Comparison Between Cloud Computing, Grid Computing and Distributed Computing on Working Aspect
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Distributed & Cloud Computing: Lecture 1
Distributed & cloud computing: Lecture 1 Chokchai “Box” Leangsuksun SWECO Endowed Professor, Computer Science Louisiana Tech University [email protected] CTO, PB Tech International Inc. [email protected] Class Info ■" Class Hours: noon-1:50 pm T-Th ! ■" Dr. Box’s & Contact Info: ●"www.latech.edu/~box ●"Phone 318-257-3291 ●"Email: [email protected] Class Info ■" Main Text: ! ●" 1) Distributed Systems: Concepts and Design By Coulouris, Dollimore, Kindberg and Blair Edition 5, © Addison-Wesley 2012 ●" 2) related publications & online literatures Class Info ■" Objective: ! ●"the theory, design and implementation of distributed systems ●"Discussion on abstractions, Concepts and current systems/applications ●"Best practice on Research on DS & cloud computing Course Contents ! •" The Characterization of distributed computing and cloud computing. •" System Models. •" Networking and inter-process communication. •" OS supports and Virtualization. •" RAS, Performance & Reliability Modeling. Security. !! Course Contents •" Introduction to Cloud Computing ! •" Various models and applications. •" Deployment models •" Service models (SaaS, PaaS, IaaS, Xaas) •" Public Cloud: Amazon AWS •" Private Cloud: openStack. •" Cost models ( between cloud vs host your own) ! •" ! !!! Course Contents case studies! ! •" Introduction to HPC! •" Multicore & openMP! •" Manycore, GPGPU & CUDA! •" Cloud-based EKG system! •" Distributed Object & Web Services (if time allows)! •" Distributed File system (if time allows)! •" Replication & Disaster Recovery Preparedness! !!!!!! Important Dates ! ■" Apr 10: Mid Term exam ■" Apr 22: term paper & presentation due ■" May 15: Final exam Evaluations ! ■" 20% Lab Exercises/Quizzes ■" 20% Programming Assignments ■" 10% term paper ■" 20% Midterm Exam ■" 30% Final Exam Intro to Distributed Computing •" Distributed System Definitions. •" Distributed Systems Examples: –" The Internet. –" Intranets. –" Mobile Computing –" Cloud Computing •" Resource Sharing. •" The World Wide Web. •" Distributed Systems Challenges. Based on Mostafa’s lecture 10 Distributed Systems 0. -
Distributed Algorithms with Theoretic Scalability Analysis of Radial and Looped Load flows for Power Distribution Systems
Electric Power Systems Research 65 (2003) 169Á/177 www.elsevier.com/locate/epsr Distributed algorithms with theoretic scalability analysis of radial and looped load flows for power distribution systems Fangxing Li *, Robert P. Broadwater ECE Department Virginia Tech, Blacksburg, VA 24060, USA Received 15 April 2002; received in revised form 14 December 2002; accepted 16 December 2002 Abstract This paper presents distributed algorithms for both radial and looped load flows for unbalanced, multi-phase power distribution systems. The distributed algorithms are developed from tree-based sequential algorithms. Formulas of scalability for the distributed algorithms are presented. It is shown that computation time dominates communication time in the distributed computing model. This provides benefits to real-time load flow calculations, network reconfigurations, and optimization studies that rely on load flow calculations. Also, test results match the predictions of derived formulas. This shows the formulas can be used to predict the computation time when additional processors are involved. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Distributed computing; Scalability analysis; Radial load flow; Looped load flow; Power distribution systems 1. Introduction Also, the method presented in Ref. [10] was tested in radial distribution systems with no more than 528 buses. Parallel and distributed computing has been applied More recent works [11Á/14] presented distributed to many scientific and engineering computations such as implementations for power flows or power flow based weather forecasting and nuclear simulations [1,2]. It also algorithms like optimizations and contingency analysis. has been applied to power system analysis calculations These works also targeted power transmission systems. [3Á/14]. -
Cluster, Grid and Cloud Computing: a Detailed Comparison
The 6th International Conference on Computer Science & Education (ICCSE 2011) August 3-5, 2011. SuperStar Virgo, Singapore ThC 3.33 Cluster, Grid and Cloud Computing: A Detailed Comparison Naidila Sadashiv S. M Dilip Kumar Dept. of Computer Science and Engineering Dept. of Computer Science and Engineering Acharya Institute of Technology University Visvesvaraya College of Engineering (UVCE) Bangalore, India Bangalore, India [email protected] [email protected] Abstract—Cloud computing is rapidly growing as an alterna- with out any prior reservation and hence eliminates over- tive to conventional computing. However, it is based on models provisioning and improves resource utilization. like cluster computing, distributed computing, utility computing To the best of our knowledge, in the literature, only a few and grid computing in general. This paper presents an end-to- end comparison between Cluster Computing, Grid Computing comparisons have been appeared in the field of computing. and Cloud Computing, along with the challenges they face. This In this paper we bring out a complete comparison of the could help in better understanding these models and to know three computing models. Rest of the paper is organized as how they differ from its related concepts, all in one go. It also follows. The cluster computing, grid computing and cloud discusses the ongoing projects and different applications that use computing models are briefly explained in Section II. Issues these computing models as a platform for execution. An insight into some of the tools which can be used in the three computing and challenges related to these computing models are listed models to design and develop applications is given. -
Grid Computing: What Is It, and Why Do I Care?*
Grid Computing: What Is It, and Why Do I Care?* Ken MacInnis <[email protected]> * Or, “Mi caja es su caja!” (c) Ken MacInnis 2004 1 Outline Introduction and Motivation Examples Architecture, Components, Tools Lessons Learned and The Future Questions? (c) Ken MacInnis 2004 2 What is “grid computing”? Many different definitions: Utility computing Cycles for sale Distributed computing distributed.net RC5, SETI@Home High-performance resource sharing Clusters, storage, visualization, networking “We will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country.” Len Kleinrock (1969) The word “grid” doesn’t equal Grid Computing: Sun Grid Engine is a mere scheduler! (c) Ken MacInnis 2004 3 Better definitions: Common protocols allowing large problems to be solved in a distributed multi-resource multi-user environment. “A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.” Kesselman & Foster (1998) “…coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organizations.” Kesselman, Foster, Tuecke (2000) (c) Ken MacInnis 2004 4 New Challenges for Computing Grid computing evolved out of a need to share resources Flexible, ever-changing “virtual organizations” High-energy physics, astronomy, more Differing site policies with common needs Disparate computing needs -
Cloud Computing Over Cluster, Grid Computing: a Comparative Analysis
Journal of Grid and Distributed Computing Volume 1, Issue 1, 2011, pp-01-04 Available online at: http://www.bioinfo.in/contents.php?id=92 Cloud Computing Over Cluster, Grid Computing: a Comparative Analysis 1Indu Gandotra, 2Pawanesh Abrol, 3 Pooja Gupta, 3Rohit Uppal and 3Sandeep Singh 1Department of MCA, MIET, Jammu 2Department of Computer Science & IT, Jammu Univ, Jammu 3Department of MCA, MIET, Jammu e-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract—There are dozens of definitions for cloud Virtualization is a technology that enables sharing of computing and through each definition we can get the cloud resources. Cloud computing platform can become different idea about what a cloud computing exacting is? more flexible, extensible and reusable by adopting the Cloud computing is not a very new concept because it is concept of service oriented architecture [5].We will not connected to grid computing paradigm whose concept came need to unwrap the shrink wrapped software and install. into existence thirteen years ago. Cloud computing is not only related to Grid Computing but also to Utility computing The cloud is really very easier, just to install single as well as Cluster computing. Cloud computing is a software in the centralized facility and cover all the computing platform for sharing resources that include requirements of the company’s users [1]. software’s, business process, infrastructures and applications. Cloud computing also relies on the technology II. CLUSTER COMPUTING of virtualization. In this paper, we will discuss about Grid computing, Cluster computing and Cloud computing i.e. -
“Grid Computing”
VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY A seminar report on “Grid Computing” Submitted by Nagaraj Baddi (2SD07CS402) 8th semester DEPARTMENT OF COMPUTER SCIENCE ENGINEERING 2009-10 1 VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE ENGINEERING CERTIFICATE Certified that the seminar work entitled “Grid Computing” is a bonafide work presented by Mr. Nagaraj.M.Baddi, bearing USN 2SD07CS402 in a partial fulfillment for the award of degree of Bachelor of Engineering in Computer Science Engineering of the Vishveshwaraiah Technological University Belgaum, during the year 2009-10. The seminar report has been approved as it satisfies the academic requirements with respect to seminar work presented for the Bachelor of Engineering Degree. Staff in charge H.O.D CSE (S. L. DESHPANDE) (S. M. JOSHI) Name: Nagaraj M. Baddi USN: 2SD07CS402 2 INDEX 1. Introduction 4 2. History 5 3. How Grid Computing Works 6 4. Related technologies 8 4.1 Cluster computing 8 4.2 Peer-to-peer computing 9 4.3 Internet computing 9 5. Grid Computing Logical Levels 10 5.1 Cluster Grid 10 5.2 Campus Grid 10 5.3 Global Grid 10 6. Grid Architecture 11 6.1 Grid fabric 11 6.2 Core Grid middleware 12 6.3 User-level Grid middleware 12 6.4 Grid applications and portals. 13 7. Grid Applications 13 7.1 Distributed supercomputing 13 7.2 High-throughput computing 14 7.3 On-demand computing 14 7.4 Data-intensive computing 14 7.5 Collaborative computing 15 8. Difference: Grid Computing vs Cloud Computing 15 9. -
Computer Systems Architecture
CS 352H: Computer Systems Architecture Topic 14: Multicores, Multiprocessors, and Clusters University of Texas at Austin CS352H - Computer Systems Architecture Fall 2009 Don Fussell Introduction Goal: connecting multiple computers to get higher performance Multiprocessors Scalability, availability, power efficiency Job-level (process-level) parallelism High throughput for independent jobs Parallel processing program Single program run on multiple processors Multicore microprocessors Chips with multiple processors (cores) University of Texas at Austin CS352H - Computer Systems Architecture Fall 2009 Don Fussell 2 Hardware and Software Hardware Serial: e.g., Pentium 4 Parallel: e.g., quad-core Xeon e5345 Software Sequential: e.g., matrix multiplication Concurrent: e.g., operating system Sequential/concurrent software can run on serial/parallel hardware Challenge: making effective use of parallel hardware University of Texas at Austin CS352H - Computer Systems Architecture Fall 2009 Don Fussell 3 What We’ve Already Covered §2.11: Parallelism and Instructions Synchronization §3.6: Parallelism and Computer Arithmetic Associativity §4.10: Parallelism and Advanced Instruction-Level Parallelism §5.8: Parallelism and Memory Hierarchies Cache Coherence §6.9: Parallelism and I/O: Redundant Arrays of Inexpensive Disks University of Texas at Austin CS352H - Computer Systems Architecture Fall 2009 Don Fussell 4 Parallel Programming Parallel software is the problem Need to get significant performance improvement Otherwise, just use a faster uniprocessor, -
Strategies for Managing Business Disruption Due to Grid Computing
Strategies for managing business disruption due to Grid Computing by Vidyadhar Phalke Ph.D. Computer Science, Rutgers University, New Jersey, 1995 M.S. Computer Science, Rutgers University, New Jersey, 1992 B.Tech. Computer Science, Indian Institute of Technology, Delhi, 1989 Submitted to the MIT Sloan School of Management in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Management of Technology at the Massachusetts Institute of Technology June 2003 © 2003 Vidyadhar Phalke All Rights Reserved The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part Signature of Author: MIT Sloan School of Management 9 May 2003 Certified By: Starling D. Hunter III Theodore T. Miller Career Development Assistant Professor Thesis Supervisor Accepted By: David A. Weber Director, Management of Technology Program 2 Strategies for managing business disruption due to Grid Computing by Vidyadhar Phalke Submitted to the MIT Sloan School of Management on May 9 2003 in partial fulfillment of the requirements for the degree of Master of Science in the Management of Technology ABSTRACT In the technology centric businesses disruptive technologies displace incumbents time and again, sometimes to the extent that incumbents go bankrupt. In this thesis we would address the issue of what strategies are essential to prepare for and to manage disruptions for the affected businesses and industries. Specifically we will look at grid computing that is poised to disrupt (1) certain Enterprise IT departments, and (2) the software industry in the high-performance and web services space. -
Taxonomy of Flynn (1966)
Taxonomy of Flynn (1966). To describe these non-von Neumann or parallel architectures, a generally accepted taxonomy is that of Flynn (1966). The classification is based on the notion of two streams of information flow to a processor: instructions and data. These two streams can be either single or multiple, giving four classes of machines: 1. Single instruction single data (SISD) 2. Single instruction multiple data (SIMD) 3. Multiple instruction single data (MISD) 4. Multiple instruction multiple data (MIMD) Table 5.4 shows the four primary classes and some of the architectures that fit in those classes. Most of these architectures will be briefly discussed. 5.4.2.1 Single Instruction Single Data The SISD architectures encompass standard serial von Neumann architecture computers. In a sense, the SISD category is the base metric for Flynn’s taxonomy. 5.4.2.2 Single Instruction Multiple Data The SIMD computers are essentially array processors. This type of parallel computer architecture has n-processors, each executing the same instruction, but on different data streams. Often each element in the array can only communicate with its nearest neighbour. Computer architectures that are usually classified as SIMD are the systolic and wave-front array computers. In both types of processor, each processing element executes the same (and only) instruction, but on different data. Hence these architectures are SIMD. SIMD machines are widely used for such imaging computation as matrix arithmetic and convolution . 5.4.2.3 Multiple Instruction Single Data The MISD computer architecture lends itself naturally to those computations requiring an input to be subjected to several operations, each receiving the input in its original form. -
Computing at Massive Scale: Scalability and Dependability Challenges
This is a repository copy of Computing at massive scale: Scalability and dependability challenges. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/105671/ Version: Accepted Version Proceedings Paper: Yang, R and Xu, J orcid.org/0000-0002-4598-167X (2016) Computing at massive scale: Scalability and dependability challenges. In: Proceedings - 2016 IEEE Symposium on Service-Oriented System Engineering, SOSE 2016. 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), 29 Mar - 02 Apr 2016, Oxford, United Kingdom. IEEE , pp. 386-397. ISBN 9781509022533 https://doi.org/10.1109/SOSE.2016.73 (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. -
Computer Architectures
Parallel (High-Performance) Computer Architectures Tarek El-Ghazawi Department of Electrical and Computer Engineering The George Washington University Tarek El-Ghazawi, Introduction to High-Performance Computing slide 1 Introduction to Parallel Computing Systems Outline Definitions and Conceptual Classifications » Parallel Processing, MPP’s, and Related Terms » Flynn’s Classification of Computer Architectures Operational Models for Parallel Computers Interconnection Networks MPP’s Performance Tarek El-Ghazawi, Introduction to High-Performance Computing slide 2 Definitions and Conceptual Classification What is Parallel Processing? - A form of data processing which emphasizes the exploration and exploitation of inherent parallelism in the underlying problem. Other related terms » Massively Parallel Processors » Heterogeneous Processing – In the1990s, heterogeneous workstations from different processor vendors – Now, accelerators such as GPUs, FPGAs, Intel’s Xeon Phi, … » Grid computing » Cloud Computing Tarek El-Ghazawi, Introduction to High-Performance Computing slide 3 Definitions and Conceptual Classification Why Massively Parallel Processors (MPPs)? » Increase processing speed and memory allowing studies of problems with higher resolutions or bigger sizes » Provide a low cost alternative to using expensive processor and memory technologies (as in traditional vector machines) Tarek El-Ghazawi, Introduction to High-Performance Computing slide 4 Stored Program Computer The IAS machine was the first electronic computer developed, under -
Lecture 18: Parallel and Distributed Systems
Lecture 18: Parallel and What is a distributed system? Distributed Systems n From various textbooks: u “A distributed system is a collection of independent computers n Classification of parallel/distributed that appear to the users of the system as a single computer.” architectures u “A distributed system consists of a collection of autonomous n SMPs computers linked to a computer network and equipped with n Distributed systems distributed system software.” u n Clusters “A distributed system is a collection of processors that do not share memory or a clock.” u “Distributed systems is a term used to define a wide range of computer systems from a weakly-coupled system such as wide area networks, to very strongly coupled systems such as multiprocessor systems.” 1 2 What is a distributed system?(cont.) What is a distributed system?(cont.) n Michael Flynn (1966) P1 P2 P3 u n A distributed system is Network SISD — single instruction, single data u SIMD — single instruction, multiple data a set of physically separate u processors connected by MISD — multiple instruction, single data u MIMD — multiple instruction, multiple data one or more P4 P5 communication links n More recent (Stallings, 1993) n Is every system with >2 computers a distributed system?? u Email, ftp, telnet, world-wide-web u Network printer access, network file access, network file backup u We don’t usually consider these to be distributed systems… 3 4 MIMD Classification parallel and distributed Classification of the OS of MIMD computers Architectures tightly loosely coupled