USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES

Size: px
Start display at page:

Download "USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES"

Transcription

1 USING VIRTUAL MACHINE REPLICATION FOR DYNAMIC CONFIGURATION OF MULTI-TIER INTERNET SERVICES Carlos Oliveira, Vinicius Petrucci, Orlando Loques Universidade Federal Fluminense Niterói, Brazil ABSTRACT In recent years, several Internet services have been developed targeting people that spend most of their time online performing many different activities, such as browsing the Web. Typically, those services in the Web, including e-commerce sites and social networks, adopt a multi-tier design consisting of application and database tiers. In practice, the high workload demands for such Internet services cannot be supported by a single physical machine. To address this issue, we present an implementation of a virtualized cluster architecture that includes mechanisms for managing both application and database tiers. The overall cluster performance is guaranteed by dynamic resource allocation. Specifically, we make use of replication mechanisms in the multiple tiers of the server system. This, in turn, allows a given Internet service workload to be expanded or contracted dynamically across multiple different physical machines. Experimental results using our implemented architecture show that Internet workloads can be effectively replicated and balanced among different servers and the time-varying workloads of different Internet services can be supported accordingly in the server cluster. KEYWORDS Multi-tier Internet applications; Virtual machine replication. 1. INTRODUCTION In recent years, we have seen an increase in demand for web content and services. As a result, several Internet services have been developed targeting those people. Most of those services adopt a multi-tier design, consisting of application and database tiers, as found in server systems such as e-commerce sites and social networks. In order to respond to such high demand, web content and service providers have built large data centers to support many different Internet services. Although there are high peak demands, the average server utilization remains low in a typical server system. For instance, the average resource utilization in traditional data centers range between 5-20% [J. Heo et al., 2009]. This leads to a tremendous waste in terms of capital employed and energy used. To increase resources utilization, server virtualization has been widely adopted in data centers around the world. Basically, virtualization allows physical servers to run multiple independent services, where each service runs on the top of a Virtual Machine (VM). A physical server can run one or more VMs that dynamically share the underlying hardware resources leading to an improved usage of the available resources. In this work we present an implementation of a typical virtualized server architecture where both application and database tiers are implemented using virtual machines (VMs). To manage the Internet services in our architecture, a layer of proxies is interposed between clients and servers to intercept requests generated by the web clients. These proxies are used to permit the load balancing in the different tiers. The overall server performance is guaranteed by a dynamic resource allocation. That is, as we replicate and consolidate VMs, both application and database tiers can be executed by different physical machines.

2 2. ARCHITECTURE AND IMPLEMENTATION We propose an implementation of a server cluster architecture that takes advantage of Apache proxy [The Apache Software Foundation, 2010a] and MySQL proxy [MySQL, 2010a] capabilities. Our architecture aims to provide mechanisms for replicating multi-tier Internet services in a cluster. This means that application and database tiers may have multiple services running on multiple physical servers. In practice, a running service can be a web server (Apache) or database server (MySQL). In our architecture, we consider that the services within application and database tiers are executed by means of VMs. The underlying idea is that these VMs can be dynamically added, removed or even dimensioned in the system. In other words, the available resources can be allocated/deallocated for both application and database tiers in the system. In the multi-tier architecture, a number of requests are generated by web clients and received by the frontend machine that directs it to the application proxy. Then, the request is sent to an application server. In our implementation, each application server runs on top of a VM containing a copy of a RuBiS PHP version [RUBiS, 2010], which simulates an ebay site. An application server does not directly access a database server. A database proxy is interposed between applications and databases servers running on top of VMs. Each database server contains a copy of a MySQL database that stores tables containing information about users and items available in the web site. This mean that the application server generates dynamic page content and query-language calls are intercepted by the database proxy that redirects it to a database server. The front-end layer runs both application and database proxies, performing low-latency functions such as request forwarding and load balancing. We assume that the physical machine in this layer has enough resources to perform its role and will not fail, although it could be replicated to scale and to achieve fault tolerance. We have implemented two specific modules in the front-end layer. The first, termed Monitor, constantly monitors the CPU utilization of the physical machines. The second, termed Manager, has a list of physical machines and VMs, and uses the information about CPU utilization, collected by the Monitor, to allocate/deallocate resources, i.e., to add or remove VMs, for both the application and database tiers. The Manager uses weights available in the Apache Proxy to balance the amount of work to be done by the servers in the application tier. In our implementation, the Manager acts as follows. When a physical machine achieves a given CPU utilization threshold, the Manager starts a new VM for the application or database, according to the bottlenecked tier. The Manager starts the VM in the physical machine that has the lowest CPU utilization in the cluster. If all active physical machines are overloaded, the Manager adds a new physical machine to the cluster. The deallocation occurs when the applications supported by the cluster can be supported by a smaller number of physical machines, maintaining the CPU utilization bellow a given threshold. At this point, it is also possible to use an optimization technique to adjust the capacity of individual VMs, as experimented with other works, e.g., [V.Petrucci et al., 2011]. 3. REPLICATION MECHANISMS 3.1 Replication in application tier In the application tier, we used the Apache HTTP Server [The Apache Software Foundation, 2010b] integrated with a module termed mod_proxy_balancer [The Apache Software Foundation, 2010b], which is an Apache extension for load balancing. The load-balancer runs in a front-end machine and redirects requests between the pool of VMs that act as back-end servers. Weights are attributed to each back-end server. To do this, Apache provides the variable lbfactor that is a normalized value representing their share of the amount of work to be done. When we need to replicate an application in a second back-end server, we start a VM replica at this server. At the moment this replica is ready for processing clients requests, its status on the load balancer manager is set

