Comparison of Various Particle Swarm Optimization based Algorithms in Cloud Computing

Transcription

1 Comparison of Various Particle Swarm Optimization based Algorithms in Cloud Computing Er. Talwinder Kaur M.Tech (CSE) SSIET, Dera Bassi, Punjab, India - Er. Seema Pahwa Department of Information Technology SSIET, Dera Bassi, Punjab, India - Abstract - Cloud Computing is subscription-based service which is used to obtain storage space on network and computer resources. The cloud makes it possible to access information from anywhere at any time. Cloud provides both software and hardware necessary to run various applications according to the needs of the user. To fulfil those needs of user internet connection is required to access the cloud. In a cloud, primary benefit is application scalability which allows real-time provisioning of resources to meet application requirements. In this paper various PSO based scheduling strategies i.e. Particle Swarm Optimization (PSO), Revised Discrete Particle Swarm Optimization (RDPSO), Improved Particle Swarm Optimization are compared on the basis of various parameters. Keywords- Cloud Computing, Scheduling, Particle Swarm Optimization (PSO), Revised Discrete Swarm Model (RDPSO), Improved Particle Swarm Optimization I Introduction Cloud computing is emerging as the latest distributed computing paradigm and attracts increasing interests of researchers. It is used in many applications today that are beyond distribution and sharing of resources [4]. It is a subscription-based service which is used to obtain storage space network and computer resources [5]. It is an extension of parallel computing, distributed computing and grid computing. It provides secure, quick, convenient data storage and computing power with the help of internet [6]. Cloud computing environment facilitates application by providing virtualized resources that can be provisioned dynamically. Cloud computing has attracted an increasing number of users because it offers computational capabilities as service on a pay-per-use basis [7]. 1 International Research Journal of Applied Sciences & Engineering

2 The characteristics of a cloud computing environment are as follows: 1. Elasticity and Scalability: Cloud computing gives the ability to expand and reduce resources according to specific service requirements [8]. 2. Pay-Per-Use: Cloud Services are payed only when they are used either for short term or for a longer duration [8]. 3. On Demand: Because cloud services are invoked only when they are needed, they are not permanent part of IT infrastructure [8]. 4. Resiliency: The resiliency of cloud service offering can completely isolate the failure of server and storage resources from cloud users [8]. Figure 1. Cloud Computing Context II Objectives The objective of this paper is to focus on various algorithms based on PSO. Section III presents key concepts of this paper. Section IV explain about scheduling and Section V presents the various existing PSO based algorithms and their comparison. Section VI explains the Conclusion on the basis the survey and comparison done in the Section V III- KEY CONCEPTS Cloud computing is a term that involves delivering hosted services over the internet. According to the type of services provided cloud computing is classified into three service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (Iaas) [9]. 2 International Research Journal of Applied Sciences & Engineering

3 A. Software as a Service In the SaaS model, cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients. Cloud users do not manage the infrastructure and platform where the application runs. This eliminates the need to install and run the application on the cloud user s own computers, which simplifies maintenance and support [10]. B. Platform as a Service In the PaaS model, cloud providers deliver a computing platform typically including operating system, programming language execution environment, database, and web server. Application developer can develop and run their software solutions on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers. C. Infrastructure as a Service In the most basic cloud-service model, providers of IaaS offer computers- physical or virtual machines- and other resources. IaaS clouds often offer additional resources such as a virtualmachine disk image library, raw and file based storage, firewalls, load balancers, IP addresses, virtual local area networks and software bundles [11]. Figure 2. Service model in Cloud Computing There are two different but related types of cloud services. One is on-demand computing instance and the other is on-demand computing capacity. These two types of cloud services classify cloud computing into two distinct deployment models: Public and Private [12]. a) Public Cloud:- A public cloud can be accessed by any subscriber with an internet connection and access to the cloud space [5]. 3 International Research Journal on Applied Sciences & Engineering

