BUNGEE An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments
|
|
|
- Adela Simmons
- 10 years ago
- Views:
Transcription
1 BUNGEE An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments Andreas Weber, Nikolas Herbst, Henning Groenda, Samuel Kounev Munich - November 5th, 2015 Symposium on Software Performance
2 Elasticity 2
3 Size Size Characteristics of... Rubber Bands Base Length IaaS Clouds Performance (1 resource unit) Width / Thickness / Force Quality Criteria / SLA Contract Contract Contract Strechability Scalability Elasticity Elasticity Time Time 3
4 4 Comparing Elastic Behavior of... Rubber Bands IaaS Clouds 2 cm Measure Elasticity Independent of demand supply time 2 cm 4 cm Performance and Scalability demand supply time
5 Motivation & Related Work 5
![Industry Academia [Gartner09] [Galante12, Jannings14] Benchmark for](/docs-images/27/9690317/images/6-0.png "comparability! You can t control what you can t measure?")
6 6 Why measure Elasticity? Elasticity Major quality attribute of clouds Many strategies exist Industry Academia [Gartner09] [Galante12, Jannings14] Benchmark for comparability! You can t control what you can t measure? (DeMarco) If you cannot measure it, you cannot improve it (Lord Kelvin)
7 7 Related Work: Elasticity Benchmarking Approaches Specialized approaches Measure technical provisioning time Measure SLA compliance Focus on scale up/out [Binning09, Li10, Dory11, Almeida13] Business perspective What is the financial impact? Disadvantage: Mix-up of elasticity technique and business model [Weimann11, Folkerts12, Islam12, Moldovan13, Tinnefeld14]
8 Concept & Implementation 8
9 9 Elasticity Benchmarking Concept System Analysis Analyze the performance of underlying resources & scaling behavior Benchmark Calibration Measurement Elasticity Evaluation
10 intensity # resources resource amount Analyze System Approach Evaluate system separately at each scale Find maximal load intensity that the system can withstand without violating SLOs (binary search) Derive demand step function: resourcedemand = f(intensity) f(intensity) Benefit Derive resource demand for arbitrary load intensity variations load intensity f(intensity) time time 10
11 11 Elasticity Benchmarking Concept System Analysis Analyze the performance of underlying resources & scaling behavior Benchmark Calibration Adjust load profile Measurement Elasticity Evaluation
12 f(intensity) resources intensity f(intensity) intensity resources 12 Benchmark Calibration time time demand time demand time Approach: Adjust load intensity profile to overcome Different performance of underlying resources Different scalability
13 13 Elasticity Benchmarking Concept System Analysis Analyze the performance of underlying resources & scaling behavior Benchmark Calibration Adjust load profile Measurement Expose cloud system to varying load & monitor resource supply & demand Elasticity Evaluation
14 14 Measurement Requirement: Stress SUT in a representative manner Realistic variability of load intensity Adaptability of load profiles to suit different domains Approach: Open workload model [Schroeder06] Model Load Variations with the LIMBO toolkit [SEAMS15Kistowski] Facilitates creation of new load profiles Derived from existing traces With desired properties (e.g. seasonal pattern, bursts) Execute load profile using JMeter Timer-Plugin delays requests according to timestamp file created by LIMBO
15 15 Elasticity Benchmarking Concept System Analysis Analyze the performance of underlying resources & scaling behavior Benchmark Calibration Adjust load profile Measurement Elasticity Evaluation Expose cloud system to varying load & monitor resource supply & demand Evaluate elasticity aspects accuracy & timing with metrics CloudStack
16 resources 16 Metrics: Accuracy (1/3) O 2 U 3 O 3 U 2 O 1 U 1 resource demand T resource supply accuracy U : T U accuracy O : T O
17 resources 17 Metrics: Timeshare (2/3) A 1 B 1 A 2 A 3 B 2 B 3 O 2 U 3 O 3 U 2 O 1 U 1 resource demand T resource supply timeshare U : T A timeshare O : T B
