Grid computing by joshy joseph craig fellenstein download

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Grid Computing is a type of parallel anddistributed system set-up that enables and encourages Joshy Joseph, Craig Fellenstein how to download this pdf. Grid Computing [Joshy Joseph, Craig Fellenstein] on aracer.mobi *FREE* have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App. Table of Contents. Grid Computing. By Joshy Joseph, Craig Fellenstein. Publisher: Prentice Hall PTR. Pub Date: December 30, ISBN:

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Grid Computing By Joshy Joseph Craig Fellenstein Download

Download and Read Free Online Grid Computing Joshy Joseph, Craig Fellenstein. From reader reviews: Nakia Schultz: This Grid Computing book is not . GRID COMPUTING – Introduction: Early Grid Activities, Current Grid Activities, Joshy Joseph and Craig Fellenstein "Grid Computing” ;Pearson Education. Features of Grid Computing; Early Grid Activities; Current Grid Activities; Layered Grid Architecture; Grid . 1)Grid Computing by Joshy Joseph,Craig Fellenstein.

When you turn on the light, the power grid delivers exactly what you need, instantly. What if computers and networks could work that way? Now they can Grid Computing is about the "Business on Demand" revolution: delivering the exact technology resources you need, anywhere, anytime, without complexity or high cost. You can do it all, starting right now, starting with your existing systems. Whether you're an executive, strategist, architect, technologist, or developer, this book will show you how. Master the fundamental concepts underlying grid and utility computing Learn how to start and which applications to start with Understand the state-of-the-art in technologies and standards Use grid computing to maximize the value of existing resources Build more flexible, resilient, and available operational infrastructures Deliver instantaneous access to data and resources on a "sense and respond" basis Eliminate the burden of administering disparate, non-integrated systems In Grid Computing, leading IBM experts bring together best deployment practices, practical guidance on integrating existing resources, and upto-the-minute case studies: all you need to drive business value from the grid computing revolution. All rights reserved.

The application layer often includes the service ware, which performs general management functions like tracking who is providing grid resources and who is using them. The grid users need not be aware of the computational resources that are used for executing their jobs and storing their data. Grid computing is an emerging technology. Grid Applications: There are some basic applications of grid computing as: Application partitioning that involves breaking the problem into discrete pieces Discovery and scheduling of tasks and workflow Data communications distributing the problem data where and when it is required Provisioning and distributing application codes to specific system nodes Results management assisting[3] in the decision processes of the environment Autonomic features such as self-configuration, self-optimization, self-recovery, and self-management Schedulers Schedulers are types of applications responsible for the management of jobs, such as allocating resources needed for any specific job, partitioning of jobs to schedule parallel execution of tasks, data management, event correlation, and servicelevel management capabilities.

These schedulers may be constructed with a local scheduler implementation approach for specific job execution, or another metascheduler or a cluster scheduler for parallel executions.

The jobs submitted to Grid Computing schedulers are evaluated based on their service-level requirements, and then allocated to the respective resources for execution. Load Balancing The Grid Computing infrastructure load-balancing issues are concerned with the traditional load-balancing distribution of workload among the resources in a Grid Computing environment.

This load-balancing feature must always be integrated into any system in order to avoid processing delays and over commitment of resources.

This level of load balancing involves partitioning of jobs, identifying the resources, and queueing of the jobs. Akl discuss about the grid computing. As popularity of the Internet and the availability of powerful computers and the high speed networks as low-cost commodity components are changing the way we use computers today. These technical opportunities have led to the possibility of using geographically distributed resources to solve large-scale problems in science, engineering, and commerce.

Recent research on these topics has led to the emergence of a new paradigm known as Grid computing. Grid computing is used to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient [4] Grid scheduling system is an essential part of the Grid. In this paper author discuss the lots of things. First, the architecture of components involved in scheduling is briefly introduced to provide an intuitive image of the Grid scheduling process.

TO ENHANCE THE PERFORMANCE OF GRID COMPUTING USING SCHEDULING ALGORITHM

Then various Grid scheduling algorithms are discussed from different points of view, such as static vs. Stefka Fidanova, et. Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations.

The goal of grid task scheduling is to achieve high system throughput and to match the application needed with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment.

The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented. This area is based on developed heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper, author introduce a tasks scheduling algorithm for grid computing.

The paper shows how to search for the best tasks scheduling for grid computing. Ryan J. Wisnesky, discuss about the heterogeneous computation of grid computing. Now days, computational grids are becoming more prevalent as the cost of bringing together disparate computing resources declines. However, a number of challenges remain before these grids can be utilized efficiently. This paper explores the results of using several well known scheduling algorithms to schedule work on a grid under probabilistic work arrival rates and varying task completion times.