3 up to enable and it is attributed a weight for this VM replica. Note that the weight is incremented in a loop, in small units until it reaches the target weight. 3.2 Replication in database tier The replication in database tier is much more complex than in the application tier. In fact, there are several different database management systems. To simplify our task, we use the Master-Slave Replication [MySQL, 2010c], provided by MySQL. The Master-Slave Replication enables data from one MySQL database server (the master) to be replicated to one or more MySQL database servers (the slaves). Replication is based on the master server keeping track of all changes to its databases in its binary log. The binary log serves as a written record of all events that modify the database from the moment the server was started. Each slave that connects to the master requests a copy of the binary log. The slave also replays the events from the binary log that it receives. This has the effect of repeating the original changes just as they were made on the master. In this environment, all writes and updates must take place on the master server. Reads, however, may take place on one or more slaves. The load balancing in the database layer uses the MySQL Proxy, which is a module provided by MySQL that provides communication between one or more MySQL servers and one or more MySQL clients. Thus, it allows to direct applications requests to the proxy that schedules them between a pool of available application databases. The MySQL Proxy distributes connections from clients to each server. Distribution is handled by a count for the number of connections distributed to each server. New connections are automatically sent to the server with the lowest count. If MySQL Proxy identifies that the slave is lagging behind the master for its replication threads, then the slave is automatically taken out of the list of available servers. Work will therefore be distributed to other MySQL servers within the slave replication group. 4. EXPERIMENTAL EVALUATION To evaluate our architecture implementation, we have conducted an experiment aimed at managing a threetier (front-end/application/database) application using the RuBiS web benchmark [RUBiS, 2010]. The major goal is to use the proposed replication mechanisms to support a multi-tier application while handling a timevarying workload. In order to meet fair response time requirements, we shall perform VM replication before the physical machine saturates, dimensioned for playing safe as 300% of CPU utilization (the maximum value for utilization is 400% because we are using quad-core machines and each core represents 100%). This leaves an amount of 100% CPU capacity available to be used by the VM management domain (Dom0) on the physical servers during the replication activities. All machines share a NFS storage mounted in the front-end to store the VM images. 4.1 Workload generation and QoS measurement RuBiS is the application used in our experiment. Several recent research works took advantage of the RuBiS benchmark, e.g., authors in [Y. Chen et al., 2008] which used the tool to evaluate their proposal. However, one can notice that Rubis is restricted to a semi-static number of clients which can only be increased and decreased following a ramp up/down style during a given experiment. In order to add flexibility, we have extended the RuBiS benchmark tool allowing it to produce arbitrary and more realistic benchmarks for server systems. The QoS (quality-of-service) considered in this work is related to the response time from server side. The response time is defined by the difference between the time a response is generated and the moment the server has accepted the associated request. To obtain the response time for the web applications we have implemented a new Apache module that collects the time information between these two moments using predefined hooks provided by the Apache Module API [Nick Kew, 2007]. To smooth out high short-term

4 fluctuations in measurements readings, we have integrated a filter procedure in our Apache module based on a single exponential moving average [Engineeting Statistics Handbook, 2010]. 4.2 Management of Internet services We performed an experiment in which we vary the number of accesses to the web site leading to the necessity of expand or contract the service workload across multiple different physical machines to prevent degradation in the QoS perceived by the user. Specifically, in the experiment, we increased the workload in steps, thus resources were allocated to handle the incoming user requests. After the allocation of our four worker machines (Itaipu, Tucurui, Maxwell and Edison each of them an Intel Core i5 2.67GHz, 8GB RAM), the workload decreased in steps and resources were deallocated. Our goal is to use the replication mechanisms to scale the system maintaining the CPU utilization under a given threshold. It is shown, in Figure 1, the allocation of the physical machines, the number of clients and the response time during the experiment. The experiment starts with one load generator machine (Xingo - Intel Core i7 2.67GHz, 8GB RAM), one application VM (running on Itaipu) and one master database VM (running on Tucurui). At time=60s, the workload increased and Itaipu could not handle all requests to the application-tier. Thus, a new worker machine (Maxwell) was added to the cluster and the replication mechanism was used to start a VM replica for the application-tier and balance requests between the VMs running on Itaipu and Maxwell. To handle the increase in the amount of user requests, a slave database was started, at time=200s, on Maxwell since it had free CPU cycles. At this moment, all read requests were sent to the slave database while the write requests were sent to the master database, reducing the CPU utilization on the master database. To increase the number of client requests, at time=250s, we added a second load generator machine (Farad - Intel Core Duo 3.00GHz, 6GB RAM) and a new worker machine (Edison) to the cluster. Edison was used to run an application-tier VM and a slave database. Thus, at this moment, read requests were balanced between slave databases running on Maxwell and Edison. After the workload reach a peak of 52 clients at time around 320s, the workload decreased and resources were deallocated. The experiment finished at time=600s. We observed in this experiment that, using the replication mechanisms for both application and database tiers, a multi-tier Internet application can be supported by different physical servers while handling a timevarying workload. Note that for a large system, a single master can become a bottleneck. In the future, it would be interesting to change the MySQL Master-Slave replication to another database replication policy that supports multiple master databases. Figure 1: Physical machines allocation