18 resources resources 18 Metrics: Jitter (3/3) resource demand resource supply resource demand resource supply jitter: E S E D T E D : # demand adaptations, E S : # supply adaptations
19 19 Elasticity Benchmarking Concept System Analysis Analyze the performance of underlying resources & scaling behavior Benchmark Calibration Adjust load profile Measurement Elasticity Evaluation Expose cloud system to varying load & monitor resource supply & demand Evaluate elasticity aspects accuracy & timing with metrics CloudStack
20 20 BUNGEE Implementation Java-based elasticity benchmarking framework Components Harness (Benchmark Node) Cloud-side load generation application (CSUT) Automates the four benchmarking activities Extensible with respect to new cloud management software new resource types new metrics CloudStack System Analysis Benchmark Calibration Measurement Elasticity Evaluation Analysis of horizontally scaling clouds based on CloudStack AWS Code is Open Source Quick Start Guide available
21 Evaluation & Case Study 21
22 22 Evaluation & Case Study Evaluation (private cloud) Reproducibility of system analysis Err rel < 5%, confidence 95% for first scaling stage Simplified system analysis Linearity assumption holds for test system Consistent ranking by metrics Separate evaluation for each metric, min. 4 configurations per metric Case Study (private & public cloud) Applicability in real scenario Different performance of underlying resources Metric Aggregation
23 23 Case Study: Configuration F - 1Core Configuration accuarcy O [res. units] accuracy U [res. units] timeshare O [%] timeshare U [%] jitter [adap/min.] elastic speedup violations [%] F 1Core
24 24 Case Study: Config. F - 2Core not adjusted Configuration accuarcy O [res. units] accuracy U [res. units] timeshare O [%] timeshare U [%] jitter [adap/min.] elastic speedup violations [%] F 1Core F 2Core no adjustment
25 25 Case Study: Config. F - 2Core adjusted Configuration accuarcy O [res. units] accuracy U [res. units] timeshare O [%] timeshare U [%] jitter [adap/min.] elastic speedup violations [%] F 1Core F 2Core no adjustment F 2Core adjusted
26 26 Conclusion Goal: Evaluate elastic behavior independent of Performance of underlying resources and scaling behavior Business model Contribution: Elasticity benchmark concept for IaaS cloud platforms Refined set of elasticity metrics Concept implementation: BUNGEE - framework for elasticity benchmarking Evaluation: Consistent ranking of elastic behavior by metrics Case study on AWS and CloudStack Future Work: BUNGEE: Distributed load generation, scale vertically, dif. resource types Experiments: Tuning of elasticity parameters, evaluate proactive controllers
27 27 Literature (1/2) Gartner09: D.C. Plume, D. M. Smith, T.J. Bittman, D.W. Cearley, D.J. Cappuccio, D. Scott, R. Kumar, and B. Robertson. Study: Five Refining Attributes of Public and Private Cloud Computing", Tech. rep., Gartner, Galante12: G. Galante and L. C. E. d. Bona, A Survey on Cloud Computing Elasticity" in Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing, Washington, 2012 Jennings14: B. Jennings and R. Stadler, Resource management in clouds: Survey and research challenges, Journal of Network and Systems Management, pp. 1-53, 2014 Binning09: C. Binnig, D. Kossmann, T. Kraska, and S. Loesing, How is the weather tomorrow?: towards a benchmark for the cloud" in Proceedings of the Second International Workshop on Testing Database Systems, 2009 Li10: A. Li, X. Yang, S. Kandula, and M. Zhang, CloudCmp: Comparing Public Cloud Providers" in Proceedings Dory11: of the 10th ACM SIGCOMM Conference on Internet Measurement, 2010 T. Dory, B. Mejías, P. V. Roy, and N.-L. Tran, Measuring Elasticity for Cloud Databases" in Proceedings of the The Second International Conference on Cloud Computing, GRIDs, and Virtualization, 2011 Almeida13: R.F. Almeida, F.R.C. Sousa, S. Lifschitz, and J.C. Machado: On defining metrics for elasticity of cloud databases, Simpósio Brasileiro de Banco de Dados - SBBD 2013, last consulted Nov Weimann11: J. Weinman, Time is Money: The Value of On-Demand, 2011, last consulted Nov. 2015
