Hybrid Particle Swarm Optimization scheduling for cloud computing

Sridhar, M.; Babu, G. Rama Mohan

doi:10.1109/iadcc.2015.7154892

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…Moreover, ACO also uses the global updating process and job rescheduling. [80] proposed a new PSO and TS algorithm in which PSO executes global search and TS performs a local search. The idea behind this hybrid approach is to develop both local (in confined space) and global solutions.…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

et al. 2020

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Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

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Section: Hybrid Algorithmsmentioning

confidence: 99%

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

et al. 2020

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“…Environment Type of approach [12] total execution cost, security and deadline Cloud environment Meta-heuristic [13] completion time Cloud environment heuristic [16] security, completion time, cost and load balancing Cloud environment Meta-heuristic [17] Reliability, total cost, execution cost, total turnaround, total waiting time and total execution time Cloud environment heuristic [19] makespan and reliability Cloud environment Meta-heuristic [20] cost Cloud environment Meta-heuristic [21] execution cost and makespan Cloud environment Meta-heuristic [22] cost Cloud environment [23] failure rate and makespan Cloud environment Meta-heuristic [24] tanent cost Cloud environment heuristic [25] execution time, security and budget Cloud environment heuristic [26] decreases cost, round trip time, execution time, transmission time and reliability Cloud environment Meta-heuristic [28] computation cost, data transmission cost and the cost of data storage Cloud environment Meta-heuristic [29] data transmission cost Cloud environment Meta-heuristic [30] execution time Cloud environment Meta-heuristic [31] cost, makespan and energy consumption Cloud environment heuristic [32] makespan and budget Cloud environment Meta-heuristic [33] execution cost, deadline and budget Cloud environment Meta-heuristic [34] communication cost and computation cost Cloud environment Meta-heuristic [35] energy consumption, reliability, deadline and budget Cloud environment heuristic…”

Section: Featurementioning

confidence: 99%

“…A hybrid Particle Swarm Optimization for workflow scheduling in cloud computing is investigated by Sridhar. This algorithm picks out proper resources and handles load among resources and decreases the execution time [30]. An endocrine-based co-evolutionary multi-swarm for multi-objective optimization algorithm (ECMSMOO), for workflow scheduling in cloud computing system is proposed by Yao et al, [31].…”

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confidence: 99%

A New Optimization Method for Security-Constrained Workflow Scheduling

Abdali¹,

Nia²

2019

INDJCSE

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Broadly speaking, scheduling is a process of developing bunches of security policies and to control the certain available tasks which have to be taken care of using a computer system. Care should be exercised that a scheduler in a particular fields should be quite cautious in adapting scheduling strategy as the environment and the version of task change. In many cases, security is proven to be able to encourage relevance between two entities for a long time. This does imply that a secure scheduling algorithm, of course, can completely alleviate the failure probability during task execution in a security environment. Therefore, to move in a direction of a systematic approach, this paper, very natural, aims at introducing a brand new reliable algorithm using different quality of service (QoS) parameters to manage the scheduling large number of workflows. To do this, it combines two metaheuristic algorithms, CPSO and GA. It should be noted, however, that three different QoS such as execution cost, loadbalancing and security are employed as the most immediate performance measure to handle the scheduling process. Conclusion reached through this algorithm mention that its total execution cost will be minimized while meeting deadline and risk rate constraints. Verification of the proposed algorithm with other algorithms was taken into consideration. The accumulation of results through exercising this algorithm reveals that optimal solution to the problems is promised. Therefore, it is highly desirable to claim that obtained results from this effective approach are better than with the approach used with other algorithms such as CPSO and GA.

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“…Moving further, ACO is applied with "global updation procedure" and "task rescheduling" is also handled by ACO as well. • PSO and TS Dr. Sridhar et al [81] have projected a new algorithm that includes TS and PSO in which PSO is used to perform "global search" and TS performed "local search". The plan behind this hybrid approach is to enhance the solutions both globally and in confined space as well.…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Quality of Service (QoS) Aware Workflow Scheduling (WFS) in Cloud Computing: A Systematic Review

et al. 2018

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Workflow scheduling concerns the mapping of complex tasks to cloud resources by taking into account various Quality of Service requirements. In virtue of continuous proliferation in the exploration of cloud computing, it has become stringent to find the proper scheduling scheme for the execution of workflow under user specifications. Moreover, till date, there exists no systematic review of the existing numerous techniques for this NP-complete problem in the cloud. Taking this into account, the present study seeks to address this gap and spotlights the comprehensive taxonomy of various scheduling schemes as well as extensively compares them by illuminating their objectives, features, merits, and demerits. This paper also highlights the future research challenges with an aim to foster more research in the realm of workflow scheduling as an optimization task.

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Hybrid Particle Swarm Optimization scheduling for cloud computing

Cited by 20 publications

References 14 publications

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

A New Optimization Method for Security-Constrained Workflow Scheduling

Quality of Service (QoS) Aware Workflow Scheduling (WFS) in Cloud Computing: A Systematic Review

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