This paper presents the results of a simulation study of a heterogeneous computational grid using different scheduling algorithms. After a definition of robustness based on the concept of work completion latency is discussed, a method to simulate grids based on estimated time to compute matrices is presented. Three well known scheduling algorithms are then evaluated against each other, and the highest-performing scheduler is then analyzed in detail.

The notion of ETC perturbation is presented, and this high-performing scheduling algorithm is found to be relatively robust against uncertainties in estimated task completion times. Our literature survey indicates that efforts are made to to minimize the total power consumptions in computing system. Power consumption has become the major challenge to system performance and reliability. We can implement the MCT minimum completion time and MET Minimum execution time algorithm to increase the performance in terms of their speed of execution.

Along with we can implement the PFM algorithm to reduce the power consumption, reduce its waiting time and improved its execution with the help of MCT or MET algorithm.

I am also very thankful to my parents and all of their help in supporting and encouraging me in my professional career aspirations. Craig, I thank you for introducing me to the world of authorship, and for helping me sort out the complexities of technical and educational book composition. Last but certainly not least, I wish to thank everyone in my family and all of my friends for their support in helping me to develop this book. From Craig Fellenstein I would like to extend a very sincere thank you to my family, who is absolutely more important than Business On Demand.

It was my familyL indsey, Jimmy, and Elizabeth w ho supported me in the many late night hours required to develop this book.

I would also like to thank my father, Jim, my sister, Nancy, and my wife's mother, Dorothy, for their unconditional encouragement and love in helping me find the energy to complete not only this book, but a second book in parallel, entitled Business On Demand: T echnologies and Strategy Perspectives. Joshy, I thank you for your many late night hours and outstanding leadership in the creation of this book: Y ou are, indeed, a world-class professional role model, and expert practitioner in the discipline of Grid Computing.

Thanks also to my contributing editor, Elizabeth Fellenstein. Please accept my warmest and most sincere thank you to each of you. Part 1: Grid Computing I n today's incredibly complex w orld of computational pow er, very high speed machine processing capabilities, complex data storage methods, next-generation telecommunications, new -generation operating systems and services, and extremely advanced netw orking services capabilities w e are entering a new era of computing.

At the same time, industry, businesses, and home users alike are placing more complex and challenging demands on the netw orks.

I n this book w e explore all of these aspects in simple to understand terms as w e unveil a new era of computing, simply referred to as "Grid Computing. T his part of the book unveils many of these pow erful approaches to this new era of computing, and explores w hy so many are considering a Grid Computing environment as a single, incredibly pow erful, and effective computing solution. Chapter 1.

Grid Computing

Introduction In today's pervasive world of needing information anytime and anywhere, the explosive Grid Computing environments have now proven to be so significant that they are often referred to as being the world's single and most powerful computer solutions.

It has been realized that with the many benefits of Grid Computing, we have consequently introduced both a complicated and complex global environment, which leverages a multitude of open standards and technologies in a wide variety of implementation schemes.

As a matter of fact the complexity and dynamic nature of industrial problems in today's world are much more intensive to satisfy by the more traditional, single computational platform approaches. Grid Computing equates to the world's largest computer The Grid C omputing discipline involves the actual networking services and connections of a potentially unlimited number of ubiquitous computing devices within a "grid.

This delivery of utility-based power has become second nature to many of us, worldwide.

We know that by simply walking into a room and turning on the lights, the power will be directed to the proper devices of our choice for that moment in time. In this same utility fashion, Grid C omputing openly seeks and is capable of adding an infinite number of computing devices into any grid environment, adding to the computing capability and problem resolution tasks within the operational grid environment.

The incredible problem resolution capabilities of Grid C omputing remain yet unknown, as we continue to forge ahead and enter this new era of massively powerful grid-based problem-solving solutions. This "Introduction" section of the book will begin to present many of the Grid Computing topics, which are discussed throughout this book.

These discussions in Chapter 1 are intended only to provide a rather high-level examination of Grid Computing. Later sections of the book provide a full treatment of the topics addressed by many worldwide communities utilizing and continuing to develop Grid Computing. The worldwide business demand requiring intense problem-solving capabilities for incredibly complex problems has driven in all global industry segments the need for dynamic collaboration of many ubiquitous computing resources to be able to work together.

These difficult computational problemsolving needs have now fostered many complexities in virtually all computing technologies, while driving up costs and operational aspects of the technology environments. However, this advanced computing collaboration capability is indeed required in almost all areas of industrial and business problem solving, ranging from scientific studies to commercial solutions to academic endeavors.