4 b) Private Cloud:- A private cloud is established for a specific group or organization and limits access to just that group [5]. Besides the above two type cloud computing is further classified into two more deployment models: c) Community Cloud:- A community cloud is shared among two or more organizations that have similar cloud requirements [5]. d) Hybrid Cloud:- A hybrid cloud is essentially a combination of at least two clouds, where the clouds included are a mixture of public, private or community [5]. Figure 3. Types of Cloud Computing IV SCHEDULING Scheduling the basic processing units on a computing environment has always been an important issue [13]. Applications and services can be decomposed into set of smaller components, called jobs. The logical sequence of jobs of an application is called workflow. A job can be executed only after the data it depends on has been produced and sent to the resource where it will be executed [7]. The goal of job scheduling is to properly dispatch parallel jobs to slave node machines according to scheduling policy under meeting certain performance indexes and priority constraints to shorten total execution time and lower computing cost and improve system efficiency [4]. V - EXISTING PSO ALGORITHM 4 International Research Journal of Applied Sciences & Engineering

5 Following are the various existing scheduling algorithms for the resource allocation in cloud computing environment: A. Particle Swarm Optimizatione PSO is a swarm based intelligent algorithm. It is self adaptive global search optimization technique introduced by Kennedy and Eberhart [2]. Because of merits of parallel distribution, scalability, easy to realize, strong robustness, with high flexibility and robust in dynamic environments, PSO solves many combinational optimization problems successfully. Task scheduling problem can select a better one from various combinations distributed to task by resources. To solve the problem PSO is very suitable to solve resource scheduling problem in cloud environment [1]. B. Revised Discrete Particle Swarm Optimization PSO is originally designed to find the solution for continuous optimization problems. To solve the workflow scheduling problem, a revised discrete version of PSO (RDPSO) based on the concept of set-based is adopted in The key issue is to define the position and velocity of particle as well as to define their operation rules and the equation of motion according to the features of discrete variables. This model reduces the search space and enhances the algorithm performance. It optimizes the schedules of workflow application in cloud computing environment. This algorithm greatly reduces the search space and enhances the performance [3]. C. Improved Particle Swarm Optimization It has fast global searching ability, improved convergence rate and optimized problem solving accuracy. Based on the IPSO, cloud computing server cluster can fast realize resources discovery, resources matching, scheduling production, task execution [1]. IPSO shortens the average operation time of tasks, supplies proper resources to user task efficiently in the environment, increases utilization ratio of resources. Table 1 summarizes the scheduling algorithms on parameters, their benefits and various tools to implement them for experiment purpose. All these algorithms work in cloud environment. S.No Comparison. Algorithm Parameters Findings Tools 1 Particle Swarm Optimization Resource Utilization, Time Good Distribution of workload, Cost Saving Amazon EC2 5 International Research Journal on Applied Sciences & Engineering

6 2 Revised Discrete Particle Swarm Optimization 3 Improved Particle Swarm Optimization Cost, Makespan Throughput, Cost Cost Saving, Better Performance on Makespan Increased Convergence Speed, Less operation time, Good Resource Utilization Amazon EC2 CloudSim Table -1 Experiment Result VI - CONCLUSION Cloud Computing is used in many applications today that are beyond distribution and sharing of resources. The distributed resources are useful only if the cloud resources are scheduled. Scheduling is a challenging job in cloud because the capability and availability of resources vary dynamically. The demand for scheduling is to achieve high performance computing. In this paper survey of three existing scheduling algorithms i.e. Particle Swarm Optimization, Revised Discrete Particle Swarm Optimization, and Improved Particle Swarm Optimization in cloud computing and compare their various parameters. Therefore on the basis of survey it has been analyzed that there is a need to implement a scheduling algorithm that can improve cost, time, throughput and makespan as compared to existing ones. VII - ACKNOWLEDGEMENT I am very grateful to Er. Satinder Pal Ahuja Associate Prof., Er. Anil Manohar Dogra Asstt Prof., Er. Rubal Jeet Asstt Prof. for their support and motivation in writing this paper. VIII - REFERENCES [1] S. Zhan, H. Huo, Improved PSO-based Task Scheduling Algorithm in Cloud Computing, Journal of Information & Computational Science 9: 13, , [2] S. Pandey, L. Wu, S. Guru, R. Buyya, A Particle Swarm Optimazation (PSO)-based Heuristic for Sceduling Workflow Application in Cloud Computing Environments. [3] Z. Wu, Z. Ni, L. Gu, X. Liu, A revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling. 6 International Research Journal of Applied Sciences & Engineering

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