An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing

Keshanchi, Bahman; Souri, Alireza; Navimipour, Nima Jafari

doi:10.1016/j.jss.2016.07.006

Cited by 255 publications

(123 citation statements)

References 46 publications

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“…Resource scheduling is the placement of the services to perform a set of tasks with the given time bound. One of the objectives of resource scheduling is decreasing the task's makespan . The makespan is the total time that is required for all tasks to be finished.…”

Section: Related Workmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

Self Cite

123

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Section: Related Workmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

Self Cite

123

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“…Keshanchi, et al have presented an optimal task scheduling based on a hybrid algorithm in the cloud which implemented via C#. This hybrid algorithm is a combination of a genetic algorithm and a meta‐heuristic algorithm.…”

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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“…[93][94][95][96] Therefore, new techniques, which enable the query optimization by considering the task scheduling optimization, are extremely promising. [93][94][95][96] Therefore, new techniques, which enable the query optimization by considering the task scheduling optimization, are extremely promising.…”

Section: Open Issuesmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing

Cited by 255 publications

References 46 publications

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Task scheduling mechanisms in cloud computing: A systematic review

Deterministic and non‐deterministic query optimization techniques in the cloud computing

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