It is a difficult challenge across all the technical communities to achieve this level of resource collaboration needed for solving these complex and dynamic problems, within the bounds of the necessary quality requirements of the end user. To further illustrate this environment and oftentimes very complex set of technology challenges, let us consider some common use case scenarios one might have already encountered, which will begin to examine the many values of a Grid Computing solution environment.

These simple use cases, for purposes of introduction to the concepts of Grid Computing, are as follows: A financial organization processing wealth management application collaborates with the different departments for more computational power and software modeling applications. It pools a number of computing resources, which can thereby perform faster with real-time executions of the tasks and immediate access to complex pools of data storage, all while managing complicated data transfer tasks.

This ultimately results in increased customer satisfaction with a faster turnaround time. A group of scientists studying the atmospheric ozone layer will collect huge amounts of experimental data, each and every day. These scientists need efficient and complex data storage capabilities across wide and geographically dispersed storage facilities, and they need to access this data in an efficient manner based on the processing needs.

This ultimately results in a more effective and efficient means of performing important scientific research. Massive online multiplayer game scenarios for a wide community of international gaming participants are occurring that require a large number of gaming computer servers instead of a dedicated game server.

This allows international game players to interact among themselves as a group in a real-time manner.

Joseph & Fellenstein, Grid Computing | Pearson

This involves the need for on-demand allocation and provisioning of computer resources, provisioning and self-management of complex networks, and complicated data storage resources.

This on-demand need is very dynamic, from momentto-moment, and it is always based upon the workload in the system at any given moment in time. This ultimately results in larger gaming communities, requiring more complex infrastructures to sustain the traffic loads, delivering more profits to the bottom lines of gaming corporations, and higher degrees of customer satisfaction to the gaming participants.

A government organization studying a natural disaster such as a chemical spill may need to immediately collaborate with different departments in order to plan for and best manage the disaster. These organizations may need to simulate many computational models related to the spill in order to calculate the spread of the spill, effect of the weather on the spill, or to determine the impact on human health factors.

This ultimately results in protection and safety matters being provided for public safety issues, wildlife management and protection issues, and ecosystem protection matters: Needles to say all of which are very key concerns. Today, Grid Computing offers many solutions that already address and resolve the above problems.

Grid Computing solutions are constructed using a variety of technologies and open standards.

Grid Computing, in turn, provides highly scalable, highly secure, and extremely high-performance mechanisms for discovering and negotiating access to remote computing resources in a seamless manner. This makes it possible for the sharing of computing resources, on an unprecedented scale, among an infinite number of geographically distributed groups. This serves as a significant transformation agent for individual and corporate implementations surrounding computing practices, toward a general-purpose utility approach very similar in concept to providing electricity or water.

These electrical and water types of utilities, much like Grid Computing utilities, are available "on demand," and will always be capable of providing an always-available facility negotiated for individual or corporate utilization. In this new and intriguing book, we will begin our discussion on the core concepts of the Grid Computing system with an early definition of grid. In addition to these qualifications of coordinated resource sharing and the formation of dynamic virtual organizations, open standards become a key underpinning.

It is important that there are open standards throughout the grid implementation, which also accommodate a variety of other open standardsbased protocols and frameworks, in order to provide interoperable and extensible infrastructure environments.

Grid Computing environments must be constructed upon the following foundations: Coordinated resources. W e should avoid building grid systems with a centralized control; instead, we must provide the necessary infrastructure for coordination among the resources, based on respective policies and service-level agreements.

Open standard protocols and framew orks. The use of open standards provides interoperability and integration facilities. These standards must be applied for resource discovery, resource access, and resource coordination. Another basic requirement of a Grid Computing system is the ability to provide the quality of service QoS requirements necessary for the end-user community.

These QoS validations must be a basic feature in any Grid system, and must be done in congruence with the available resource matrices. These QoS features can be for example response time measures, aggregated performance, security fulfillment, resource scalability, availability, autonomic features such as event correlation and configuration management, and partial fail over mechanisms.

There have been a number of activities addressing the above definitions of Grid Computing and the requirements for a grid system. The most notable effort is in the standardization of the interfaces and protocols for the Grid Computing infrastructure implementations. W e will cover the details later in this book. Let us now explore some early and current Grid Computing systems and their differences in terms of benefits.

Early Grid Activities Over the past several years, there has been a lot of interest in computational Grid Computing worldwide. W e also note a number of derivatives of Grid Computing, including compute grids, data grids, science grids, access grids, knowledge grids, cluster grids, terra grids, and commodity grids. As we explore careful examination of these grids, we can see that they all share some form of resources; however, these grids may have differing architectures.

One key value of a grid, whether it is a commodity utility grid or a computational grid, is often evaluated based on its business merits and the respective user satisfaction.

User satisfaction is measured based on the QoS provided by the grid, such as the availability, performance, simplicity of access, management aspects, business values, and flexibility in pricing. The business merits most often relate to and indicate the problem being solved by the grid.

For instance, it can be job executions, management aspects, simulation workflows, and other key technology-based foundations. Earlier Grid Computing efforts were aligned with the overlapping functional areas of data, computation, and their respective access mechanisms.

Let us further explore the details of these areas to better understand their utilization and functional requirements. Data The data aspects of any Grid Computing environment must be able to effectively manage all aspects of data, including data location, data transfer, data access, and critical aspects of security.

The core functional data requirements for Grid Computing applications are: The ability to integrate multiple distributed, heterogeneous, and independently managed data sources.

The ability to provide efficient data transfer mechanisms and to provide data where the computation will take place for better scalability and efficiency. The ability to provide necessary data discovery mechanisms, which allow the user to find data based on characteristics of the data. The capability to implement data encryption and integrity checks to ensure that data is transported across the network in a secure fashion.

Computation The core functional computational requirements for grid applications are: The ability to allow for independent management of computing resources The ability to provide mechanisms that can intelligently and transparently select computing resources capable of running a user's job The understanding of the current and predicted loads on grid resources, resource availability, dynamic resource configuration, and provisioning Failure detection and failover mechanisms Ensure appropriate security mechanisms for secure resource management, access, and integrity Let us further explore some details on the computational and data grids as they exist today.

Computational and Data Grids In today's complex world of high speed computing, computers have become extremely powerful as to that of let's say five years ago. Even the home-based PCs available on the commercial markets are powerful enough for accomplishing complex computations that we could not have imagined a decade prior to today. The quality and quantity requirements for some business-related advanced computing applications are also becoming more and more complex.

These requirements can actually exceed the demands and availability of installed computational power within an organization. Sometimes, we find that no single organization alone satisfies some of these aforementioned computational requirements.

This advanced computing power applications need is indeed analogous to the electric power need in the early s, such that to provide for the availability of electrical power, each user has to build and be prepared to operate an electrical generator. Thus, when the electric power grid became a reality, this changed the entire concept of the providing for, and utilization of, electrical power.

This, in turn, paved the way for an evolution related to the utilization of electricity. In a similar fashion, the computational grids change the perception on the utility and availability of the computer power. Thus the computational Grid Computing environment became a reality, which provides a demanddriven, reliable, powerful, and yet inexpensive computational power for its customers.

Later in this book, in the "Grid Anatomy" section, we will see that this definition has evolved to give more emphasis on the seamless resource sharing aspects in a collaborative virtual organizational world. But the concept still holds for a computational grid where the sharable resource remains a computing power. As of now, the majority of the computational grids are centered on major scientific experiments and collaborative environments.

The requirement for key data forms a core underpinning of any Grid Computing environment. For example, in data-intensive grids, the focus is on the management of data, which is being held in a variety of data storage facilities in geographically dispersed locations.

These data sources can be databases, file systems, and storage devices. The grid systems must also be capable of providing data virtualization services to provide transparency for data access, integration, and processing.

In addition to the above requirements, security and privacy requirements of all respective data in a grid system is quite complex. W e can summarize the data requirements in the early grid solutions as follows: The ability to discover data The access to databases, utilizing meta-data and other attributes of the data The provisioning of computing facilities for high-speed data movement The capability to support flexible data access and data filtering capabilities As one begins to realize the importance of extreme high performance-related issues in a Grid Computing environment, it is recommended to store or cache data near to the computation, and to provide a common interface for data access and management.

It is interesting to note that upon careful examination of existing Grid Computing systems, readers will learn that many Grid Computing systems are being applied in several important scientific research and collaboration projects; however, this does not preclude the importance of Grid Computing in business-, academic-, and industry-related fields.

The commercialization of Grid Computing invites and addresses a key architectural alignment with several existing commercial frameworks for improved interoperability and integration. As we will describe in this book, many current trends in Grid Computing are toward service-based architectures for grid environments. This "architecture" is built for interoperability and is again based upon open standard protocols.

Many of these projects aren't persistent, which means that once the respective project's goals are met, the system will dissolve. In some cases, a new, related project could take the place of the completed one. While each of these projects has its own unique features, in general, the process of participation is the same. A user interested in participating downloads an application from the respective project's Web site.

After installation, the application contacts the respective project's control node. The control node sends a chunk of data to the user's computer for analysis. The software analyzes the data, powered by untapped CPU resources. The project's software has a very low resource priority -- if the user needs to activate a program that requires a lot of processing power, the project software shuts down temporarily.

Once CPU usage returns to normal, the software begins analyzing data again. Eventually, the user's computer will complete the requested data analysis. At that time, the project software sends the data back to the control node, which relays it to the proper database.

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