Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

Zhang, Rong; Tian, Feng; Ren, Xiaochun; Chen, Yaxing; Chao, Kuo‐Ming; Zhao, Ruomeng; Dong, Bo; Wang, Wei

doi:10.1007/s11761-018-0231-7

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…An enhanced PSO (SADCPSO) combined three techniques of adaptive inertia weight, and the operators for chaos and disruption theories [126]. The three techniques facilitated adjustments in convergence, retaining population diversity, and global optimum solutions to minimize execution time.…”

Section: B Setting/configuration Of Weightsmentioning

confidence: 99%

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

Khan,

Aziz,

Osman

et al. 2023

IEEE Access

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Efficient task scheduling on the cloud is critical for optimal utilization of resources in data centers. It became even more challenging with the emergence of 5G and IoT applications that generate massive number of tasks with stringent latency requirements. This gives birth to fog/edge computing -a complementary layer to cloud. Tasks latency in fog computing can be reduced as processing in the network is done closer to the end devices and users, but every task cannot be scheduled in the fog due to limited resources availability. Conventional scheduling algorithms often fail to exploit the heterogeneous resources; therefore, specially designed and well-tuned scheduling algorithms are desired for achieving better quality of service. In this study, the state-of-the-art task scheduling algorithms in the cloud and fog environments are investigated in a diverse set of dimensions. Among the relevant studies published between 2018-2022 and indexed in the Web-of-Science (WOS), SCOPUS, and Google Scholar databases, eighteen studies are selected for both the cloud and fog domains from WOS and Scopus, while seventeen studies are chosen for both the cloud and fog domains from Google Scholar. Thus, a total of 106 studies are included in this survey for the detail investigation. The scheduling algorithms are broadly classified into three categories such as heuristic, meta-heuristic, and hybrid meta-heuristic followed by detailed critical analysis. It has been observed that most of the scheduling algorithms are dynamic and non-preemptive in nature, while the higher fraction of the tasks is independent in comparison to bag of tasks and workflows. Similarly, 97% of the studies focus on multiple objectives and 68% of the techniques are non-deterministic. Further, a total of twenty different scheduling objectives are identified with makespan, resource utilization, delay, load balancing, and energy consumption as the most significant metrics. The evaluation methods including simulations (51%), real experiments (4%), analytical equations (2%), and datasets (43%) etc. are surveyed. At the end, the open issues, challenges, and future directions are argued. Cloud computing, fog computing, task scheduling, workflow, Internet of Things (IoT). INDEX TERMS I. INTRODUCTION Cloud computing offers different kinds of scalable resources to end users based on pay-per-use basis by providing costThe associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li . savings, security, flexibility, and automatic software updates etc. The users can concentrate on their personal and business objectives without bothering about the infrastructure and supporting services as the vendor is responsible for managing it. There are three service models and four deployment models provided by the cloud providers [1]. Though it is

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Section: B Setting/configuration Of Weightsmentioning

confidence: 99%

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

Khan,

Aziz,

Osman

et al. 2023

IEEE Access

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“…Zhang et al [24] had proposed the SADCPSO algorithm for improving the performance of PSO algorithm by integrating three factors namely self-adaptive inertia weight, disruption operator and chaos operator. The SADCPSO algorithm was capable of solving multi-task scheduling issues.…”

Section: Related Workmentioning

confidence: 99%

GA-WEO: A Hybrid Meta-Heuristic Algorithm for Heterogeneous Task Scheduling on Cloud Environment

Ali

Gobalakrishnan²,

Pradeep

2022

Preprint

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The growth of cloud computing is astounding in the business and research domains in the last decade. The popularity of cloud could be attributed to the virtualization technology. To achieve balance and due to resource scarcity during peak load period, a few of those tasks are migrated to other datacenters. The potential of cloud computing can be harvested to its fullest by employing efficient task scheduling techniques. In cloud, focus is on makespan and maximum system utilization phenomenon owing to which tasks may be scheduled on different virtual machines. Meta-heuristic scheduling algorithms could be employed to provide solution to such issues since task scheduling is a NP-hard problem. In this proposed research work, a integration of Genetic Water Evaporation Optimization Algorithm (GWEO) had been presented for efficiently scheduling of tasks on a heterogeneous cloud environment. The proposed research work objective is to minimize the time of task execution, cost and energy consumed by the resources during task execution. The proposed hybrid GWEO algorithm is hybridization of Genetic Algorithm (GA) and Water Evaporation Optimization Algorithm (WEO). Different datasets had been employed for evaluating the proposed GWEO algorithm. The proposed hybrid technique performance was evolved for the metrics cost, energy and execution time using CloudSim toolkit. The experimental results obtained through simulation had been compared with other algorithms, which showed that the proposed GWEO technique achieve a significant improvement in the QoS metrics (cost, energy consumption and execution time).

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Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment

Tabrizchi

Rafsanjani

Bălaş

2020

Advances in Intelligent Systems and Computing

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Associate multi-task scheduling algorithm based on self-adaptive inertia weight particle swarm optimization with disruption operator and chaos operator in cloud environment

Cited by 9 publications

References 17 publications

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

GA-WEO: A Hybrid Meta-Heuristic Algorithm for Heterogeneous Task Scheduling on Cloud Environment

Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment

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