Many-Objective Cloud Task Scheduling

Geng, Shaojin; Wu, Di; Wang, Penghong; Cai, Xingjuan

doi:10.1109/access.2020.2990500

Cited by 31 publications

(15 citation statements)

References 47 publications

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“…Geng S et al developed a multi-objective cloud model with four objectives: minimizing time, minimizing time cost, maximizing resource utilization, and load balancing. At the same time, multi-objective optimization is carried out to propose an algorithm based on mixed angles to solve the model [14].…”

Section: Related Workmentioning

confidence: 99%

Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

Liu

et al. 2021

Business Intelligence and Information Technology

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Study of cloud computing task scheduling problem, in view of many existing in the process of logistics transportation route choice question, to consider in a certain amount of time constraints, road clearance restriction, road comfort, and driving cost constraints under the condition of how to carry out the task allocation, to achieve the shortest path as the optimization goal, application of particle swarm optimization (PSO) algorithm to extend it a task scheduling method based on hybrid particle swarm optimization (PSO) cloud is proposed. Experimental results show that hybrid particle swarm optimization plays a good role in cloud computing task scheduling, which can shorten the distance to a certain extent, improve the efficiency, improve the applicability and scalability of the algorithm, not trapped in local optimization, and achieve the optimal global effect.

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Section: Related Workmentioning

confidence: 99%

Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

Liu

et al. 2021

Business Intelligence and Information Technology

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“…Zhang et al [10], [11] survey provided a thorough examination of PSO. PSO advancements by initializing with chaotic and quantum behavior, analyzed PSO with different population topologies, hybridization and extensions by discussing multiple objectives [12] and theoretical analysis of PSO were considered in various computing environments for targeting researchers from all engineering fields.…”

Section: Alsaidy Et Al [2] Proposed Heuristic Initialized Pso [3] [4]mentioning

confidence: 99%

“…One of the criteria used to classify the meta-heuristics algorithms for optimization problems by considering the no. of objectives, they are single objective, multi-objective [12] [13] and many objectives [11].…”

Section: B Proposed Mohipso Modelmentioning

confidence: 99%

Multi-objective based Cloud Task Scheduling Model with Improved Particle Swarm Optimization

Udatha¹,

Lakshmeeswari²

2021

IJACSA

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Now-a-days, advanced technologies have emerged from the parallel, cluster, client-server, distributed, and grid computing paradigms. Cloud is one of the advanced technology paradigms that deliver services to users on demand by cost per usage over the internet. Nowadays, a number of cloud services have rapidly increased to facilitate the user requirements. The cloud is able to provide anything as a service over web networks from hardware to applications on demand. Due to the complex infrastructure of the cloud, it needs to manage resources efficiently, and constant monitoring is required from time to time. Task scheduling plays an integral role in improving cloud performance by reducing the number of resources used and efficiently allocating tasks to the requested resources. The paper's main idea attempts to assign and schedule the resources efficiently in the cloud environment by using proposed Multi-Objective based Hybrid Initialization of Particle Swarm Optimization (MOHIPSO) strategy by considering both sides of the cloud vendor and user. The proposed algorithm is a novel hybrid approach for initializing particles in PSO instead of random values. This strategy can obtain the minimum total task execution time for the benefit of the cloud user and maximum resource usage for the benefit of the cloud provider. The proposed strategy shows improvement over standard PSO and the other heuristic initialization of PSO approach to reduce the makespan, execution time, waiting time, and virtual machine imbalance parameters are considered for comparison results.

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“…Its goal is to minimize the computational cost and meet the delay requirements of tasks. Moreover, a novel many-objective optimization algorithm based on hybrid angles (MaOEA-HA) was proposed in [29] to enhance the performance of task scheduling in cloud computing. Rahmani Hosseinabadi et al [30] studied the selection of crossover and mutation operators in the genetic algorithm for addressing the open-shop scheduling problem (OSSP).…”

Section: Related Workmentioning

confidence: 99%

Computation Offloading and Task Scheduling for DNN-Based Applications in Cloud-Edge Computing

Chen

et al. 2020

IEEE Access

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Due to the high demands of deep neural network (DNN) based applications on computational capability, it is hard for them to be directly run on mobile devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks of neural network layers to edges or remote clouds that are equipped with sufficient resources. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this important problem, we first regard the average response time of multi-task parallel scheduling as our optimization goal. Next, the problem of computation offloading and task scheduling for DNN-based applications in cloud-edge computing is formulated with a scheme evaluation algorithm. Finally, the greedy and genetic algorithms based methods are proposed to solve the problem. The extensive experiments are conducted to demonstrate the effectiveness of the proposed methods for scheduling tasks of DNN-based applications in different cloud-edge environments. The results show that the proposed methods can obtain the near-optimal scheduling performance, and generate less average response time than traditional scheduling schemes. Moreover, the genetic algorithm leads to less average response time than the greedy algorithm, but the genetic algorithm needs more running time.INDEX TERMS Cloud-edge computing, DNN-based applications, computation offloading, task scheduling, greedy algorithm, genetic algorithm.

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Many-Objective Cloud Task Scheduling

Cited by 31 publications

References 47 publications

Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

Multi-objective based Cloud Task Scheduling Model with Improved Particle Swarm Optimization

Computation Offloading and Task Scheduling for DNN-Based Applications in Cloud-Edge Computing

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