28 28 Literature (2/2) Islam12: S. Islam, K. Lee, A. Fekete, and A. Liu, How a consumer can measure elasticity for cloud platforms" in Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering, New York, 2012 Folkerts12: E. Folkerts, A. Alexandrov, K. Sachs, A. Iosup, V. Markl, and C. Tosun, Benchmarking in the Cloud: What It Should, Can, and Cannot Be in Selected Topics in Performance Evaluation and Benchmarking, Berlin Heidelberg, 2012 Moldovan13: D. Moldovan, G. Copil, H.-L. Truong, and S. Dustdar, MELA: Monitoring and Analyzing Elasticity of Cloud Services, in IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom), 2013 Tinnefeld14: C. Tinnefeld, D. Taschik, and H. Plattner, Quantifying the Elasticity of a Database Management System, in DBKDA 2014, The Sixth International Conference on Advances in Databases, Knowledge, and Data Applications, 2014 Schroeder06: B. Schroeder, A. Wierman, and M. Harchol-Balter, Open Versus Closed: A Cautionary Tale," in Proceedings of the 3rd Conference on Networked Systems Design & Implementation - Volume 3, ser. NSDI'06. Berkeley, CA, USA: USENIX Association, 2006 SEAMS15Kistowski: Jóakim von Kistowski, Nikolas Roman Herbst, Daniel Zoller, Samuel Kounev, and Andreas Hotho. Modeling and Extracting Load Intensity Profiles. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015), Firenze, Italy, May 18-19, Herbst13: N. R. Herbst, S. Kounev, and R. Reussner, Elasticity in Cloud Computing: What it is, and What it is Not" in Proceedings of the 10th International Conference on Autonomic Computing, San Jose, 2013
BUNGEE: An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments
BUNGEE: An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments Nikolas Roman Herbst and Samuel Kounev University of Würzburg Würzburg, Germany Email: {firstname.lastname}@uni-wuerzburg.de Andreas
On defining metrics for elasticity of cloud databases
On defining metrics for elasticity of cloud databases Rodrigo F. Almeida 1, Flávio R. C. Sousa 1, Sérgio Lifschitz 2 and Javam C. Machado 1 1 Universidade Federal do Ceará - Brasil [email protected],
BUNGEE Quick Start Guide for AWS EC2 based elastic clouds
BUNGEE Quick Start Guide for AWS EC2 based elastic clouds Felix Rauh ([email protected]) and Nikolas Herbst ([email protected]) At the Department of Computer Science Chair
Elasticity in Cloud Computing: What It Is, and What It Is Not
Elasticity in Cloud Computing: What It Is, and What It Is Not Nikolas Roman Herbst, Samuel Kounev, Ralf Reussner Institute for Program Structures and Data Organisation Karlsruhe Institute of Technology
SPEC Research Group. Sam Kounev. SPEC 2015 Annual Meeting. Austin, TX, February 5, 2015
SPEC Research Group Sam Kounev SPEC 2015 Annual Meeting Austin, TX, February 5, 2015 Standard Performance Evaluation Corporation OSG HPG GWPG RG Open Systems Group High Performance Group Graphics and Workstation
A Proposed Framework for Ranking and Reservation of Cloud Services Based on Quality of Service
II,III A Proposed Framework for Ranking and Reservation of Cloud Services Based on Quality of Service I Samir.m.zaid, II Hazem.m.elbakry, III Islam.m.abdelhady I Dept. of Geology, Faculty of Sciences,
Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems
Bratislava, Slovakia, 2014-12-10 Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems André van Hoorn, Christian Vögele Eike Schulz, Wilhelm
Load Balancing on a Grid Using Data Characteristics
Load Balancing on a Grid Using Data Characteristics Jonathan White and Dale R. Thompson Computer Science and Computer Engineering Department University of Arkansas Fayetteville, AR 72701, USA {jlw09, drt}@uark.edu
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
SLA-driven Dynamic Resource Provisioning for Service Provider in Cloud Computing
IEEE Globecom 2013 Workshop on Cloud Computing Systems, Networks, and Applications SLA-driven Dynamic Resource Provisioning for Service Provider in Cloud Computing Yongyi Ran *, Jian Yang, Shuben Zhang,
Key Research Challenges in Cloud Computing
3rd EU-Japan Symposium on Future Internet and New Generation Networks Tampere, Finland October 20th, 2010 Key Research Challenges in Cloud Computing Ignacio M. Llorente Head of DSA Research Group Universidad
