Cloud service reliability modelling and optimal task scheduling

Cui, Hongyan; Li, Yang; Liu, Xiaofei; Ansari, Nirwan; Liu, Yunjie

doi:10.1049/iet-com.2016.0417

Cited by 34 publications

(16 citation statements)

References 31 publications

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“…In our work, we dynamically rejuvenate software based on general system state individually for each service provider. The meta-heuristic algorithm has been employed since the server selection from candidates is regarded as an NP-hard problem [31]. iii.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

2020

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Software rejuvenation is an effective technique to counteract software ageing in continuously-running applications such as web-service-based systems. In a client-server application, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the receiver's requirements and server's facilities. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, meta-heuristics seems to be suitable. In this study, the proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of ant colony optimisation (ACO) and gravitational emulation local search (GELS) so as to determine the optimal times when rejuvenation can be performed and failure rate can be minimised. The newly proposed method combined the public search capabilities of ACO with local search of GELS algorithm in an effort to create a stable algorithm, which can make reaching the global optimum largely possible in the proposed work. The simulation results revealed that the proposed strategy can decrease the failure rate of web services averagely by 28% in comparison with genetic algorithm and decision-tree strategies.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

2020

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“…Cui, et al have presented an optimal and reliable task scheduling model in the cloud using a meta‐heuristic algorithm trying to minimize cost metric. MATLAB simulation results confirmed that this model could reduce execution time, cost, and makespan efficiently.…”

Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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“…H. Cui, Y. Li, X. Liu, N. Ansari, and Y. Liu [11] presented a multi objective model with constraints for tackling the TS issue in cloud computing. The multiobjective model takes task processing mode of cloud system into account, and illustrates scheduling performance in terms of makespan, flow time and reliability by using Queuing theory and Markov process.…”

Section: Related Workmentioning

confidence: 99%

MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

Reddy¹,

Kumar²

2019

IJISA

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Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature.

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Cloud service reliability modelling and optimal task scheduling

Cited by 34 publications

References 31 publications

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

Efficient improved ant colony optimisation algorithm for dynamic software rejuvenation in web services

Task scheduling mechanisms in cloud computing: A systematic review

MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

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