Quality of Service of Grid Computing

Quality of Service of Grid Computing

Ming Wu (Illinois Institute of Technology, USA) and Xian-He Sun (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-60566-184-1.ch007
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Abstract

Rapid advancement of communication technology has changed the landscape of computing. New models of computing, such as business-on-demand, Web services, peer-to-peer networks, and Grid computing have emerged to harness distributed computing and network resources to provide powerful services. The non-deterministic characteristic of the resource availability in these new computing platforms raises an outstanding challenge: how to support Quality of Service (QoS) to meet a user’s demand? This chapter conducts a thorough study of QoS of distributed computing, especially on Grid computing where the requirement of distributed sharing and coordination goes to the extreme. The research starts at QoS policies, and then focuses on technical issues of the enforcement of the policies and performance optimization under each policy. This chapter provides a classification of QoS metrics and policies, a systematic understanding of QoS, and a framework for QoS of Grid computing.
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Introduction

With the advance of network technology, many new distributed computing models are being constructed to harness geographically distributed computing and communication resources, such as business-on-demand, Web services, peer-to-peer networks, and Grid computing. Typical examples of these systems include WebSphere, Gnutella, Skype, Seti@home, Condor, PPLive (a P2P television network), and Globus (Wu, 2006). The system size of these systems scales from hundreds of nodes to tens of thousands of nodes, and even more. In these systems, resources are shared and collaborated to provide services/functionalities such as online shopping, online telephony and television, teleimmersion, online control of scientific instrumentation, and resource pooling. Much effort is being made in the standardization of protocols and interface for service orchestration and resource collaboration in these environments (Foster and Kesselman, 2004). With the maturity of these systems, when more and more users to use them as day-to-day computing infrastructure, Quality of Service (QoS) of these newly-emerged computing platforms is becoming more and more important.

QoS study was focused on QoS control and delivery in a dedicated environment where resources are controlled and managed in a centralized mechanism. In a shared network environment like a Grid, where resources are shared among different applications and managed within different organizations and domains, there are several new issues related to QoS support that do not arise in a single computer system. The first issue is the variation of resource availability, the accessibility of a system resource to an application. This variation may be due to resource contention, dynamic system configuration, software or hardware failures, and other factors beyond the control of a user. The uncertainty of resource availability presents a big challenge on guaranteed application quality delivery. The second issue is parallel processing. The total workload of a large scale application is often partitioned into smaller pieces, called subtasks. These subtasks are then allocated to resources in a distributed system to be processed concurrently. The challenge of parallel processing in a shared network environment lies on that the computing resources may be heterogeneous and have individual availability patterns. The third issue is non-centralized control. In a general Grid environment, the computing resources are autonomous. Local schedulers schedule local jobs and the Grid scheduler does not have the control of the local jobs.

Because of these difficulties, a suitable and broadly applicable QoS solution has been elusive. This is especially true for Grid computing, where the requirement of distributed sharing and coordination goes to the extreme. QoS is a known technical hurdle preventing a broader adoption of Grid computing for which there has been no well-conducted QoS study to balance the need of Grid tasks and local jobs. Some efforts have been made to address the issues of sharing. Distributed systems, such as Condor, NetSolve, Nimrod, and Globus (Foster and Kesselman, 2004), support Grid computing and facilitate resource sharing and collaboration. These systems adopt different QoS policies, usually implicitly, and try to provide a satisfactory QoS under their adopted policies. These policies often support QoS for one side and sacrifice that of the other – they perform well for certain applications but do not provide a satisfactory solution for general Grid computing.

Without a better understanding of the impact of resource reservation on QoS an appropriate decision cannot be made. Recently, a prototype of QoS system, Grid Harvest Service (GHS) has been developed at Scalable Software System lab in Illinois Institute of Technology (Wu and Sun, 2006). GHS is based on a fundamental understanding of QoS of Grid computing in two stages: policymaking and optimization mechanisms. Policymaking decides the QoS policy of resource sharing among Grid tasks and local jobs. Optimization mechanisms obtain an optimum QoS under each QoS policy. They are integrated solutions of advanced performance modeling, resources management, and scheduling algorithms. These QoS optimization mechanisms provide a comprehensive investigation of the impact of system characteristics, such as resource sharing, non-centralized control, heterogeneity, and dynamics; and application characteristics, such as parallel processing, computation or communication, hard guarantee or soft guarantee, on the application QoS delivery in Grid computing (Wu et al, 2006).

