https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Resource scheduling for infrastructure as a service (IaaS) in cloud computing

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.

A review of task scheduling based on meta-heuristics approach in cloud computing

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

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Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. Requests of asset sharing and reuse become more and more imperative. Efficient task scheduling mechanism should meet users' requirements and improve the resource utilization, so as to enhance the overall performance of the cloud computing environment. In order to solve this problem, considering the new characteristics of cloud computing and original adaptive genetic algorithm(AGA), a new scheduling algorithm based on double-fitness adaptive algorithm-job spanning time and load balancing genetic algorithm(JLGA) is established. This strategy not only works out a tasks scheduling sequence with shorter job and average job makespan, but also satisfies inter-nodes load balancing. At the same time, this paper adopts greedy algorithm to initialize the population, brings in variance to describe the load intensive among nodes, weights multi-fitness function. We then compare the performance of JLGA with AGA through simulations. It proves the validity of the scheduling algorithm and the effectiveness of the optimization method.

Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

With explosive growth in data size at era of information, MapReduce - a programing mode, which can process data in parallel, has been widely used. However, the original system gradually exposes some shortcomings. For example, handling skewed data can cause the imbalance of the system loads. After mapper processes data, the result will be sent to reducer by partition function. An inappropriate partition algorithm may result in poor network quality, the overloading of some reducers and the extension of the execution time of job. In summary, using an inappropriate algorithm to process skewed data will form a negative impact on the system performance. In order to solve load imbalance problem and improve performance of cluster, we plan to design an effective partition algorithm to guide the process of assigning data. Therefore, we develop an algorithm named CLP - Cluster Locality Partition, this algorithm consists of three parts: Preprocess part, Data-Cluster part and Locality-Partition part. The experimental results illustrate that the algorithm proposed in this paper is better than the default partition algorithm in the aspects of execution time and load balancing.

Zhaobin Liu

Papers

Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Load Balancing in MapReduce Based on Data Locality