An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

Ant Colony Optimization for Efficient Resource Allocation in Cloud Computing

Resource scheduling and energy consumption is an important issue of Cloud Computing. The intention of optimization for scheduling resources is an important issue to be considered in scheduling of different resources among heterogeneous users. The resources are placed in a distributed location in cloud and the major task is to distribute the resources effectively such that the processing time and energy is reduced. In this paper, Ant Colony optimization technique is proposed to optimize the resources in an efficient manner. ACO is used to choose one among the different alternative paths to determine the processing order of each resource. The search space is reduced to provide a better solution. Travelling Salesman Problem(TSP) is the application that is used here to find the shortest path to the destination. This reduces the delay in allocating resources to the user by providing a global search technique. The energy conservation which is the main objective of Green Computing, can also be achieved using this technique

A Model of Resource- Aware Load Balancing Scheme using Multi-objective Optimization in Cloud Environment

Cloud computing is a new class of network based computing that provides the customers with computing resources as a service over a network on their demand. The unique concept of cloud computing creates new opportunities for Business and IT enterprises to achieve their goals. In cloud computing, usually there are number of jobs that need to be executed with the available resources to achieve optimal performance, least possible total time for completion, shortest response time, and efficient utilization of resources etc. To accomplish these goals and achieve high performance, it is important to design and develop a multi objective scheduling algorithm. Hence it is most challenging to schedule the tasks along with satisfying the user’s Quality of Service requirements. This paper proposes a multi- objective scheduling algorithm that considers wide variety of attributes in cloud environment. The paper aims to improve the performance of CPU, memory and network operations by reducing the load of a virtual machine (VM) by using Load Balancing Method. Finally, it optimizes the resource utilization by using Resource Aware Scheduling Algorithm. Keywords: VM, QoS, Non- dominated sorting, Pareto optimal, Makespan, AHP

Design of Simulator to Evaluate Performance Parameters of Scheduling Clouds in Virtual Machine Environment

Cloud computing acts as a vision of infinite computing resources that are provided on-demand to the cloud users as needed and are billed on pay as per usage basis Cloud computing employs the concept of virtualization that provides an opportunity to achieve business and IT objectives Scheduling is one of the most important challenges that a cloud computing environment faces Scheduling process determines the order of execution of jobs and the virtual machine to which job is assigned to execute so as to improve the performance and quality of service and at the same time resources are utilized effectively An attempt has been made in this paper to develop a simulator to schedule a job on allocated virtual machine so as to make efficient resource utilization In the proposed work scheduling is done on the basis of availability of allocated virtual machine providing equal capabilities We assumed that arrival of jobs in a cloud and their execution on a virtual machine is exponentially distributed For analysing the proposed algorithm the performance parameters of scheduling clouds are evaluated in Virtual Machine Environmen

A STUDY ON CLOUD COMPUTING EFFICIENT JOB SCHEDULING ALGORITHMS

cloud computing is a general term used to depict another class of system based computing that happens over the web. The essential advantage of moving to Clouds is application versatility. Cloud computing is extremely advantageous for the application which are sharing their resources on various hubs. Scheduling the errand is a significant testing in cloud condition. Typically undertakings are planned by client prerequisites. New scheduling techniques should be proposed to defeat the issues proposed by organize properties amongst client and resources. New scheduling systems may utilize a portion of the customary scheduling ideas to consolidate them with some system mindful procedures to give answers for better and more effective employment scheduling. Scheduling technique is the key innovation in cloud computing. This paper gives the study on scheduling calculations. There working regarding the resource sharing. We systemize the scheduling issue in cloud computing, and present a cloud scheduling pecking order

An Effective PSO-inspired Algorithm for Workflow Scheduling

The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible

A hybrid approach for scheduling applications in cloud computing environment

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list

Cloud Computing CPU Allocation and Scheduling Algorithms using CloudSim Simulator

In this paper, we describe the Cloud Computing basic compute resources scheduling and allocation algorithms, in addition to the working mechanism. This paper also presents a number of experiments conducted based on CloudSim simulation toolkit in order to assess and evaluate the performance of these scheduling algorithms on Cloud Computing like infrastructure. Furthermore, we introduced and explained the CloudSim simulator design, architecture and proposed two new scheduling algorithms to enhance the existent ones and highlight the weaknesses and/or effectiveness of these algorithms

Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research