cloud + core cloud". On the basis of access to the cloud, a distributed micro cloud system is deployed to migrate the service capabilities of the remote core cloud server to the local area. This paper proposes a task offloading assignment algorithm in a small cell cloud scenario. This algorithm establishes a SCC (Small Cell Cloud) based on the channel quality between small cells and the remaining available computing resources, and allocates the load to each small cell in the SCC according to the channel quality and the remaining available computing resources. Simulation results show that this solution can improve the utilization of wireless and computing resources in the small cell cloud computing scenario, and improve the user QoE (Quality of Experience). In order to make the system operate normally under heavy load, this paper proposes a feedback adaptive random access strategy based on the adaptive random access model. This can ensure that the throughput rate does not decrease under heavy load conditions, and at the same time, the average access delay of the existing system is reduced. When the arrival rate of user requests gradually increases, the throughput rate of RA-RACH access will continue to decrease due to collisions until it approaches below 0.1. In the state where the number of users is low and the load is lighter, both RA-RACH, AC-RACH, and FC-RACH have a higher access success rate. But as the load continues to increase, RA-RACH will quickly drop to 0. [ABSTRACT FROM AUTHOR]

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms—grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio. [ABSTRACT FROM AUTHOR]

The evolution of cloud computing results in the development of numerous huge data-centers. These data-centers use large quantities of electrical energy, which in turn results in high operating cost and high emissions of CO 2 . The grounds for this excessive usage of energy is the inefficient usage of data-center resources (hosts). The resources in the data-center can be in either peak or idle or sleep state. It has been experimentally proven that hosts in the idle state consumes more energy than hosts in sleep state. Through an efficient and optimal VM placement strategy, an energy efficient resource utilization can be achieved, thereby reducing the number of idle hosts in the data-center. This paper introduces two energy efficient VM placement algorithms based on bin packing heuristics considering the physical machine's energy efficiency, Energy Efficient VM Placement (EEVMP) and Modified Energy Efficient VM Placement (MEEVMP), that can reduce the overall energy usage in the data-center. The EEVMP algorithm when compared with the default VM placement algorithm Power-Aware Best-Fit Decreasing algorithm (PABFD) of CloudSim, it reduces the energy consumption by 53%, average SLA violation by 3.5% and number of VM migrations by 64.47%. Further, we have performed MEEVMP algorithm where we achieve a reduction in energy consumption by 54.24%, average SLA violation by 4.39% and number of VM migrations by 67.713 % as compared to PABFD. [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]

The application of edge clouds is becoming more and more widespread. The resource optimization is one of the important research contents of edge cloud. Generally, the edge cloud has limited computing resources and energy. Resource optimization can make tasks perform efficiently and reduce costs. Therefore, achieving high energy efficiency while ensuring a satisfying user experience is critical. This paper proposes the resource optimization and load balancing model. By considering factors such as user preferences, SLA and cost, the algorithm of resource optimization determines the resources scheme of edge cloud. The data movement after resource optimization is achieved through migration strategies. The load balancing of the edge cloud environment can be ensured. The results of the experiment prove that our proposed algorithm can better control costs. [ABSTRACT FROM AUTHOR]

The mobile devices have entered the race of deploying high tech to the users but inherit a defect of limited battery storage. With the limitationof battery capacity, energy efficiency has been a major concern for Mobile Cloud Computing (MCC). The multimedia application comprises rich media that needs higher processing and computation, and the resource and computation are constrained. Energy-efficient transmission is a big concern because it equips the mobile device with high-end hardware components, but still, are far behind to the battery capacity and computation competence. This paper intends to introduce and investigate the different approaches/algorithms and tools that best fit to save resource utilization and energy consumption rationally. The algorithms like the Genetic Algorithm, Greedy scheduling algorithm, power-aware list-based scheduling, Offloading based task scheduling, media cloud distributed scheduling algorithm, etc. are discussed in this research. The data, Energy-efficient technique, and Cloud server response that defines each of the main components is introduced in this paper that concerns the implementation of the energy-efficient transmission in MCC. We propose these components that illustrate the workflow in the technology. A process when the data is perceived by the technique to adequately deliver a response to the mobile device. An energy-efficient transmission technique for MCC is a development towards the use of mobile cloud computing in this field working to counter the utilization of energy and resources that execute locally. This concept results in reducing the amount of data transmitted with the use of sharing the data externally between the tasks that will save energy. Through this paper, readers will understand the benefit of using the energy-efficient technique in the MCC. Also, the reader will understand the classification groups, validation criteria, future gaps of the 30 literature reviews about the technique, and how they intend to energy optimization in mobile devices. [ABSTRACT FROM AUTHOR]