Key Terms in this Chapter

QoS Modeling: The process of building performance models to identify the impact of system parameters and application parameters on application QoS.

Task Scheduling: The process of task scheduling partitions a Grid application into sub-tasks and assigns each sub-task to a selected set of resources based on the pre-developed QoS models to support or optimize user required application QoS.

QoS Architecture: The structure or structures of a software system, which comprise QoS specification component, QoS provision component and QoS management mechanisms.

Resource Management: The management and control of applications running on resources to enforce specific QoS policies, and carry out the scheduling decisions.

Quality of Service (QoS): QoS defines nonfunctional characteristics of a system, affecting the perceived quality of the results.

Resource Reservation: A resource allocation where the system reserves physical resources of host machines or network resources for an application or a class of services. It includes CPU reservation, memory reservation, disk reservation and network reservation.

QoS Metric: A set of parameters to describe and measure the application QoS characteristics such as performance, cost, reliability, security and fidelity.

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Table of Contents
Foreword
Ruth E. Shaw
Preface
Emmanuel Udoh, Frank Zhigang Wang
Acknowledgment
Emmanuel Udoh
Chapter 1
Emmanuel Udoh, Frank Zhigang Wang, Vineet R. Khare
This chapter presents a historical record of the advent of Grid with a recourse to some basic definitions commonly accepted by most researchers. It... Sample PDF
Overview of Grid Computing
$37.50
Chapter 2
Eric Aubanel
The problem of load balancing parallel applications is particularly challenging on computational grids, since the characteristics of both the... Sample PDF
Resource-Aware Load Balancing of Parallel Applications
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Chapter 3
Enis Afgan, Purushotham Bangalore
Grid computing has emerged as the next generation computing platform. Because of the resource heterogeneity that exists in the grid environment... Sample PDF
Assisting Efficient Job Planning and Scheduling in the Grid
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Chapter 4
Kuo-Chan Huang, Po-Chi Shih, Yeh-Ching Chung
Most current grid environments are established through collaboration among a group of participating sites which volunteer to provide free computing... Sample PDF
Effective Resource Allocation and Job Scheduling Mechanisms for Load Sharing in a Computational Grid
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Chapter 5
Tevfik Kosar
As the data requirements of scientific distributed applications increase, the access to remote data becomes the main performance bottleneck for... Sample PDF
Data-Aware Distributed Batch Scheduling
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Chapter 6
Gianni Pucciani, Flavia Donno, Andrea Domenici, Heinz Stockinger
Data replication is a well-known technique used in distributed systems in order to improve fault tolerance and make data access faster. Several... Sample PDF
Consistency of Replicated Datasets in Grid Computing
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Chapter 7
Ming Wu, Xian-He Sun
Rapid advancement of communication technology has changed the landscape of computing. New models of computing, such as business-on-demand, Web... Sample PDF
Quality of Service of Grid Computing
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Chapter 8
QoS in Grid Computing  (pages 75-83)
Zhihui Du, Zhili Cheng, Xiaoying Wang, Chuang Lin
This chapter first summarizes popular terms of QoS related concepts and technologies in grid computing, including SLA, End-to-End QoS Provision and... Sample PDF
QoS in Grid Computing
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Chapter 9
Kris Bubendorfer, Ben Palmer, Ian Welch
A Grid resource broker is the arbiter for access to a Grid’s computational resources and therefore its performance and functionality has a... Sample PDF
Trust and Privacy in Grid Resource Auctions
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Chapter 10
Sandro Fiore, Alessandro Negro, Salvatore Vadacca, Massimo Cafaro, Giovanni Aloisio, Roberto Barbera
Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed... Sample PDF
An Architectural Overview of the GRelC Data Access Service
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Chapter 11
Man Wang, Zhihui Du, Zhili Cheng
Resource Management System (RMS), which manages the Grid resources and matches the applications’ requests to the proper resources, is one of the... Sample PDF
Adaptive Resource Management in Grid Environment
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Chapter 12
Vineet R. Khare, Frank Zhigang Wang
The need for a dynamic and scalable expansion of the grid infrastructure and resources and other scalability issues in terms of execution efficiency... Sample PDF
Bio-Inspired Grid Resource Management
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Chapter 13
Yuhui Deng, Frank Zhigang Wang, Na Helian
Storage Grid is a new model for deploying and managing the heterogeneous, dynamic, large-scale, and geographically distributed storage resources.... Sample PDF