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
CBUD Micro: A Micro Benchmark for Performance Measurement and Resource Management in IaaS Clouds
CBUD Micro: A Micro Benchmark for Performance Measurement and Resource Management in IaaS Clouds Vivek Shrivastava 1, D. S. Bhilare 2 1 International Institute of Professional Studies, Devi Ahilya University
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,
Comparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications
Comparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications Rouven Kreb 1 and Manuel Loesch 2 1 SAP AG, Walldorf, Germany 2 FZI Research Center for Information
SLA Based Information Security Metric for Cloud Computing from COBIT 4.1 Framework
International Journal of Computer Networks and Communications Security VOL. 1, NO. 3, AUGUST 2013, 95 101 Available online at: www.ijcncs.org ISSN 2308-9830 C N C S SLA Based Information Security Metric
Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.
Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement
A Survey of Cloud Computing Simulations and Cloud Testing
Page 1 of 8 A Survey of Cloud Computing Simulations and Cloud Testing Azin Oujani, [email protected] (A project report written under the guidance of Prof. Raj Jain) Download Abstract: Cloud computing
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
DiPerF: automated DIstributed PERformance testing Framework
DiPerF: automated DIstributed PERformance testing Framework Ioan Raicu, Catalin Dumitrescu, Matei Ripeanu Distributed Systems Laboratory Computer Science Department University of Chicago Ian Foster Mathematics
A Hierarchical Self-X SLA for Cloud Computing
A Hierarchical Self-X SLA for Cloud Computing 1 Ahmad Mosallanejad, 2 Rodziah Atan, 3 Rusli Abdullah, 4 Masrah Azmi Murad *1,2,3,4 Faculty of Computer Science and Information Technology, UPM, Malaysia,
Performance Workload Design
Performance Workload Design The goal of this paper is to show the basic principles involved in designing a workload for performance and scalability testing. We will understand how to achieve these principles
Architecting the Cloud
Architecting the Cloud Sumanth Tarigopula Director, India Center, Best Shore Applications Services 2011Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without
C-Meter: A Framework for Performance Analysis of Computing Clouds
9th IEEE/ACM International Symposium on Cluster Computing and the Grid C-Meter: A Framework for Performance Analysis of Computing Clouds Nezih Yigitbasi, Alexandru Iosup, and Dick Epema Delft University
Federation of Cloud Computing Infrastructure
IJSTE International Journal of Science Technology & Engineering Vol. 1, Issue 1, July 2014 ISSN(online): 2349 784X Federation of Cloud Computing Infrastructure Riddhi Solani Kavita Singh Rathore B. Tech.
Setting deadlines and priorities to the tasks to improve energy efficiency in cloud computing
Setting deadlines and priorities to the tasks to improve energy efficiency in cloud computing Problem description Cloud computing is a technology used more and more every day, requiring an important amount
Cloud Template, a Big Data Solution
Template, a Big Data Solution Mehdi Bahrami Electronic Engineering and Computer Science Department University of California, Merced, USA [email protected] Abstract. Today cloud computing has become
Evaluating Cloud Services Using DEA, AHP and TOPSIS model Carried out at the
A Summer Internship Project Report On Evaluating Cloud Services Using DEA, AHP and TOPSIS model Carried out at the Institute of Development and Research in Banking Technology, Hyderabad Established by
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
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
RANKING THE CLOUD SERVICES BASED ON QOS PARAMETERS
RANKING THE CLOUD SERVICES BASED ON QOS PARAMETERS M. Geetha 1, K. K. Kanagamathanmohan 2, Dr. C. Kumar Charlie Paul 3 Department of Computer Science, Anna University Chennai. A.S.L Paul s College of Engineering
Figure 1. The cloud scales: Amazon EC2 growth [2].
