Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Hamed, Ahmed Y.; Alkinani, Monagi H.

doi:10.32604/cmc.2021.018658

Cited by 11 publications

(12 citation statements)

References 10 publications

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“…Correspondingly, edge computing has the advantage of a low transmission delay and a high service responsiveness due to its deployment at the edge despite certain resource constraints in terms of computation and storage [20]. Thus, cloud-edge collaboration technology can better overcome the shortcomings of both cloud computing and edge computing, and has attracted a lot of research attention from academia and industry in recent years, in areas such as computational offloading [21][22][23], task and resource scheduling [24][25][26], and resource allocation [27][28][29][30], so as to achieve a lower transmission latency and better user experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

Liu

et al. 2023

Systems

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In the digital economy era, cloud–edge collaboration technology provides the necessary technical support for the digital transformation of enterprises, which can improve the quality of services (QoS), and it attracts extensive attention from scholars and entrepreneurs from all fields. Under the bounded-rationality hypothesis, this paper investigates the service mechanism for the cloud–edge collaboration system considering the quality of experience (QoE) and presents a dynamic evolutionary game model between cloud service providers and edge operators by applying the evolutionary game theory. Then, this paper analyzes the equilibrium and stability conditions for the decision-making of both parties involved to guarantee the QoE reaches the ideal state. In addition, we investigate the factors that influence the stable cooperation between the two evolutionary stable strategies and validate the theoretical analytical results with numerical simulations. The research results show that the final evolution of the cloud–edge collaboration system depends on the benefits and costs of the game matrix between the two parties and the initial state values of the system. Under a specific condition, the cloud–edge collaboration system can eventually be driven to be an ideal state by reducing the collaboration cost and improving the collaboration benefit. The more both parties focus on the QoE, the more conducive it will be for the formation of a cloud–edge collaboration, thus effectively promoting long-term stability and better serving enterprises’ digital transformation.

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Section: Literature Reviewmentioning

confidence: 99%

Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

Liu

et al. 2023

Systems

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“…2. The results obtained by the proposed ECSA are compared with those obtained by the Genetic Algorithm (GA) [8], and New Genetic Algorithm (NGA) [14]. The task priority of ECSA {Tas 0 , Tas 2 , Tas 3 , Tas 4 , Tas 1 , Tas 6 , Tas 8 , Tas 7 , Tas 5 , Tas 9 , Tas 10 }, task priority of NGA {Tas 0 , Tas 4 , Tas 3 , Tas 2 , Tas 8 , Tas 1 , Tas 6 , Tas 5 , Tas 7 , Tas 9 , Tas 10 }, GA {Tas 0 , Tas 2 , Tas 6 , Tas 4 , Tas 1 , Tas 3 , Tas 8 , Tas 7 , Tas 5 , Tas 9 , Tas 10 }.…”

Section: Casementioning

confidence: 99%

“…A Genetic Algorithm (GA) has been described for task assignment and execution. The algorithm aims to decrease the execution cost and makespan of tasks and increase resource utilization [8].…”

Section: Introductionmentioning

confidence: 99%

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Hamed¹,

Elnahary²,

Alsubaei³

et al. 2023

Computers, Materials &Amp; Continua

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Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system. As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan. This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We assess our algorithm's performance by running it through three scenarios with varying numbers of tasks. The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.

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“…In cloud computing, task scheduling is represented as a graph with NTAS tasks (TAS 1 , TAS 2 , TAS 3 , ..., TAS NTAS ). Each node (task) with GRT and E-directed edges represents a subset of the tasks' requests (Hamed & Alkinani, 2021). Each node (task) represents an instruction that may be executed sequentially on the same virtual machine as other instructions; it may have one or more inputs.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Where i = 1.2. ...., NTAS, and j = 1,2, …NVTM Algorithm 1: To find the schedule length (Hamed & Alkinani, 2021) Input the schedule of tasks as shown in Table 1 Rey_Time[VTM j ] = 0 where j = 1, 2,…NVTM.…”

Section: Problem Descriptionmentioning

confidence: 99%

Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

Hamed¹,

Elnahary²,

El-Sayed³

2023

ASIR

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Cloud computing provides resources to its consumers as a service. The cloud computing paradigm offers dynamic services by providing virtualized resources via the internet for enabling applications, and these services are provided by large-scale data centers known as clouds. Cloud computing is entirely reliant on the internet to provide its services to consumers. Cloud computing offers several advantages, including the fact that users only pay for what they use weekly, monthly, or yearly, that anybody with an internet connection may use the cloud, and that there is no need to purchase resources, hardware, or software on their own. This paper proposes an efficient task scheduling algorithm based on the Jaya algorithm for the cloud computing environment. We evaluate the performance of our method by applying it to three instances. The recommended technique produced the optimal solution in makespan, speedup, efficiency, and throughput, according to the findings.

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Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Cited by 11 publications

References 10 publications

Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

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