Optimization of Multi-Dimensional Metrics through Task Scheduling in Cloud Computing Systems (original) (raw)
Related papers
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
Energy-Efficient Task Consolidation for Cloud Data Center
International Journal of Cloud Applications and Computing
Energy saving in a Cloud Computing environment is a multidimensional challenge, which can directly decrease the in-use costs and carbon dioxide emission, while raising the system consistency. The process of maximizing the cloud computing resource utilization which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc., is known as task consolidation. This article suggests the energy saving with task consolidation, by minimizing the number of unused resources in a cloud computing environment. In this article, various task consolidation algorithms such as MinIncreaseinEnergy, MaxUtilECTC, NoIdleMachineECTC, and NoIdleMachineMaxUtil are presented aims to optimize energy consumption of cloud data center. The outcomes have shown that the suggested algorithms surpass the existing ECTC and FCFSMaxUtil, MaxMaxUtil algorithms in terms of the CPU utilization and energy consumption.
International Journal of Electrical and Computer Engineering (IJECE)
Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy. Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.
Energy Efficient Task Scheduling in Cloud Data Center
International Journal of Distributed and Cloud Computing, 2018
Cloud computing is emerging as a necessary need for the IT industry in order to reduce the setup and operational cost of its infrastructure. There is a huge requirement of computing resources to satisfy customer demands. A minute delay in a service may result in a measurable amount of loss for an organization. Response time is a major metric for evaluating performance of cloud applications. Cloud data centers form backbone of cloud computing. Data centers consume enormous amount of energy. Server racks have processing units, storage and network interface. Energy is dissipated at the server racks and cooling units. Various task scheduling algorithms and virtual machine scheduling algorithms have been proposed to measure the loss in performance but the energy loss is kept at the lowest priority. The paper is focused on discussing about the two techniques that maintain a scheduled routine for tasks arriving in a data center through a simulation scenario. VM-specific scheduling of tasks is done for assignment of the tasks to single or multiple virtual machines. Comparison of the two techniques, time-shared and space-shared technique is also done to give the reader a clear view about the situation in which both techniques are used. Future work is also discussed in the same context.
Journal of Computer and Knowledge Engineering, 2022
In cloud computing, task scheduling is one of the most important issues that need to be considered for enhancing system performance and user satisfaction. Although there are many task scheduling strategies, available algorithms mainly focus on reducing the execution time while ignoring the profits of service providers. In order to improve provider profitability as well as meet the user requirements, tasks should be executed with minimal cost and without violating Quality of Service (QoS) restrictions. This study presents a Cost and Energy-aware Task Scheduling Algorithm (CETSA) intending to reduce makespan, energy consumption, and cost. The proposed algorithm considers the trade-off between cost, energy consumption, and makespan while considering the load on each virtual machine to prevent virtual machines from overloading. Experimental results with CloudSim show that the CETSA algorithm has better results in terms of energy consumption, waiting time, success rate, cost, improvement ratio, and degree of imbalance compared with MSDE, CPSO, CJS, and FUGE.
A SURVEY ON ENERGY EFFICIENT WITH TASK CONSOLIDATION IN THE VIRTUALIZED CLOUD COMPUTING ENVIRONMENT
Cloud computing is a new model of computing that is widely used in today's industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment.
A Review on Engery Efficient Strategy for Task Allocation in Cloud Environment
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Although cloud computing is now becoming more advanced and matured as many companies have released their own computing platforms to provide services to public, but the research on cloud computing is still in its infancy. Apart from many other challenges of cloud computing, efficient management of energy is one of the most challenging research issues. In this paper we review the existing algorithm of dynamic resource provisioning and allocation algorithms and holistically work to boost data center energy efficiency and performance. This particular paper purposes a) heterogeneous workload and its implication on data centers energy efficiency b) solving the problem of VM resource scheduling to cloud applications
ENERGY-EFFICIENT TASK SCHEDULING ALGORITHMS FOR CLOUD DATA CENTERS
Cloud computing is a modern technology which contains a network of systems that form a cloud. Energy conservation is one of the major concern in cloud computing. Large amount of energy is wasted by the computers and other devices and the carbon dioxide gas is released into the atmosphere polluting the environment. Green computing is an emerging technology which focuses on preserving the environment by reducing various kinds of pollutions. Pollutions include excessive emission of greenhouse gas, disposal of e-waste and so on leading to greenhouse effect. So pollution needs to be reduced by lowering the energy usage. By doing this, utilization of resources should not be reduced. With less usage of energy, maximum resource utilization should be possible. For this purpose, many green task scheduling algorithms are used so that the energy consumption can be minimized in servers of cloud data centers. In this paper, ESF-ES algorithm is developed which focuses on minimizing energy consumption by minimizing the number of servers used. The comparison is made with hybrid algorithms and most-efficient-server first scheme.
Energy-efficient cloud computing with task consolidation
International Journal of Latest Trends in Engineering and Technology, 2017
Energy saving in a Cloud Computing environment is a multidimensional challenge, which can directly decrease the in-use costs and carbon dioxide emission, while raising the system consistency. We in this paper, suggest the energy saving with task consolidation, by minimizing the number of unused resources in a cloud computing environment. In this paper, it is proposed that task consolidation using Minimization Of IDLE VM algorithm. The existing ECTC has been compared with the recital of the recommend algorithms, and MaxMaxUtil energy conscious task consolidation algorithms (i.e. it minimizes the number of idle resources by allocating a task at an instance to a VM which is currently idle). The outcomes have shown that the suggested algorithms surpass the ECTC and MaxMaxUtil algorithm in terms of the CPU utilization and energy consumption.
IJERT-A Survey of the Impact of Task Scheduling Algorithms on Energy-Efficiency in Cloud Computing
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-survey-of-the-impact-of-task-scheduling-algorithms-on-energy-efficiency-in-cloud-computing https://www.ijert.org/research/a-survey-of-the-impact-of-task-scheduling-algorithms-on-energy-efficiency-in-cloud-computing-IJERTV3IS10624.pdf Cloud computing is a recent and upcoming technology which includes various areas. One among them is energy conservation. Maintaining the efficiency of energy has become a major problem with increased usage of devices consuming more energy. Each and every person has a separate system in the current world. Many efforts have been taken to minimize energy consumption. In this paper, task scheduling is taken as the factor to reduce consumption of energy. Tasks can be assigned and scheduled based on the algorithms and so energy can be conserved.