![Figure 1. The cloud scales: Amazon EC2 growth [2].](/thumbs/26/9133435.jpg "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 [email protected], [email protected] Abstract One of the most important issues
SMICloud: A Framework for Comparing and Ranking Cloud Services
2011 Fourth IEEE International Conference on Utility and Cloud Computing SMICloud: A Framework for Comparing and Ranking Cloud Services Saurabh Kumar Garg, Steve Versteeg and Rajkumar Buyya Cloud Computing
Workload Predicting-Based Automatic Scaling in Service Clouds
2013 IEEE Sixth International Conference on Cloud Computing Workload Predicting-Based Automatic Scaling in Service Clouds Jingqi Yang, Chuanchang Liu, Yanlei Shang, Zexiang Mao, Junliang Chen State Key
MELA: Monitoring and Analyzing Elasticity of Cloud Services
MELA: Monitoring and Analyzing Elasticity of Cloud Services Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar Distributed Systems Group, Vienna University of Technology E-mail: {d.moldovan,
Model-Driven Cloud Data Storage
Model-Driven Cloud Data Storage Juan Castrejón 1, Genoveva Vargas-Solar 1, Christine Collet 1, and Rafael Lozano 2 1 Université de Grenoble, LIG-LAFMIA, 681 rue de la Passerelle, Saint Martin d Hères,
Energetic Resource Allocation Framework Using Virtualization in Cloud
Energetic Resource Allocation Framework Using Virtualization in Ms.K.Guna *1, Ms.P.Saranya M.E *2 1 (II M.E(CSE)) Student Department of Computer Science and Engineering, 2 Assistant Professor Department
International Journal of Advance Research in Computer Science and Management Studies
Volume 2, Issue 11, November 2014 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
Secure Cloud Transactions by Performance, Accuracy, and Precision
Secure Cloud Transactions by Performance, Accuracy, and Precision Patil Vaibhav Nivrutti M.Tech Student, ABSTRACT: In distributed transactional database systems deployed over cloud servers, entities cooperate
NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations
2011 Fourth IEEE International Conference on Utility and Cloud Computing NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations Saurabh Kumar Garg and Rajkumar Buyya Cloud Computing and
FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS
International Journal of Computer Engineering and Applications, Volume VIII, Issue II, November 14 FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS Saju Mathew 1, Dr.
DOCLITE: DOCKER CONTAINER-BASED LIGHTWEIGHT BENCHMARKING ON THE CLOUD
DOCLITE: DOCKER CONTAINER-BASED LIGHTWEIGHT BENCHMARKING ON THE CLOUD 1 Supervisors: Dr. Adam Barker Dr. Blesson Varghese Summer School 2015 Lawan Thamsuhang Subba Structure of Presentation 2 Introduction
On the Amplitude of the Elasticity Offered by Public Cloud Computing Providers
On the Amplitude of the Elasticity Offered by Public Cloud Computing Providers Rostand Costa a,b, Francisco Brasileiro a a Federal University of Campina Grande Systems and Computing Department, Distributed
Cloud Broker for Reputation-Enhanced and QoS based IaaS Service Selection
Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC Cloud Broker for Reputation-Enhanced and QoS based IaaS Service Selection Ruby Annette.J 1, Dr. Aisha Banu.W 2 and Dr.Sriram
SLA Business Management Based on Key Performance Indicators
, July 4-6, 2012, London, U.K. SLA Business Management Based on Key Performance Indicators S. Al Aloussi Abstract-It is increasingly important that Service Level Agreements (SLAs) are taken into account
AEIJST - June 2015 - Vol 3 - Issue 6 ISSN - 2348-6732. Cloud Broker. * Prasanna Kumar ** Shalini N M *** Sowmya R **** V Ashalatha