The emerging technologies such as high-performance computing, cloud computing and large data centre networks demand very high injection rate, high scalability and very low switching latency (SL) that can manage high network traffic, and buffering and also be cost-effective, so that they can be a viable solution for future manufacturing industry. To overcome the biggest challenge of optical buffering and big quantity of data switching, the Data Vortex (DV) all optical switch innovations and its various enhancements such as Augmented Data Vortex (ADV) seem to be the possible solution and can bring the revolution in the industrial world. In this paper, a new generalized algorithm has been used to simulate the equivalent planar virtual model of 3-D ADV and is named as equivalent planar algorithm (EPA). The algorithm is more versatile and provides more flexible design for multiple level networks. To verify the proposed EPA of ADV (or EPA ADV), the simulations have been performed for various input traffic loads and switch size keeping active angle 'A' constant. Also the ADV switch is not studied under asymmetric mode (AM) of operation till now which has been done here. Lastly the paper also provides the performance comparison study of original DV and EPA ADV under symmetric and asymmetric I/O mode. The results obtained from simulation have shown that under modest degree of asymmetric I/O operation of the switch EPA ADV shows performance improvement in injection rate (throughput) and average switch latency (SL) under different traffic pattern as compared to symmetric mode (SM) of operation in ADV. [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]

Software as a service (SaaS) provider hires resources from an Infrastructure as a Service (IaaS) provider and provides these sharable resources to user's applications on lease. However, it is becoming a more challenging issue for SaaS providers to meet user's Quality of Service (QoS) Parameter and maximize profit from cloud infrastructure. This proposed work satisfies both the user and the service provider by fulfilling service level agreement (SLA), user's QoS requirement, and increasing profit with efficient resources utilization. This paper proposes an Improved Quantum Salp Swarm Algorithm (IQSSA), which improves the Salp Swarm algorithm by incorporating the principles of Quantum computing to increase the convergence rate. Further, Quantum-inspired Salp Swarm Grey Wolf Algorithm (QSSGWA) embeds SSA with Grey Wolf Optimizer (GWO) to improve the global optimum solution, and quantum operator is used to initializing population. Proposed algorithms execute tasks under the user-defined deadline and budget constraints. Furthermore, the penalty cost is formulated and applied in the case of a deadline violation. IQSSA and QSSGWA are tested on 19 global benchmark functions, and results prove their superior performance compared to SSA, GWO, BAT, and Particle Swarm Optimization (PSO) algorithm. Furthermore, these algorithms are simulated on CloudSim, and performance matrices such as service provider's profit, makespan, SLA violation rate, task rejection rate, throughput, resource utilization, and response time are compared. The comparison analysis demonstrates that the proposed algorithms offer better performance and more efficient scheduling than existing metaheuristics. Furthermore, simulation results clearly show that QSSGWA gives the best results for all performance matrices. This proposed approach can be applied in many scientific domains, where distributed processing of data or large scale data analysis is required such as distributed and federated machine learning, serverless computing, medical applications, etc. [ABSTRACT FROM AUTHOR]

As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper. [ABSTRACT FROM AUTHOR]

With the progress of speech retrieval technology in the cloud, it brings a lot of conveniences for speech user. Yet, the inquiry encrypted speech results from the speech retrieval system are faced with some secure issues to settle, such as integrity authentication and tampering recovery. In this paper, an encrypted speech authentication and tampering recovery method based on perceptual hashing is proposed. Firstly, the original speech is scrambled by Duffing mapping to construct an encrypted speech library in the cloud, through extracting product of uniform sub-band spectrum variance and spectral entropy of encrypted speech and constructing a perceptual hashing sequence to generate the hashing template of the cloud. From this, a one-to-one correspondence between the encrypted speech and perceptual hashing sequence is established. Secondly, the authentication digest of encrypted speech is extracted according to the inquiry result during the retrieval. Then, the authentication digest and the perceptual hashing sequence of the hashing template in the cloud are matched by the Hamming distance algorithm. Finally, for encrypted speech that fails authentication, tampering detection and location are performed, and the tampered samples are recovered by the least square curve fitting method. The simulation results show that the proposed method can extract the authentication digest directly in the encrypted speech, and the authentication digest not only has good discrimination and robustness, but it accurately locates the tampered area for malicious substitution and mute attacks. In addition, the proposed method can recover tampered speech signals in high quality without any extra information. [ABSTRACT FROM AUTHOR]

Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and led us towards an era of cloud-based activities. Virtual machine is one of the main elements of virtualization in cloud computing that represents physical server into the virtual machine. The utilizations of these VM's are important to achieved effective task scheduling mechanism in cloud environment. This paper focuses on improvment of the task distribution system in VM for cloud computing using load balancing technique. For that reason modification took place at Bat algorithm fitness function value this section used in load balancer section. When algorithm iteration are complete then time to distribute the task among different VM therefore in this section of algorithm was modified. The second modification took place at the search process of Bat at dimension section. The proposed algorithm is known as modified Bat algorithm. Four parameter are used to check the performance of the system which are throughput, makespan, degree of imbalance and processing time. The proposed algorithm provides efficient result as compaire to other standard technique. Hence the proposed algorithm improved cloud data center accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Workflow applications are a popular tool used by scientists to model and run applications on cloud computing. With the rise of multi-core parallel systems and cloud computing high-performance computing is becoming more accessible to scientists, but it also presents new challenges. One pressing issue is the energy consumption of these systems, both for environmental and financial reasons. To minimize energy usage in cloud computing, methods such as energy-efficient scheduling are gaining attention. However, current scheduling solutions have limitations, or failing to address the problem as a multi-objective optimization balancing performance and energy. This problem is considered complex and NP-complete. Many researchers have attempted to resolve it using heuristic and meta-heuristic methods. Although these methods may not always provide optimal results, they are still a subject of active research. This paper proposes a new multi-objective optimization algorithm that combines the Heterogeneous Earliest Finish Time and BAT algorithm to optimize multiple conflicting objectives for energy, cost, makespan, and resource utilization. The results are verified using the Analysis of Variance statistical tool, and the proposed algorithm is shown to be superior to existing contemporary algorithms. [ABSTRACT FROM AUTHOR]

The Cloud environment had been the go-to for many users recently. Once request from users get submitted, cloud resources are put into action to fulfill the request. Scheduling is the primary task in cloud that needs to be up-to the mark for completing the requests swiftly. Multiple dynamic requests are submitted simultaneously by cloud users that necessitates precise and prompt scheduling in cloud. Scheduling in cloud may be hampered by various constraints, take for example the various QoS parameters that needs to be upheld. Though many researchers had proposed solutions for scheduling in cloud, improvisations can still be made by combining several QoS parameters that help attain optimized scheduling in cloud to boost the overall cloud performance. In this paper, we had proposed a Binary Grey Wolf Optimization (BGWO) algorithm to optimize the scheduling activity in cloud computing environment. The BGWO is a multi-heuristic algorithm where tasks are scheduled based on a fitness function, explicitly designed for achieving optimization goal. The fitness function that had been designed comprises of three prime parameters namely, the degree of imbalance (DoI), energy consumption and makespan. The performance efficiency of the proposed BGWO had been ascertained by comparing it with Oppositional based Grey Wolf Optimization algorithm (OGWO) and Mean Grey Wolf Optimization algorithm (Mean GWO) with respect to imbalance, energy and makespan parameters. The proposed algorithm had produced a cumulative improvement of 10.13% and 17.4% for makespan, 30.18% and 41.96% for DoI, 8.94% and 14.95% for energy consumption parameters. Detailed comparative results obtained had been described in the Results part of this research article. [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]

To solve the problem of lack of trust in cloud platforms, we propose an image encryption algorithm based on the Lattice Hash function and privacy protection in this paper. A new chaotic system using Tent Map and Sine Map is designed in our proposed scheme, which does not have a period window. Meanwhile, our algorithm uses a new chaos system to provide random matrices F for Lattice Hash functions. The image feature vector and the initial key are also employed to obtain the security key, which can be used as input to the Lattice Hash. Besides, our algorithm utilizes the Paillier cryptosystem to encrypt the feature vector as the ciphertext image index for higher security. In addition, the 2D-Line map is improved and applied as a function for scrambling pixel positions to avoid the problem of storing pixel subscripts. Simulation experiments and security analysis shows that our proposed algorithm can resist differential attacks, statistical attacks, brutal attacks. Therefore, our scheme has good security performance and allows precise search over the ciphertext. [ABSTRACT FROM AUTHOR]