Service Oriented Storage System Grid
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Chapter 14
Dominic Cherry, Maozhen Li, Man Qi
This chapter presents MediaGrid, a distributed storage system for archiving broadcast media contents. MediaGrid utilizes storage resources donated... Sample PDF
A Distributed Storage System for Archiving Broadcast Media Content
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Chapter 15
Maozhen Li, Man Qi, Bin Yu
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale... Sample PDF
Service Discovery with Rough Sets
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Chapter 16
Irfan Habib, Ashiq Anjum, Richard McClatchey
Due to some barriers to adoption we have not seen a proliferation of Grid Computing technologies throughout e-Science or other domains. This chapter... Sample PDF
On the Pervasive Adoption of Grid Technologies: A Grid Operating System
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Chapter 17
Kurt Vanmechelen, Jan Broeckhove, Wim Depoorter, Khalid Abdelkader
As grid computing technology moves further up the adoption curve, the issues of dealing with conflicting user requirements formulated by different... Sample PDF
Pricing Computational Resources in Grid Economies
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Chapter 18
Rosario M. Piro
Large, geographically distributed and heterogeneous computing infrastructures, such as the Grid, often span multiple organizations and... Sample PDF
Resource Usage Accounting in Grid Computing
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Chapter 19
Frans Arickx, Jan Broeckhove, Peter Hellinckx, David Dewolfs, Kurt Vanmechelen
Quantum structure or scattering calculations often belong to a class of computational problems involving the aggregation of a set of matrices... Sample PDF
Grid-Based Nuclear Physics Applications
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Chapter 20
Gabriel Aparicio, Fernando Blanco, Ignacio Blanquer, César Bonavides, Juan Luis Chaves, Miguel Embid, Álvaro Hernández
In the last years an increasing demand for Grid Infrastructures has resulted in several international collaborations. This is the case of the EELA... Sample PDF
Developing Biomedical Applications in the Framework of EELA
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Chapter 21
Gerald Schaefer, Roger Tait
Efficient approaches to computationally intensive image processing tasks are currently highly sought after. In this chapter, the authors show how a... Sample PDF
Distributed Image Processing on a Blackboard System
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Chapter 22
Daniele Andreotti, Armando Fella, Eleonora Luppi
The BaBar experiment uses data since 1999 in examining the violation of charge and parity (CP) symmetry in the field of high energy physics. This... Sample PDF
Simulated Events Production on the Grid for the BaBar Experiment
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Chapter 23
Diego Liberati
A framework is proposed that creates, uses, and communicates information, whose organizational dynamics allows performing a distributed cooperative... Sample PDF
A Framework for Semantic Grid in E-Science
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Chapter 24
Roberto Barbera, Valeria Ardizzone, Leandro Ciuffo
The Grid INFN virtual Laboratory for Dissemination Activities (GILDA) is a fully working Grid test-bed devoted to training and dissemination... Sample PDF
Grid INFN Virtual Laboratory for Dissemination Activities (GILDA)
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Chapter 25
Dirk Gorissen, Tom Dhaene, Piet Demeester, Jan Broeckhove
The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling... Sample PDF
Grid Enabled Surrogate Modeling
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Chapter 26
Patrik Skogster
Grid computing is becoming as essential part of different business analysis. In traditional business computing infrastructures data transfer occurs... Sample PDF
GIS Grids and the Business Use of GIS Data
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Chapter 27
Gokop Goteng, Ashutosh Tiwari, Rajkumar Roy
The emerging grid technology provides a secured platform for multidisciplinary experts in the security intelligence profession to collaborate and... Sample PDF
Grid Computing: Combating Global Terrorism with the World Wide Grid
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Chapter 28
Salvatore Scifo
This chapter focuses on the efforts to design and develop a standard pure Java API to access the metadata service of the EGEE Grid middleware, and... Sample PDF
Accessing Grid Metadata through a Web Interface
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Chapter 29
Jyotsna Sharma
Efforts in Grid Computing, both in academia and industry, continue to grow rapidly worldwide for research, scientific and commercial purposes.... Sample PDF
Grid Computing Initiatives in India
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Chapter 30
Hai Jin, Li Qi, Jie Dai, Yaqin Luo
A grid system is usually composed of thousands of nodes which are broadly distributed in different virtual organizations. Owing to geographical... Sample PDF
Dynamic Maintenance in ChinaGrid Support Platform
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