A comparative study into swarm intelligence algorithms for dynamic tasks scheduling in cloud computing

Elhady, Gamal F.; Tawfeek, Medhat A.

doi:10.1109/intelcis.2015.7397246

Cited by 29 publications

(16 citation statements)

References 22 publications

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“…Hence, it is quite complex to judge on the relevance of each of these three heuristics. Nevertheless, paper [40] investigates these three approaches to compare them using well-known existed algorithms for dynamic task scheduling problem in cloud computing. The experimental results prove that ABC algorithm is the superior; then, PSO and ACO can be putted in second level and third level, respectively, in terms of makespan, degree of imbalance, and standard deviation.…”

Section: Natural Inspired Algorithms (Nia)mentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Section: Natural Inspired Algorithms (Nia)mentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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“…In [10], a concept of load priority is introduced in the genetic algorithm. Firstly, a reasonable load evaluation is performed on resource nodes, then CPU utilization and memory utilization are selected as priority reference coefficients, and finally through genetic algorithm selection, crossover and mutation operations, optimal allocation scheme is obtained, which can effectively improve the throughput of the cloud computing system, and significantly shorten the completion time of task scheduling [2].…”

Section: Related Workmentioning

confidence: 99%

“…Cloud computing provides a new service mode. Users can access system resources and various services through the network anytime and anywhere according to their needs, and use the most convenient way to configure resources [2]. Cloud computing provides a resource sharing pool that integrates hardware, network, storage and other resources.…”

Section: Introductionmentioning

confidence: 99%

MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

et al. 2019

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Cloud computing is popular in nowadays for its convenient and cheap. Grid provides services of available everywhere, and shares everyone. Therefore, smart grid cloud is a good way to manage data for sharing with all power supply stations. Grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. The advantages and disadvantages of scheduling algorithms will directly affect the scheduling performance of both cloud computing and the stability of the entire system platform. Cloud task scheduling problem has been proved to be a NP-hard problem, The traditional task scheduling algorithm can no longer meet the actual needs of cloud task scheduling, but the Heuristic algorithm is an effective method to solve this problem. This paper studies and analyzes the application of heuristic algorithms in cloud task scheduling problems, and proposes a cloud task scheduling strategy to minimize the task completion time and execution cost (MCTE) for the smart grid cloud. Then, carry out mathematical modeling on the grid cloud task scheduling problem. The experimental results show MCTE is well for the smart grid cloud. INDEX TERMS Smart grid cloud, task scheduling, particle swarm optimization-genetic algorithm.

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“…These performance parameters are makespan, scalability, throughput, cost, resource utilization rate, fault tolerance, migration time and delay. A cloud task scheduling based ABC, PSO and ACO approaches [76] are proposed for the allocation of incoming jobs to VMs with considering the makespan parameter to achieve a high user satisfaction. The cloud task scheduling based on ABC PSO and ACO algorithm can achieve good system load balance than random and FCFS.…”

Section: Aco-abc-pso Task Schedulingmentioning

confidence: 99%

Review of the quality of service scheduling mechanisms in cloud

RemeshBabu¹,

Samuel²

2018

IJET

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Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

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A comparative study into swarm intelligence algorithms for dynamic tasks scheduling in cloud computing

Cited by 29 publications

References 22 publications

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

Review of the quality of service scheduling mechanisms in cloud

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