In this paper, we study the possibility of using a certain cloud computing model supplied with cryptoservers to obfuscate software programs. Earlier, we proposed this cloud computing model in our study of some information security problems for multi-client distributed computing over encrypted data. Based on this model, we proposed a new approach involving the use of threshold homomorphic cryptosystems for program obfuscation. The main result of this paper is a new definition of the resistance of obfuscation of programs in the cloud computing model and a theorem proving the cryptographic strength of the proposed algorithm of obfuscation of programs under the assumption of the existence of cryptographically strong threshold homomorphic encryption systems. The paper is organized as follows. In the introducing section we discuss the main aspects of the information security problems for cloud computing systems. Section 2 provides a description of the obfuscation program objectives, as well as a brief overview of the main achievements in its study. The next section provides general information about homomorphic cryptosystems. Section 4 describes a more special class of homomorphic cryptosystems - threshold homomorphic encryption systems. Section 5 introduces the cloud computing model, which is used as a framework for our program obfuscation techniques. For this computing environment, in Section 6, the definition of the cryptographic strength of program obfuscation is formulated, a new method of program obfuscation using threshold homomorphic cryptosystems is described, and the cryptographic strength of the proposed obfuscation algorithm is proved. [ABSTRACT FROM AUTHOR]

The historical collaborative filtering based recommendation system has become the most essential one in cloud computing environment. The recommender system which has been depends on collaborative filtering easily identify the user references and allow to learn relationship items—past users from the user group who exhibit similar preferences. Hence, the recommendation system has been considered as the most powerful tool for cloud providers and users. This paper proposed the clustering recommendation system executed in cloud environment. The accuracy of the system reduced when irrelevant features presents in data. So that in this proposed scheme, an effective feature selection approach named as modified LDA has been utilized for acquiring the relevant information only. The LDA technique defined as Linear Discriminant Analysis which decreased features numbers to expected value before classification process. DBSCAN is utilized as a clustering approach which provides better quality in terms of segregating the number of movies. Based on the genre, the similar movies are clustered together with the user ratings. DBSCAN elaborated as Density Based Spatial Clustering of Applications with Noise termed as famous method of learning or clustering which detached the high density clusters from low density clusters. The LDA technique has been utilized to accomplish the appropriate user reviews with categorized ratings. In this paper, the main challenge described as to analyze the reviews of the user with ratings using best clustering approach. The evaluation results proved the highest accuracy of 92.56% when compared with various methods of accuracy. This proposed method also has minimum execution time. [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]

Cloud computing is a domain that provides the on-demand availability of IT resources, which can be accessed from a remote server over the internet. A widely used cloud service model is Infrastructure as a Service (IAAS). Clients offload their documents, images, and multimedia files to the cloud storage service. Securing the information of the clients is the challenging job of the cloud service providers (CSP). To ensure the Quality of Service (QoS) of the information CSP follows efficient encryption algorithms. To enhance the security of the image files this paper proposes a new Simplified Image Encryption Algorithm (SIEA) using an efficient key generation module, which uses the basic structure of Feistel cipher. The proposed secure algorithm encrypts the images and stores them in the cloud storage to overcome the data attack. The plain image is converted into corresponding pixel values and the converted plaintext is divided into blocks of 128 bits each. The 128-bit cipher key is used to find the sub-keys for 10 rounds. Five sub keys (K0 to K4) are generated with 32-bit each. It also contains 5 keys used in the 10 rounds of the process and after 10 rounds data will be shuffled again in the post-round step to generate the 128-bit cipher text. To ensure Shannon's confusion and diffusion logic the substitution and permutation concepts are used in the proposed algorithm. To show the performance of the algorithm the images of three categories black and white, gray-scale, and color images are taken as input and the algorithm produces encrypted images. The cloud storage devices store the encrypted images. This proposed SIEA algorithm is compared with standard AES algorithms and other recent and competitive algorithms in terms of various statistical analysis methods like key sensitivity, information entropy, correlation, histogram, and differential attack analysis shows that the proposed algorithm outperforms other algorithms in terms of security and robustness with its lightweight process. [ABSTRACT FROM AUTHOR]

Virtual Machine Placement (VMP) plays a significant role in improving efficiency of Cloud Data Center (CDC). With the dramatic increase in the use of cloud computing, it seems necessary to apply effective algorithms to reduce the power consumption of CDC. VMP is known as a NP-Hard problem that cannot be solved by deterministic algorithms in polynomial time. In this paper, an algorithm named Combinated Random Best First Fit (CRBFF) is proposed with the aim of increasing the Quality of Service (QoS), in which Virtual Machines (VMs) are optimally placed on heterogeneous Physical Machines (PMs). The effectiveness of CRBFF is evaluated by different metrics on Google Compute Engine (GCE), Amazon Web Service Elastic Compute Cloud (AWS EC2) and Microsoft Azure scenarios and the results show that CRBFF performs better than other common algorithms. [ABSTRACT FROM AUTHOR]

Cloud Computing is playing a huge role in future technology. Further, with the explosive growth of the Internet and cloud computing, several service providers, such as Amazon, Microsoft, IBM, and Google, have expanded their data centers and rapidly deployed data centers in different places around the world to deliver various cloud computing services. However, several challenges are raised with the wide spread use of cloud environment such as power consumption, load balance, reliability, scalability, and security. This paper tackles the power consumption problem and presents an efficient algorithm, called Task Consolidation based Power Minimization (TCPM), to efficiently schedule tasks onto available resources of the cloud environment so as to minimize power consumption. In proposed TCPM algorithm, several benefits of the existing algorithms are enhanced and incorporated into the TCPM algorithm, where the best-fit procedure is used to achieve the best possible resource utilization and avoid wasting energy. The results of the proposed TCPM algorithm are compared with other recent algorithms such as FCFS, WWO, and MCT algorithms using the CloudSim toolkit. [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]

Electronics-Health Care is one of the new ideas in the healthcare industry. For analyzing the patient's records, doctors need a centralized record of the patients, which gives useful and enormous information. There will be a threat of leaking of confidential e-health data, which will seriously endanger by modifying the health care data of medical and its individual entities. Protection of the confidential data of patients will be a primary constraint in the health care industry. There is a lot of progressiveness in the human services area as well as there is a lot of usage of the information gathered from various sensors and this information is stored in the cloud. As per the existing security algorithms, there is still some security breach and also it won't get satisfied with integrity and consistency while transferring the data from one vendor to others in the health care system. It also causes high spoofing of IP and side channel hackers. With the increase in the use of the internet, there is an increase in hacking also, especially in the health-care sector, e-commerce, military, and Education. Dealing with the huge volume of information in the electronics-healthcare area requires the processing of patient's information day by day and its memory allocation. Cloud is the best option to store these health records in a distributed manner rather than on a single server. So, whatever the information gathered from it will be encoded on block chain technology and an anonymous digital ring signature will be used to authorize the intended user and maintain the privacy of patient cloud-based data distributed Electronics health record system before sending the information to the cloud storage. In this paper, the protection of Personal data of the patient health data is enhanced with block chain and anonymous ring signatures in the e-healthcare cloud; thus making it suitable for real-time applications. [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]

In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth. [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]

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions. [ABSTRACT FROM AUTHOR]

Recently, optimizing the monetary cost and performance metrics of executing workflow applications in cloud environments has become an important and interesting research subject. The most critical challenges with the existing methods are simplifying assumptions, which make them far from reality, offering a set of solutions that cannot cover the whole search space and are not real non-dominated solutions, and finally, their high time-complexity. To tackle the mentioned problems, in this paper, a multi-objective workflow scheduling algorithm named Time-Cost Compromised CPoP (TC3PoP) customized for cloud environments is proposed. We have modeled and formulated the workflow scheduling problem based on the cloud environment offers and characteristics. The proposed algorithm is light-weight and based on the offered resources by cloud providers, and produces a set of real Pareto optimal solutions so that a user can freely choose the best solution based on his/her budget and deadline. The results of the experiments show that in terms of HyperVolume indicator, the validity and diversity of TC3PoP solutions are 41.5% and 38.1% better than those of well-known and successful NSGA-II for two real scientific workflows, Montage and LIGO, respectively, and the proposed method is much faster than the compared method. Moreover, to evaluate the quality of Pareto fronts produced by the proposed algorithm, some statistical analysis was made to investigate the precision and distribution of the solutions. [ABSTRACT FROM AUTHOR]