5 5. RELATED WORK Mechanisms for resource allocation in virtualized environments are used by several recent works. [Y. Wang et al., 2010] use live migration between physical machines, which is a limitation when the application cannot be supported by a single machine. Other works replicate only state-less applications [D. Kusic et al., 2009], but most part of current web applications are content-aware applications. Some other proposals adopt replication mechanisms for a single tier of the application, such as [G. Soundararajan et al, 2006], that replicates only the database-tier. Unlike these works, our architecture includes mechanisms for managing both the application and database tiers. 6. CONCLUSION AND FUTURE WORK In this work, we have implemented a virtualized server architecture targeted for multi-tier Internet applications that includes mechanisms for replicating a multi-tier application across different physical servers in a cluster. Using these mechanisms, the workload can be balanced among the different servers in a cluster. For multi-tier applications we argue that it is necessary to apply mechanisms like these to permit the load balancing in the different tiers, in order to guarantee the overall server performance. As observed in [J. Heo et al., 2009], runtime re-allocation of memory among multiple VMs has not been widely studied. They found that as the memory allocation becomes smaller, the total CPU consumption goes up, mainly due to the extra paging activities by the guest OS. Thus, the investigation on how to dynamically allocate memory resources, poses a great challenge for future research. REFERENCES D. Kusic et al., Power and performance management of virtualized computing environments via lookahead control. Cluster Computing, Vol. 12, No. 1, pp Engineeting Statistics Handbook, Single Exponential Smoothing. G. Soundararajan et al, Database replication policies for dynamic content applications. EuroSys '06: Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems Leuven, Belgium, pp J. Heo et al., Memory overbooking and dynamic control of Xen virtual machines in consolidated environments. Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management. New York, USA, pp Y. Wang et al., Power Optimization with Performance Assurance for Multi-tier Applications in Virtualized Data Centers. IEEE International Conference on Parallel Processing. Los Alamitos, CA, USA, pp Y. Chen et al., Translating Service Level Objectives to lower level policies for multi-tier services. Cluster Computing, Vol. 11, No. 3, pp MySQL, 2010a. MySQL Proxy. MySQL, 2010c. MySQL 5.1 Reference Manual: Chapter 16. Replication. Nick Kew, The Apache Modules Book: Application Development with Apache. Prentice Hall, New Jersey, USA. RUBiS, RUBiS: Rice University Bidding System. The Apache Software Foundation, 2010a. Apache Module mod_proxy_balancer. The Apache Software Foundation, 2010b. Apache {HTTP} server version V.Petrucci et al., Optimized Management of Power and Performance for Virtualized Heterogeneous Server Clusters. 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid. California, USA.

Experimentation with virtual machine migration and replication mechanisms

Experimentation with virtual machine migration and replication mechanisms Fluminense Federal University CARLOS ROBERTO DE OLIVEIRA JUNIOR Experimentation with virtual machine migration and replication mechanisms NITEROI 2011 CARLOS ROBERTO DE OLIVEIRA JUNIOR Experimentation

More information

Two-Level Cooperation in Autonomic Cloud Resource Management

Two-Level Cooperation in Autonomic Cloud Resource Management Two-Level Cooperation in Autonomic Cloud Resource Management Giang Son Tran, Laurent Broto, and Daniel Hagimont ENSEEIHT University of Toulouse, Toulouse, France Email: {giang.tran, laurent.broto, daniel.hagimont}@enseeiht.fr

More information

Affinity Aware VM Colocation Mechanism for Cloud

Affinity Aware VM Colocation Mechanism for Cloud Affinity Aware VM Colocation Mechanism for Cloud Nilesh Pachorkar 1* and Rajesh Ingle 2 Received: 24-December-2014; Revised: 12-January-2015; Accepted: 12-January-2015 2014 ACCENTS Abstract The most of

More information

Dynamic Resource allocation in Cloud

Dynamic Resource allocation in Cloud Dynamic Resource allocation in Cloud ABSTRACT: Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from

More information

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902 Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited

More information

Dynamic Load Balancing of Virtual Machines using QEMU-KVM

Dynamic Load Balancing of Virtual Machines using QEMU-KVM Dynamic Load Balancing of Virtual Machines using QEMU-KVM Akshay Chandak Krishnakant Jaju Technology, College of Engineering, Pune. Maharashtra, India. Akshay Kanfade Pushkar Lohiya Technology, College

More information

A dynamic optimization model for power and performance management of virtualized clusters

A dynamic optimization model for power and performance management of virtualized clusters A dynamic optimization model for power and performance management of virtualized clusters Vinicius Petrucci, Orlando Loques Univ. Federal Fluminense Niteroi, Rio de Janeiro, Brasil Daniel Mossé Univ. of

More information

An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform

An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform A B M Moniruzzaman 1, Kawser Wazed Nafi 2, Prof. Syed Akhter Hossain 1 and Prof. M. M. A. Hashem 1 Department

More information

Elastic VM for Rapid and Optimum Virtualized

Elastic VM for Rapid and Optimum Virtualized Elastic VM for Rapid and Optimum Virtualized Resources Allocation Wesam Dawoud PhD. Student Hasso Plattner Institute Potsdam, Germany 5th International DMTF Academic Alliance Workshop on Systems and Virtualization

More information

Run-time Resource Management in SOA Virtualized Environments. Danilo Ardagna, Raffaela Mirandola, Marco Trubian, Li Zhang

Run-time Resource Management in SOA Virtualized Environments. Danilo Ardagna, Raffaela Mirandola, Marco Trubian, Li Zhang Run-time Resource Management in SOA Virtualized Environments Danilo Ardagna, Raffaela Mirandola, Marco Trubian, Li Zhang Amsterdam, August 25 2009 SOI Run-time Management 2 SOI=SOA + virtualization Goal:

More information

IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures

IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Introduction

More information

Efficient Data Management Support for Virtualized Service Providers

Efficient Data Management Support for Virtualized Service Providers Efficient Data Management Support for Virtualized Service Providers Íñigo Goiri, Ferran Julià and Jordi Guitart Barcelona Supercomputing Center - Technical University of Catalonia Jordi Girona 31, 834

More information

Database Replication Policies for Dynamic Content Applications

Database Replication Policies for Dynamic Content Applications Database Replication Policies for Dynamic Content Applications Gokul Soundararajan, Cristiana Amza, Ashvin Goel Department of Electrical and Computer Engineering University of Toronto Toronto, Canada ABSTRACT

More information

Parallels VDI Solution

Parallels VDI Solution Parallels VDI Solution White Paper VDI SIZING A Competitive Comparison of VDI Solution Sizing between Parallels VDI versus VMware VDI www.parallels.com Parallels VDI Sizing. 29 Table of Contents Overview...