Abstract Cloud Broker * Prasanna Kumar ** Shalini N M *** Sowmya R **** V Ashalatha Dept of ISE, The National Institute of Engineering, Mysore, India Cloud computing is kinetically evolving areas which
THE CLOUD AND ITS EFFECTS ON WEB DEVELOPMENT
TREX WORKSHOP 2013 THE CLOUD AND ITS EFFECTS ON WEB DEVELOPMENT Jukka Tupamäki, Relevantum Oy Software Specialist, MSc in Software Engineering (TUT) [email protected] / @tukkajukka 30.10.2013 1 e arrival
Profit-driven Cloud Service Request Scheduling Under SLA Constraints
Journal of Information & Computational Science 9: 14 (2012) 4065 4073 Available at http://www.joics.com Profit-driven Cloud Service Request Scheduling Under SLA Constraints Zhipiao Liu, Qibo Sun, Shangguang
Resource Allocation Avoiding SLA Violations in Cloud Framework for SaaS
Resource Allocation Avoiding SLA Violations in Cloud Framework for SaaS Shantanu Sasane Abhilash Bari Kaustubh Memane Aniket Pathak Prof. A. A.Deshmukh University of Pune University of Pune University
Low Cost Quality Aware Multi-tier Application Hosting on the Amazon Cloud
Low Cost Quality Aware Multi-tier Application Hosting on the Amazon Cloud Waheed Iqbal, Matthew N. Dailey, David Carrera Punjab University College of Information Technology, University of the Punjab, Lahore,
International Journal of Computer & Organization Trends Volume21 Number1 June 2015 A Study on Load Balancing in Cloud Computing
A Study on Load Balancing in Cloud Computing * Parveen Kumar * Er.Mandeep Kaur Guru kashi University,Talwandi Sabo Guru kashi University,Talwandi Sabo Abstract: Load Balancing is a computer networking
Load Distribution in Large Scale Network Monitoring Infrastructures
Load Distribution in Large Scale Network Monitoring Infrastructures Josep Sanjuàs-Cuxart, Pere Barlet-Ros, Gianluca Iannaccone, and Josep Solé-Pareta Universitat Politècnica de Catalunya (UPC) {jsanjuas,pbarlet,pareta}@ac.upc.edu
Performance analysis of Windows Azure data storage options
Performance analysis of Windows Azure data storage options Istvan Hartung and Balazs Goldschmidt Department of Control Engineering and Information Technology, Budapest University of Technology and Economics,
Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture
Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture 1 Shaik Fayaz, 2 Dr.V.N.Srinivasu, 3 Tata Venkateswarlu #1 M.Tech (CSE) from P.N.C & Vijai Institute of
Towards an Optimized Big Data Processing System
Towards an Optimized Big Data Processing System The Doctoral Symposium of the IEEE/ACM CCGrid 2013 Delft, The Netherlands Bogdan Ghiţ, Alexandru Iosup, and Dick Epema Parallel and Distributed Systems Group
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
The public-cloud-computing market has
View from the Cloud Editor: George Pallis [email protected] Comparing Public- Cloud Providers Ang Li and Xiaowei Yang Duke University Srikanth Kandula and Ming Zhang Microsoft Research As cloud computing
Characterizing Task Usage Shapes in Google s Compute Clusters
Characterizing Task Usage Shapes in Google s Compute Clusters Qi Zhang 1, Joseph L. Hellerstein 2, Raouf Boutaba 1 1 University of Waterloo, 2 Google Inc. Introduction Cloud computing is becoming a key
Hybrid Cloud Architecture: How to Streamline Hybrid Cloud Migration
Hybrid Cloud Architecture: How to Streamline Hybrid Cloud Migration Introduction According to a Nucleus Research report cloud applications deliver 1.7 times more return on investment on average over on-
International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015
RESEARCH ARTICLE OPEN ACCESS Ensuring Reliability and High Availability in Cloud by Employing a Fault Tolerance Enabled Load Balancing Algorithm G.Gayathri [1], N.Prabakaran [2] Department of Computer
