Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing env
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Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment Fatemeh Ebadifard1 • Seyed Morteza Babamir1 Received: 4 October 2019 / Revised: 21 August 2020 / Accepted: 25 August 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Applying the load balancing technique to allocate requests that dynamically enter the cloud environment is contributive in maintaining the system stability, reducing the response time, and increasing the resource productivity. One of the main challenges in dynamic load balancing is that it increases inter-VM communication overheads (swapping files between VMs). In most of the methods proposed for load balancing the issue of communication overheads is overlooked. Attempt is made here to address this problem through the Autonomous Load Balancing method. In the available studies on task scheduling in cloud computing, the focus is mostly on CPU-bound requests. Here, based on the resources, the needed the requests are divided into CPU-bound and I/O-bound requests. Considering both types of requests leads to the inability to apply the available load balancing methods. The CloudSim tool is applied here to evaluate this proposed method, which is then compared with Round Robin, Autonomous, Honey-Bee and Naı¨ve Bayesian Load Balancing approaches. The results for the actual workloads of the NASA and Calgary servers and sample workload indicate that upon an increase in the requests and their variations together with heterogeneity of different VMs, this proposed algorithm can distribute the workload among them equally and allocate requests to appropriate VMs based on the required resources; thus, a decrease in the communication overheads and an increase in load balancing degree. Keywords Cloud computing Autonomous task scheduling Autonomous load balancing CPU-bound and I/O-bound request
1 Introduction Cloud computing is a new technology that provides users with on-demand access to collective resources. As the number of cloud-based services increases on day-to-day basis, it is necessary to increase cloud environment performance to meet customer demand. Among the many challenges in cloud computing, task scheduling is a key process at SaaS level, with the objective to run system requests on resources based on the characteristics of the cloud in an efficient manner. As to customers, an appropriate scheduling algorithm should be able to run users’ tasks on demand on the virtual machine (VM) in the least time, in the same sense, the service provider needs a type of & Seyed Morteza Babamir [email protected] 1
Department of Computer Engineering, University of Kashan, Kashan, Iran
scheduling which can make the maximum use of the resources while meeting the customers’ demand. To accomplish this, the service provider assigns more than one task to one or more VMs where tasks are run concurrently. Service providers want to assure of proper distribution of a w
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