More information

Figure 1. The cloud scales: Amazon EC2 growth [2].

Figure 1. The cloud scales: Amazon EC2 growth [2]. - Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues

More information

Cloud Based Application Architectures using Smart Computing

Cloud Based Application Architectures using Smart Computing Cloud Based Application Architectures using Smart Computing How to Use this Guide Joyent Smart Technology represents a sophisticated evolution in cloud computing infrastructure. Most cloud computing products

More information

Auto-Scaling Model for Cloud Computing System

Auto-Scaling Model for Cloud Computing System Auto-Scaling Model for Cloud Computing System Che-Lun Hung 1*, Yu-Chen Hu 2 and Kuan-Ching Li 3 1 Dept. of Computer Science & Communication Engineering, Providence University 2 Dept. of Computer Science

More information

INCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD

INCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD INCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD M.Rajeswari 1, M.Savuri Raja 2, M.Suganthy 3 1 Master of Technology, Department of Computer Science & Engineering, Dr. S.J.S Paul Memorial

More information

Cloud and Virtualization to Support Grid Infrastructures

Cloud and Virtualization to Support Grid Infrastructures ESAC GRID Workshop '08 ESAC, Villafranca del Castillo, Spain 11-12 December 2008 Cloud and Virtualization to Support Grid Infrastructures Distributed Systems Architecture Research Group Universidad Complutense

More information

Autonomic resource management for the Xen Hypervisor

Autonomic resource management for the Xen Hypervisor Autonomic resource management for the Xen Hypervisor Íñigo Goiri and Jordi Guitart Universitat Politécnica de Catalunya Barcelona, Spain {igoiri,jguitart}@ac.upc.es Abstract Servers workload varies during

More information

Integration of Virtualized Workernodes in Batch Queueing Systems The ViBatch Concept

Integration of Virtualized Workernodes in Batch Queueing Systems The ViBatch Concept Integration of Virtualized Workernodes in Batch Queueing Systems, Dr. Armin Scheurer, Oliver Oberst, Prof. Günter Quast INSTITUT FÜR EXPERIMENTELLE KERNPHYSIK FAKULTÄT FÜR PHYSIK KIT University of the

More information

Final Project Proposal. CSCI.6500 Distributed Computing over the Internet

Final Project Proposal. CSCI.6500 Distributed Computing over the Internet Final Project Proposal CSCI.6500 Distributed Computing over the Internet Qingling Wang 660795696 1. Purpose Implement an application layer on Hybrid Grid Cloud Infrastructure to automatically or at least

More information

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing Liang-Teh Lee, Kang-Yuan Liu, Hui-Yang Huang and Chia-Ying Tseng Department of Computer Science and Engineering,

More information

Group Based Load Balancing Algorithm in Cloud Computing Virtualization

Group Based Load Balancing Algorithm in Cloud Computing Virtualization Group Based Load Balancing Algorithm in Cloud Computing Virtualization Rishi Bhardwaj, 2 Sangeeta Mittal, Student, 2 Assistant Professor, Department of Computer Science, Jaypee Institute of Information

More information

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing Deep Mann ME (Software Engineering) Computer Science and Engineering Department Thapar University Patiala-147004

More information

EWeb: Highly Scalable Client Transparent Fault Tolerant System for Cloud based Web Applications

EWeb: Highly Scalable Client Transparent Fault Tolerant System for Cloud based Web Applications ECE6102 Dependable Distribute Systems, Fall2010 EWeb: Highly Scalable Client Transparent Fault Tolerant System for Cloud based Web Applications Deepal Jayasinghe, Hyojun Kim, Mohammad M. Hossain, Ali Payani

More information

Black-box and Gray-box Strategies for Virtual Machine Migration

Black-box and Gray-box Strategies for Virtual Machine Migration Black-box and Gray-box Strategies for Virtual Machine Migration Wood, et al (UMass), NSDI07 Context: Virtual Machine Migration 1 Introduction Want agility in server farms to reallocate resources devoted

More information

Full and Para Virtualization

Full and Para Virtualization Full and Para Virtualization Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF x86 Hardware Virtualization The x86 architecture offers four levels

More information

Dynamic resource management for energy saving in the cloud computing environment

Dynamic resource management for energy saving in the cloud computing environment Dynamic resource management for energy saving in the cloud computing environment Liang-Teh Lee, Kang-Yuan Liu, and Hui-Yang Huang Department of Computer Science and Engineering, Tatung University, Taiwan

More information

Virtualization Technology using Virtual Machines for Cloud Computing

Virtualization Technology using Virtual Machines for Cloud Computing International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Virtualization Technology using Virtual Machines for Cloud Computing T. Kamalakar Raju 1, A. Lavanya 2, Dr. M. Rajanikanth 2 1,

More information

Virtual Machine Monitors. Dr. Marc E. Fiuczynski Research Scholar Princeton University

Virtual Machine Monitors. Dr. Marc E. Fiuczynski Research Scholar Princeton University Virtual Machine Monitors Dr. Marc E. Fiuczynski Research Scholar Princeton University Introduction Have been around since 1960 s on mainframes used for multitasking Good example VM/370 Have resurfaced

More information

Cloud Storage. Parallels. Performance Benchmark Results. White Paper. www.parallels.com

Cloud Storage. Parallels. Performance Benchmark Results. White Paper. www.parallels.com Parallels Cloud Storage White Paper Performance Benchmark Results www.parallels.com Table of Contents Executive Summary... 3 Architecture Overview... 3 Key Features... 4 No Special Hardware Requirements...

More information

Basics of Virtualisation

Basics of Virtualisation Basics of Virtualisation Volker Büge Institut für Experimentelle Kernphysik Universität Karlsruhe Die Kooperation von The x86 Architecture Why do we need virtualisation? x86 based operating systems are

More information

Performance brief for IBM WebSphere Application Server 7.0 with VMware ESX 4.0 on HP ProLiant DL380 G6 server

Performance brief for IBM WebSphere Application Server 7.0 with VMware ESX 4.0 on HP ProLiant DL380 G6 server Performance brief for IBM WebSphere Application Server.0 with VMware ESX.0 on HP ProLiant DL0 G server Table of contents Executive summary... WebSphere test configuration... Server information... WebSphere

More information

Virtualization Technologies and Blackboard: The Future of Blackboard Software on Multi-Core Technologies

Virtualization Technologies and Blackboard: The Future of Blackboard Software on Multi-Core Technologies Virtualization Technologies and Blackboard: The Future of Blackboard Software on Multi-Core Technologies Kurt Klemperer, Principal System Performance Engineer kklemperer@blackboard.com Agenda Session Length:

More information

Load Balancing for Improved Quality of Service in the Cloud

Load Balancing for Improved Quality of Service in the Cloud Load Balancing for Improved Quality of Service in the Cloud AMAL ZAOUCH Mathématique informatique et traitement de l information Faculté des Sciences Ben M SIK CASABLANCA, MORROCO FAOUZIA BENABBOU Mathématique

More information

GUEST OPERATING SYSTEM BASED PERFORMANCE COMPARISON OF VMWARE AND XEN HYPERVISOR

GUEST OPERATING SYSTEM BASED PERFORMANCE COMPARISON OF VMWARE AND XEN HYPERVISOR GUEST OPERATING SYSTEM BASED PERFORMANCE COMPARISON OF VMWARE AND XEN HYPERVISOR ANKIT KUMAR, SAVITA SHIWANI 1 M. Tech Scholar, Software Engineering, Suresh Gyan Vihar University, Rajasthan, India, Email:

More information

3.1 IaaS Definition. IaaS: Infrastructure as a Service

3.1 IaaS Definition. IaaS: Infrastructure as a Service 1 3.1 IaaS Definition IaaS: Infrastructure as a Service Through the internet, provide IT server, storage, computing power and other infrastructure capacity to the end users and the service fee based on

More information

The Mainframe Virtualization Advantage: How to Save Over Million Dollars Using an IBM System z as a Linux Cloud Server

The Mainframe Virtualization Advantage: How to Save Over Million Dollars Using an IBM System z as a Linux Cloud Server Research Report The Mainframe Virtualization Advantage: How to Save Over Million Dollars Using an IBM System z as a Linux Cloud Server Executive Summary Information technology (IT) executives should be

More information

Virtual Technologies for Learning System. Chao-Wen Chan, Chih-Min Chen. National Taichung University of Science and Technology, Taiwan

Virtual Technologies for Learning System. Chao-Wen Chan, Chih-Min Chen. National Taichung University of Science and Technology, Taiwan Virtual Technologies for Learning System Chao-Wen Chan, Chih-Min Chen 0274 National Taichung University of Science and Technology, Taiwan The Asian Conference on Technology in the Classroom 2012 2012 Abstract:

More information

Cloud Computing Simulation Using CloudSim

Cloud Computing Simulation Using CloudSim Cloud Computing Simulation Using CloudSim Ranjan Kumar #1, G.Sahoo *2 # Assistant Professor, Computer Science & Engineering, Ranchi University, India Professor & Head, Information Technology, Birla Institute

More information

Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform

Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform Benchmarking the Performance of XenDesktop Virtual DeskTop Infrastructure (VDI) Platform Shie-Yuan Wang Department of Computer Science National Chiao Tung University, Taiwan Email: shieyuan@cs.nctu.edu.tw

More information

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing

Keywords Distributed Computing, On Demand Resources, Cloud Computing, Virtualization, Server Consolidation, Load Balancing Volume 5, Issue 1, January 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Survey on Load

More information

Virtual Machine Synchronization for High Availability Clusters

Virtual Machine Synchronization for High Availability Clusters Virtual Machine Synchronization for High Availability Clusters Yoshiaki Tamura, Koji Sato, Seiji Kihara, Satoshi Moriai NTT Cyber Space Labs. 2007/4/17 Consolidating servers using VM Internet services

More information

Distributed and Cloud Computing

Distributed and Cloud Computing Distributed and Cloud Computing K. Hwang, G. Fox and J. Dongarra Chapter 3: Virtual Machines and Virtualization of Clusters and datacenters Adapted from Kai Hwang University of Southern California March

More information

Virtualization for Cloud Computing

Virtualization for Cloud Computing Virtualization for Cloud Computing Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF CLOUD COMPUTING On demand provision of computational resources

More information

New resource provision paradigms for Grid Infrastructures: Virtualization and Cloud

New resource provision paradigms for Grid Infrastructures: Virtualization and Cloud CISCO NerdLunch Series November 7, 2008 San Jose, CA New resource provision paradigms for Grid Infrastructures: Virtualization and Cloud Ruben Santiago Montero Distributed Systems Architecture Research

More information

A Middleware Strategy to Survive Compute Peak Loads in Cloud

A Middleware Strategy to Survive Compute Peak Loads in Cloud A Middleware Strategy to Survive Compute Peak Loads in Cloud Sasko Ristov Ss. Cyril and Methodius University Faculty of Information Sciences and Computer Engineering Skopje, Macedonia Email: sashko.ristov@finki.ukim.mk

More information

PARALLELS CLOUD SERVER

PARALLELS CLOUD SERVER PARALLELS CLOUD SERVER Performance and Scalability 1 Table of Contents Executive Summary... Error! Bookmark not defined. LAMP Stack Performance Evaluation... Error! Bookmark not defined. Background...