Dynamic Pricing for Usage of Cloud Resource
Dynamic Pricing for Usage of Cloud Resource K.Sangeetha, K.Ravikumar Graduate Student, Department of CSE, Rrase College of Engineering, Chennai, India. Professor, Department of CSE, Rrase College of Engineering,
STeP-IN SUMMIT 2013. June 18 21, 2013 at Bangalore, INDIA. Performance Testing of an IAAS Cloud Software (A CloudStack Use Case)
10 th International Conference on Software Testing June 18 21, 2013 at Bangalore, INDIA by Sowmya Krishnan, Senior Software QA Engineer, Citrix Copyright: STeP-IN Forum and Quality Solutions for Information
Design and Evaluation of a Hierarchical Multi-Tenant Data Management Framework for Cloud Applications
Design and Evaluation of a Hierarchical Multi-Tenant Data Management Framework for Cloud Applications Pieter-Jan Maenhaut, Hendrik Moens, Veerle Ongenae and Filip De Turck Ghent University, Faculty of
Characterizing Task Usage Shapes in Google s Compute Clusters
Characterizing Task Usage Shapes in Google s Compute Clusters Qi Zhang University of Waterloo [email protected] Joseph L. Hellerstein Google Inc. [email protected] Raouf Boutaba University of Waterloo [email protected]
Cloud Computing Architectures and Design Issues
Cloud Computing Architectures and Design Issues Ozalp Babaoglu, Stefano Ferretti, Moreno Marzolla, Fabio Panzieri {babaoglu, sferrett, marzolla, panzieri}@cs.unibo.it Outline What is Cloud Computing? A
Profit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs
Profit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs Mahdi Ghamkhari and Hamed Mohsenian-Rad Department of Electrical Engineering University of California at Riverside,
Cloud Computing for Business
4 Buying Cloud Services The following excerpt is from Chapter Four Buying Cloud Services 4.1 Determining Fit In establishing your cloud vision, you have achieved an understanding of the business context,
Principles and Methods for Elastic Computing
Principles and Methods for Elastic Computing CompArch Keynote - Lille, 1 July 2014 Schahram Dustdar Distributed Systems Group TU Vienna dsg.tuwien.ac.at Acknowledgements Includes some joint work with Hong-Linh
Flauncher and DVMS Deploying and Scheduling Thousands of Virtual Machines on Hundreds of Nodes Distributed Geographically
Flauncher and Deploying and Scheduling Thousands of Virtual Machines on Hundreds of Nodes Distributed Geographically Daniel Balouek, Adrien Lèbre, Flavien Quesnel To cite this version: Daniel Balouek,
Infrastructure as a Service (IaaS)
Infrastructure as a Service (IaaS) (ENCS 691K Chapter 4) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ References 1. R. Moreno et al.,
FCM: an Architecture for Integrating IaaS Cloud Systems
FCM: an Architecture for Integrating IaaS Systems Attila Csaba Marosi, Gabor Kecskemeti, Attila Kertesz, Peter Kacsuk MTA SZTAKI Computer and Automation Research Institute of the Hungarian Academy of Sciences
How To Understand Cloud Computing
Cloud Computing: a Perspective Study Lizhe WANG, Gregor von LASZEWSKI, Younge ANDREW, Xi HE Service Oriented Cyberinfrastruture Lab, Rochester Inst. of Tech. Abstract The Cloud computing emerges as a new
A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing
A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing Sonia Lamba, Dharmendra Kumar United College of Engineering and Research,Allahabad, U.P, India.
Dynamic Resource Pricing on Federated Clouds
Dynamic Resource Pricing on Federated Clouds Marian Mihailescu and Yong Meng Teo Department of Computer Science National University of Singapore Computing 1, 13 Computing Drive, Singapore 117417 Email: