AN IMPROVED JOB SCHEDULING ALGORITHM IN GRID COMPUTING ENVIRONMENT USING FAULT TOLERANCE MECHANISMS

MSC Project Topics and Complete Thesis in Computer Science

ABSTRACT

Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing time, and resource failure in grid is no longer an exception but a regular event. And resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that long-running applications are able to tolerate failures and avoid computation of the task from scratch when resource failure occurred, to satisfy the user’s QoS requirement. An Improved Job Scheduling Algorithm in Grid Computing Environment Using Fault Tolerance Mechanisms is proposed. The technique employed here, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check pointing aims at reducing the amount of work that is lost upon failure of the system by intermediately saving the state of the system. A comparison of our proposed approaches with Moallem‟s ACO, the result shows that the proposed algorithm achieved up to 13% reduction in make span, 12% maximization in terms of throughput and 12% maximization in ATA when the Gridlets are varied and the resources are kept constant. Also when the Resources are varied and Gridlets are kept constant, the proposed algorithm achieved 18% reduction in make span, 18% maximization in terms of throughput and up to 14% maximization in ATA.

CHAPTER ONE 

INTRODUCTION

1.1 Background of the Study

Computational approaches to problem solving have proven their worth in almost every field of human endeavor. Scientists in fields such as health, meteorology, astrophysics and many more are in need of huge processing power to perform complex calculations in a reasonable amount of time. It might take decade to run a set of modeling experiments on a standard personal computer. Buying a supercomputer costs millions of dollars and thousands more each year to maintain it. That's not to mention the hefty electric bill to keep the massive system running. The standard Personal Computers today have great processing power. The standard tasks for an average user’s computer vary very little, usually including word processing, Internet browsing, spreadsheets and presentations.

Owning to the fact that high performance computing resources are expensive and hard to access, option was to use confederated resources that could comprise computation, storage and network resources from multiple geographically distributed institutions (Foster et al., 2008). As most systems are idle for significant periods of time, it should be possible to harness their idleness or unused resources and apply them towards projects in need of such resources. The Grid paradigm now emerged, led by Foster, Carl Kesselman, and Steve Tuecke (Foster et al, 2008; Rhodes, 2006) called “fathers of Grid” (Haque et al., 2012). They got together to develop a toolkit to handle computation management, data movement, storage management and other infrastructure that could handle large grids without restricting themselves to specific hardware and requirement(Barboni, 2011).

Grid emerges from solving computational problems which otherwise cannot be solved by single personal computer. Such computational problems are financial modeling, weather modeling, data visualization etc. This extremely high computing power is achieved by optimal utilization of distributed heterogeneous resources which are lying idle. This has enable scientists to broaden their simulations and experiments to take into account more parameters (like large values) than ever before. Imagine millions of computers owned by individuals and institutions from various countries across the world connected to form a single, huge, super-computer so as to utilize the resources as depicted.

(http://eu-datagrid.web.cern.ch/eu-datagrid/images/images/grid-small-prov.jpg) The term Grid is analogous with “electrical power grids”, that provide consistent, pervasive, reliable, transparent access to utility power irrespective of location source(Rhodes, 2006; Pritpal and Gurinderpal, 2013; Foster and Kesselman, 2002). Grid computing is concerned with coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. The key concept is the ability to negotiate resource-sharing arrangements among a set of participating parties (providers and consumers) and then to use the resulting resource pool for some purpose (Foster, 2002). Grid enables the sharing, selection, and aggregation of a wide variety of geographically distributed resources including supercomputers, storage systems, data sources, and specialized devices owned by different organizations for solving large-scale resource intensive problems in science, engineering, and commerce(Buyya, 2002).

1.1.1 General Issues in Grid System: Principles

Four main aspects characterize a grid(Rhodes, 2006; Buyya, 2002):

1. Multiple Administrative Domains and Autonomy:Grid resources are geographically distributed across multiple administrative domains, most often in different time zones, owned by different organizations. It is obliged to honor the autonomy of resource owners together with their local resource management and usage policies.

2. Heterogeneity: A Grid involves a collection of resources, heterogeneous in nature and will comprise enormous range of technologies.

3. Scalability. A Grid might grow from a small number of integrated resources to millions. This led to problem of potential performance degradation as the size of Grids increases. Subsequently, applications requiring numerous geographically located resources must be designed to be latency and bandwidth tolerant.

4. Dynamicity or adaptability: With so many resources in a Grid, the probability of some resource failing is high. Therefore resource failure should be considered as a rule rather than the exception. Applications or Resource managers must adapt their behavior dynamically and use the available resources and services efficiently and effectively.

Due to dynamic nature of grid, more failures are likely to occur in grid environments, thereby affecting the time needed to execute job applications and therefore degrading the performance of the system. Grid compute intensive applications often require much longer execution time in order to solve a single problem. The huge computing potential of grids, usually, remains unexploited due to their susceptibility to failures like, process failures, machine crashes, and network failures etc (Garg and Kumar, 2011). The failure of a resource running a user job has a huge effect on the Grid performance. Hence, in order to ensure the high system availability, the job site failure handling is inevitable.I n grid computational system, incorporating fault tolerant algorithms in grid scheduling is advocated. Swarm Intelligence for Distributed Job Scheduling on the grid algorithm proposed by Moallem (2009) will be enhanced by incorporating Fault tolerant technique.

MSC Project Topics and Complete Thesis in Computer Science

AN IMPROVED JOB SCHEDULING ALGORITHM IN GRID COMPUTING ENVIRONMENT USING FAULT TOLERANCE MECHANISMS

Department: Computer Science (M.Sc)

Format: MS Word

Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix

No. of Pages: 103

NB: The Complete Thesis is well written and ready to use. 

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