More information

Energy Conscious Virtual Machine Migration by Job Shop Scheduling Algorithm

Energy Conscious Virtual Machine Migration by Job Shop Scheduling Algorithm Energy Conscious Virtual Machine Migration by Job Shop Scheduling Algorithm Shanthipriya.M 1, S.T.Munusamy 2 ProfSrinivasan. R 3 M.Tech (IT) Student, Department of IT, PSV College of Engg & Tech, Krishnagiri,

More information

Efficient Load Balancing using VM Migration by QEMU-KVM

Efficient Load Balancing using VM Migration by QEMU-KVM International Journal of Computer Science and Telecommunications [Volume 5, Issue 8, August 2014] 49 ISSN 2047-3338 Efficient Load Balancing using VM Migration by QEMU-KVM Sharang Telkikar 1, Shreyas Talele

More information

Mobile Cloud Computing T-110.5121 Open Source IaaS

Mobile Cloud Computing T-110.5121 Open Source IaaS Mobile Cloud Computing T-110.5121 Open Source IaaS Tommi Mäkelä, Otaniemi Evolution Mainframe Centralized computation and storage, thin clients Dedicated hardware, software, experienced staff High capital

More information

Application Performance in the Cloud, and its Relationship to QoS

Application Performance in the Cloud, and its Relationship to QoS Application Performance in the Cloud, and its Relationship to QoS Fall 2010 First, Last Someone@my.csun.edu Student ID: xxxxxxxxx September 22, 2010 Committee Chair: Dr. Shan Barkataki Approved by: Committee

More information

An Introduction to Virtualization and Cloud Technologies to Support Grid Computing

An Introduction to Virtualization and Cloud Technologies to Support Grid Computing New Paradigms: Clouds, Virtualization and Co. EGEE08, Istanbul, September 25, 2008 An Introduction to Virtualization and Cloud Technologies to Support Grid Computing Distributed Systems Architecture Research

More information

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM Akmal Basha 1 Krishna Sagar 2 1 PG Student,Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, India. 2 Associate

More information

COM 444 Cloud Computing

COM 444 Cloud Computing COM 444 Cloud Computing Lec 3: Virtual Machines and Virtualization of Clusters and Datacenters Prof. Dr. Halûk Gümüşkaya haluk.gumuskaya@gediz.edu.tr haluk@gumuskaya.com http://www.gumuskaya.com Virtual

More information

Migration of Virtual Machines for Better Performance in Cloud Computing Environment

Migration of Virtual Machines for Better Performance in Cloud Computing Environment Migration of Virtual Machines for Better Performance in Cloud Computing Environment J.Sreekanth 1, B.Santhosh Kumar 2 PG Scholar, Dept. of CSE, G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh,

More information

Enhancing the Performance of Live Migration of Virtual Machine s with WSClock Replacement Algorithm

Enhancing the Performance of Live Migration of Virtual Machine s with WSClock Replacement Algorithm Enhancing the Performance of Live Migration of Virtual Machine s with WSClock Replacement Algorithm C.Sagana M.Geetha Dr R.C.Suganthe PG student, Assistant Professor, Professor, Dept of CSE, Dept of CSE

More information

PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE

PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE PERFORMANCE ANALYSIS OF KERNEL-BASED VIRTUAL MACHINE Sudha M 1, Harish G M 2, Nandan A 3, Usha J 4 1 Department of MCA, R V College of Engineering, Bangalore : 560059, India sudha.mooki@gmail.com 2 Department

More information

Serving 4 million page requests an hour with Magento Enterprise

Serving 4 million page requests an hour with Magento Enterprise 1 Serving 4 million page requests an hour with Magento Enterprise Introduction In order to better understand Magento Enterprise s capacity to serve the needs of some of our larger clients, Session Digital

More information

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Pooja.B. Jewargi Prof. Jyoti.Patil Department of computer science and engineering,

More information

An Architecture Model of Sensor Information System Based on Cloud Computing

An Architecture Model of Sensor Information System Based on Cloud Computing An Architecture Model of Sensor Information System Based on Cloud Computing Pengfei You, Yuxing Peng National Key Laboratory for Parallel and Distributed Processing, School of Computer Science, National

More information

A probabilistic multi-tenant model for virtual machine mapping in cloud systems

A probabilistic multi-tenant model for virtual machine mapping in cloud systems A probabilistic multi-tenant model for virtual machine mapping in cloud systems Zhuoyao Wang, Majeed M. Hayat, Nasir Ghani, and Khaled B. Shaban Department of Electrical and Computer Engineering, University

More information

CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms

CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose,

More information

Enabling Technologies for Distributed Computing

Enabling Technologies for Distributed Computing Enabling Technologies for Distributed Computing Dr. Sanjay P. Ahuja, Ph.D. Fidelity National Financial Distinguished Professor of CIS School of Computing, UNF Multi-core CPUs and Multithreading Technologies

More information

Virtualization. Dr. Yingwu Zhu

Virtualization. Dr. Yingwu Zhu Virtualization Dr. Yingwu Zhu What is virtualization? Virtualization allows one computer to do the job of multiple computers. Virtual environments let one computer host multiple operating systems at the

More information

ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND RESOURCE UTILIZATION IN CLOUD NETWORK

ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND RESOURCE UTILIZATION IN CLOUD NETWORK International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 1, Jan-Feb 2016, pp. 45-53, Article ID: IJCET_07_01_006 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=7&itype=1

More information

VIRTUALIZATION, The next step for online services

VIRTUALIZATION, The next step for online services Scientific Bulletin of the Petru Maior University of Tîrgu Mureş Vol. 10 (XXVII) no. 1, 2013 ISSN-L 1841-9267 (Print), ISSN 2285-438X (Online), ISSN 2286-3184 (CD-ROM) VIRTUALIZATION, The next step for

