Improved Energy Efficient Job Scheduling in Cloud Computing

Cloud computing is the most recent computing paradigm, in the Information Technology where the resources and information are provided on-demand and accessed over the Internet. An essential factor in the cloud computing system is Task Scheduling that relates to the efficiency of the entire cloud computing environment. Mostly in a cloud environment, the issue of scheduling is to apportion the tasks of the requesting users to the available resources. This paper aims to offer a genetic based scheduling algorithm that reduces the waiting time of the overall system. However the tasks enter the cloud environment and the users have to wait until the resources are available that leads to more queue length and increased waiting time. This paper introduces a Task Scheduling algorithm based on genetic algorithm using a queuing model to minimize the waiting time and queue length of the system.

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.

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.

IEEE Access, 2021

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that...

— Cloud computing has been promising as a supple and potent computational structural design to put forward omnipresent services to users. It accommodates inter coupled hardware and software assets in a cohesive way, which is dissimilar to conventional computational environments. A diversity of hardware and software resources are incorporated together as a resource pool, the software is no longer resided in a sole hardware environment, it is performed upon the schedule of the resource pool for optimized resource deployment. The optimization of energy utilization in the cloud computing environment is the issue how to use diverse energy conservation strategies to efficiently allocate resources. In this paper, we study the association among infrastructure components and power utilization of the cloud computing environment, and converse the matching of job types and component power tuning methods, and then we present a resource scheduling algorithm of Cloud Computing based on energy efficient optimization methods.

The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. A Genetic Algorithm (GA) for job scheduling has been proposed and produced good results. The main disadvantage of GA is time consuming problem. In this paper, the Improved GA (IGA) with good initial solution was proposed to reduce the GA execution time.

Cloud computing has come out to be an interesting and beneficial way of changing the whole computing Schedulers for cloud computing determine on which processing resource jobs of a workflow should be allocated. Scheduling theory for cloud computing is in advance a lot of awareness with increasing popularity in this cloud era. So, this paper reviews the optimization of scheduling problem in cloud computing along with various previously used algorithms in the field of scheduling in cloud computing.

IRJET, 2022

The design of cloud computing allows for scalable computing. The cloud components can be used to process many requests and handle them in a timely manner. The cloud aids in the handling of multiple requests and the secure management of user data. There are components that locate a suitable architecture and so offer communication bonding between components. The key communication components available in the cloud are virtual machines, data centres, and user bases. As a result, it is always necessary to handle many requests, assign the appropriate virtual machine to the input request, and then provide the fastest response time possible. There are numerous methods for balancing the load on a virtual machine. The data locality preservation is rigorous in the original article, which makes load balancing across nodes a difficult task when the approach is used. a heuristic method. Most range-queriable cloud storage currently uses a combination of neighbour item exchange and neighbour migration methods, which has a high overhead and sluggish convergence. Algorithms like Round robin, throttle, and other VM allocation aid with machine allocation, but only to a limited extent. While progress is being made toward better virtual machine allocation, finding the best possible allocation is constantly needed in order to increase performance. In this paper, a Rule-based threshold heuristic technique is described as an algorithm. This is the algorithm that combines the many characteristics of virtual machines, as well as their statuses, to determine the optimum virtual machine for request allocation. The method is simulated using the Cloud Analyst simulation tool, and a comparison is done by applying an existing algorithm to several topologies.

International journal of engineering research and technology, 2018

Cloud Computing is one of the emerging technology based upon on demand pay per use model. It is a platform where various services like applications, bandwidth and data are provided to its users using Internet. The main objective of Job Scheduling Algorithms in Cloud Computing is to optimize the resource allocation and utilization to meet user requirements and for cloud service providers it is the efficient use of resources and thus attaining maximum profit. All this leads us right to the requirement of Job Scheduling in Cloud Computing. Scheduling is the method of deciding how to provide resources amongst the various available tasks or processes so as to achieve the maximum throughput efficiently. In this paper various Job Scheduling algorithms have been presented on the basis of different parameters which provide efficient cloud services.

Task scheduling is one of the significant factors for heterogeneous type of elements in multi-cloud computing environment. It is to delegate activities to most adequate resources to raise the performance with respect to some dynamic parameters. The suggested model of scheduling was designed to run applications of cloud computing which applied in three steps (classifying, execution, minimization, and rating) and is assumed to be completion time, average waiting and turnaround time, and render duration as output parameters. The tasks' execution time in cloud computing applications was created in the task scheduling model by exponential and normal distribution. The task ranking depends on the shortest strategy for First Job and the results are compared with other "Largest Processing Time" and "First Come First Serve" ranking method. Scheduling model proposed provides significant output than per performance parameters specified.