More information

Copyright www.agileload.com 1

Copyright www.agileload.com 1 Copyright www.agileload.com 1 INTRODUCTION Performance testing is a complex activity where dozens of factors contribute to its success and effective usage of all those factors is necessary to get the accurate

More information

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB Planet Size Data!? Gartner s 10 key IT trends for 2012 unstructured data will grow some 80% over the course of the next

More information

Performance Testing of a Cloud Service

Performance Testing of a Cloud Service Performance Testing of a Cloud Service Trilesh Bhurtun, Junior Consultant, Capacitas Ltd Capacitas 2012 1 Introduction Objectives Environment Tests and Results Issues Summary Agenda Capacitas 2012 2 1

More information

VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES

VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES U.P.B. Sci. Bull., Series C, Vol. 76, Iss. 2, 2014 ISSN 2286-3540 VIRTUAL RESOURCE MANAGEMENT FOR DATA INTENSIVE APPLICATIONS IN CLOUD INFRASTRUCTURES Elena Apostol 1, Valentin Cristea 2 Cloud computing

More information

8th WSEAS International Conference on SYSTEMS THEORY and SCIENTIFIC COMPUTATION (ISTASC 08) Rhodes, Greece, August 20-22, 2008

8th WSEAS International Conference on SYSTEMS THEORY and SCIENTIFIC COMPUTATION (ISTASC 08) Rhodes, Greece, August 20-22, 2008 Comparison of OS Level and Hypervisor Server Virtualization ABBAS ASOSHEH, MOHAMMAD HOSSEIN DANESH Information Technology Department Tarbiat Modares University & Amirkabir University of Technology Jalal

More information

bla bla OPEN-XCHANGE Open-Xchange Hardware Needs

bla bla OPEN-XCHANGE Open-Xchange Hardware Needs bla bla OPEN-XCHANGE Open-Xchange Hardware Needs OPEN-XCHANGE: Open-Xchange Hardware Needs Publication date Wednesday, 8 January version. . Hardware Needs with Open-Xchange.. Overview The purpose of this

More information

Server Virtualisation with VMware. Philipp Heckel, University of Mannheim, 4/12/08 1

Server Virtualisation with VMware. Philipp Heckel, University of Mannheim, 4/12/08 1 Server Virtualisation with VMware Philipp Heckel, University of Mannheim, 4/12/08 1 Virtualisation 1. Virtualisation Overview 2. Impact on Service Providers 3. Virtualisation Technology 4. VMware Infrastructure

More information

Cloud Computing Architecture with OpenNebula HPC Cloud Use Cases

Cloud Computing Architecture with OpenNebula HPC Cloud Use Cases NASA Ames NASA Advanced Supercomputing (NAS) Division California, May 24th, 2012 Cloud Computing Architecture with OpenNebula HPC Cloud Use Cases Ignacio M. Llorente Project Director OpenNebula Project.

More information

Elastic Load Balancing in Cloud Storage

Elastic Load Balancing in Cloud Storage Elastic Load Balancing in Cloud Storage Surabhi Jain, Deepak Sharma (Lecturer, Department of Computer Science, Lovely Professional University, Phagwara-144402) (Assistant Professor, Department of Computer

More information

Introduction to Windows Azure Cloud Computing Futures Group, Microsoft Research Roger Barga, Jared Jackson,Nelson Araujo, Dennis Gannon, Wei Lu, and

Introduction to Windows Azure Cloud Computing Futures Group, Microsoft Research Roger Barga, Jared Jackson,Nelson Araujo, Dennis Gannon, Wei Lu, and Introduction to Windows Azure Cloud Computing Futures Group, Microsoft Research Roger Barga, Jared Jackson,Nelson Araujo, Dennis Gannon, Wei Lu, and Jaliya Ekanayake Range in size from edge facilities

More information

Relational Databases in the Cloud

Relational Databases in the Cloud Contact Information: February 2011 zimory scale White Paper Relational Databases in the Cloud Target audience CIO/CTOs/Architects with medium to large IT installations looking to reduce IT costs by creating

More information

An Autonomic Auto-scaling Controller for Cloud Based Applications

An Autonomic Auto-scaling Controller for Cloud Based Applications An Autonomic Auto-scaling Controller for Cloud Based Applications Jorge M. Londoño-Peláez Escuela de Ingenierías Universidad Pontificia Bolivariana Medellín, Colombia Carlos A. Florez-Samur Netsac S.A.

More information

1. Simulation of load balancing in a cloud computing environment using OMNET

1. Simulation of load balancing in a cloud computing environment using OMNET Cloud Computing Cloud computing is a rapidly growing technology that allows users to share computer resources according to their need. It is expected that cloud computing will generate close to 13.8 million

More information

Cloud Computing through Virtualization and HPC technologies

Cloud Computing through Virtualization and HPC technologies Cloud Computing through Virtualization and HPC technologies William Lu, Ph.D. 1 Agenda Cloud Computing & HPC A Case of HPC Implementation Application Performance in VM Summary 2 Cloud Computing & HPC HPC

More information

An Oracle White Paper July 2011. Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide

An Oracle White Paper July 2011. Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide Oracle Primavera Contract Management, Business Intelligence Publisher Edition-Sizing Guide An Oracle White Paper July 2011 1 Disclaimer The following is intended to outline our general product direction.

More information

Virtualization. Jukka K. Nurminen 23.9.2015

Virtualization. Jukka K. Nurminen 23.9.2015 Virtualization Jukka K. Nurminen 23.9.2015 Virtualization Virtualization refers to the act of creating a virtual (rather than actual) version of something, including virtual computer hardware platforms,

More information

Computing in High- Energy-Physics: How Virtualization meets the Grid

Computing in High- Energy-Physics: How Virtualization meets the Grid Computing in High- Energy-Physics: How Virtualization meets the Grid Yves Kemp Institut für Experimentelle Kernphysik Universität Karlsruhe Yves Kemp Barcelona, 10/23/2006 Outline: Problems encountered

More information

LOGO Resource Management for Cloud Computing

LOGO Resource Management for Cloud Computing LOGO Resource Management for Cloud Computing Supervisor : Dr. Pham Tran Vu Presenters : Nguyen Viet Hung - 11070451 Tran Le Vinh - 11070487 Date : April 16, 2012 Contents Introduction to Cloud Computing

More information

PARALLELS CLOUD STORAGE

PARALLELS CLOUD STORAGE PARALLELS CLOUD STORAGE Performance Benchmark Results 1 Table of Contents Executive Summary... Error! Bookmark not defined. Architecture Overview... 3 Key Features... 5 No Special Hardware Requirements...

More information

Newsletter 4/2013 Oktober 2013. www.soug.ch

Newsletter 4/2013 Oktober 2013. www.soug.ch SWISS ORACLE US ER GRO UP www.soug.ch Newsletter 4/2013 Oktober 2013 Oracle 12c Consolidation Planer Data Redaction & Transparent Sensitive Data Protection Oracle Forms Migration Oracle 12c IDENTITY table

More information

Tuning Dynamic Web Applications using Fine-Grain Analysis

Tuning Dynamic Web Applications using Fine-Grain Analysis Tuning Dynamic Web Applications using Fine-Grain Analysis Jordi Guitart, David Carrera, Jordi Torres, Eduard Ayguadé and Jesús Labarta European Center for Parallelism of Barcelona (CEPBA) Computer Architecture

More information

Rackspace Cloud Databases and Container-based Virtualization

Rackspace Cloud Databases and Container-based Virtualization Rackspace Cloud Databases and Container-based Virtualization August 2012 J.R. Arredondo @jrarredondo Page 1 of 6 INTRODUCTION When Rackspace set out to build the Cloud Databases product, we asked many

More information

Optimization of QoS for Cloud-Based Services through Elasticity and Network Awareness

Optimization of QoS for Cloud-Based Services through Elasticity and Network Awareness Master Thesis: Optimization of QoS for Cloud-Based Services through Elasticity and Network Awareness Alexander Fedulov 1 Agenda BonFIRE Project overview Motivation General System Architecture Monitoring

More information

Economic Efficiency Control on Data Centre Resources in Heterogeneous Cost Scenarios

Economic Efficiency Control on Data Centre Resources in Heterogeneous Cost Scenarios Economic Efficiency Control on Data Centre Resources in Heterogeneous Cost Scenarios Benjamin Heckmann, Marcus Zinn, Ronald C. Moore, and Christoph Wentzel University of Applied Sciences Darmstadt Haardtring

More information

Windows Azure and private cloud

Windows Azure and private cloud Windows Azure and private cloud Joe Chou Senior Program Manager China Cloud Innovation Center Customer Advisory Team Microsoft Asia-Pacific Research and Development Group 1 Agenda Cloud Computing Fundamentals

More information

ABSTRACT. KEYWORDS: Cloud Computing, Load Balancing, Scheduling Algorithms, FCFS, Group-Based Scheduling Algorithm

ABSTRACT. KEYWORDS: Cloud Computing, Load Balancing, Scheduling Algorithms, FCFS, Group-Based Scheduling Algorithm A REVIEW OF THE LOAD BALANCING TECHNIQUES AT CLOUD SERVER Kiran Bala, Sahil Vashist, Rajwinder Singh, Gagandeep Singh Department of Computer Science & Engineering, Chandigarh Engineering College, Landran(Pb),

More information

Hyper-V R2 SP1 Application Workload Performance

Hyper-V R2 SP1 Application Workload Performance ESG Lab Summary Hyper-V R2 SP1 Application Workload Performance Virtualizing SQL Server, SharePoint, and Exchange with Confidence This report presents a summary of Microsoft-commissioned testing of the

More information

Managed Virtualized Platforms: From Multicore Nodes to Distributed Cloud Infrastructures

Managed Virtualized Platforms: From Multicore Nodes to Distributed Cloud Infrastructures Managed Virtualized Platforms: From Multicore Nodes to Distributed Cloud Infrastructures Ada Gavrilovska Karsten Schwan, Mukil Kesavan Sanjay Kumar, Ripal Nathuji, Adit Ranadive Center for Experimental

More information

Dell Compellent Storage Center SAN & VMware View 1,000 Desktop Reference Architecture. Dell Compellent Product Specialist Team

Dell Compellent Storage Center SAN & VMware View 1,000 Desktop Reference Architecture. Dell Compellent Product Specialist Team Dell Compellent Storage Center SAN & VMware View 1,000 Desktop Reference Architecture Dell Compellent Product Specialist Team THIS WHITE PAPER IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL

More information

Data Centers and Cloud Computing

Data Centers and Cloud Computing Data Centers and Cloud Computing CS377 Guest Lecture Tian Guo 1 Data Centers and Cloud Computing Intro. to Data centers Virtualization Basics Intro. to Cloud Computing Case Study: Amazon EC2 2 Data Centers

More information

Virtualization Infrastructure at Karlsruhe

Virtualization Infrastructure at Karlsruhe Virtualization Infrastructure at Karlsruhe HEPiX Fall 2007 Volker Buege 1),2), Ariel Garcia 1), Marcus Hardt 1), Fabian Kulla 1),Marcel Kunze 1), Oliver Oberst 1),2), Günter Quast 2), Christophe Saout

More information

Evaluation Methodology of Converged Cloud Environments

Evaluation Methodology of Converged Cloud Environments Krzysztof Zieliński Marcin Jarząb Sławomir Zieliński Karol Grzegorczyk Maciej Malawski Mariusz Zyśk Evaluation Methodology of Converged Cloud Environments Cloud Computing Cloud Computing enables convenient,

More information