1 Available online at ScienceDirect Procedia Computer Science 68 (2015 ) HOLACONF - Cloud Forward: From Distributed to Complete Computing CloudStore - towards scalability benchmarking in Cloud computing Richard Sanders a *, Gunnar Brataas a, Mariano Cecowski b, Kjetil Haslum a, Simon Ivanšek b, Jure Polutnik b, Brynjar Viken a a SINTEF, Strindvegen 4, 7043 Trondheim, Norway b XLAB, Pot za Brdom 100, 1000 Ljubljana, Slovenia Abstract This paper describes CloudStore, an open source application that lends itself to analyzing key characteristics of Cloud computing platforms. Based on an earlier standard from transaction processing, it represents a simplified version of a typical e-commerce application an electronic book store. We detail how a deployment on a popular public cloud offering can be instrumented to gain insight into system characteristics such as capacity, scalability, elasticity and efficiency Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review Peer-review under under responsibility responsibility of Institute of Institute of Communication of Communication and Computer and Computer Systems. Systems. Keywords: Cloud computing, Measurements, Scalability, Performance, Capacity, Elasticity, Efficiency, Amazon Web Services, AMS, TCP-W 1. Introduction Cloud Computing has and is gaining traction in the ICT industry since the turn of the millennium 1. Hybrid, private and public clouds promise a number of benefits for enterprises. However, there does not seem to be a range of standardized applications which can be used to compare the performance of different cloud deployments. In particular, there is a lack of standard open sourced applications to express and compare metrics. To this end the CloudScale project 2 has developed an application we call CloudStore 3. CloudStore is a free and open implementation of a typical web application, with some inherent scalability and performance issues related to the relational database. The goal of CloudStore is to serve as a relevant and standardized baseline to measure and compare different cloud providers, architectures and deployment in terms of capacity, scalability, elasticity and * Corresponding author. Tel.: ; fax: address: Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Institute of Communication and Computer Systems. doi: /j.procs
2 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) efficiency. CloudStore embodies functionality for an online book store, and is based on the transactional web e- Commerce benchmark (TPC-W), a web server and database performance benchmark originally proposed by the Transaction Processing Performance Council 4. Although now obsolete, TCP-W has functional and non-functional characteristics that can be used to achieve our aims. CloudStore and the measurements methodology helps answer questions such as which architecture makes more efficient use of our resources?, which cloud provider is cheaper for an expected usage fluctuation?, which implementation/deployment is more scalable within our expected range? Although our motivation for creating CloudStore was the need for an open application to demonstrate the scalability tools of the CloudScale project 2, CloudStore can equally well be used for comparing (benchmarking) different cloud and platform providers (Amazon, Google, Microsoft, and private clouds). Benchmarking is generally costly. A standardized service like CloudStore may reduce such costs. 2. CloudStore a generic e-commerce application for Cloud computing CloudStore is a typical example of a web application. It structure is depicted in Figure 1. A user makes use of the application via a web browser, which has direct IP communication to components that provide the user experience. Most of the functionality is handled by a Web server, which receives HTTP requests from Remote browsers, and for information processing purposes connects to a Database server storing information about users (customers) and the items of the store (information about books). The HTTP response back to the Remote browser contains references to an Image server (to efficiently display pictures of books and other graphics). In the case of payment transactions the HTTP response contains references to a Payment gateway. TPC-W specifies that the system should obtain credit card authorization from a Payment Gateway Emulator, and, if successful, present the user with an order confirmation. The original TCP-W standard 4 on which CloudStore is based consists of a detailed specification of an on-line shop, defining 14 operations for browsing and buying books, together with non-functional requirements such as data element size and time responses, as well as expected user behavior and work load. The standard was originally designed for benchmarking implementations and deployments of transaction systems. We have taken the specifications to create an application that provides us the means to measure characteristics such as capacity, scalability, elasticity and efficiency in alternative deployments using cloud computing. In addition to re-implementing the functional behavior, we adopted the non-functional requirements, such as the percentage of errors and time responses, in order to produce Service Level Objectives (SLOs) that should be respected by the deployment to be tested. The result is a scenario for evaluating e.g. scalability, elasticity and actual costs of different cloud solutions using a simple yet realistic example of an e-commerce application.
3 80 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) TPC-W defines three different Usage Profiles: a shopping mix, browsing mix and ordering mix. For each usage profile a different probability rate is defined for each of the 14 operations. As we shall see in the next chapter, the Usage Profiles can be exploited during the generation of synthetic user traffic used to analyze a deployment. 3. CloudStore deployed on Amazon Web Services To exemplify how CloudStore can be used, we show how we have deployed and instrumented it using a public cloud provider, in this case the widely popular Amazon Web Services (AWS) 5. Embarking on this, we made some design choices, both regarding how the functional components were deployed, how to generate synthetic user traffic and how to measure key characteristics such as resource utilization, response times (including violations) and cost The deployment architecture Figure 2 shows one deployment of CloudStore with the load generator (Distributed JMeter), the functional components of CloudStore (Web server, Database server, Image server and Payment gateway) and their deployment on the Amazon cloud computing platform. The following is a brief account of the deployment and what it entails. The workload generator Distributed JMeter 6 is used to create the synthetic user load on the CloudStore application server. Distributed JMeter is run in a Linux environment in Amazon Elastic Compute Cloud (EC2) in AWS. It sends HTTP requests to the Elastic Load Balancer. Emulating the remote browsers, JMeter also makes the HTTP requests for images from the Image server. JMeter logs data for all HTTP requests and responses, enabling the measurement of system behavior characteristics such as violations of service level agreements (SLO Violations), i.e. how many requests time out or fail. The Elastic load balancer is a part of AWS, and is used to automatically distribute incoming application traffic across multiple EC2 instances. The CloudStore Web server is deployed on one or several EC2 instances running the Linux operating system, and consists of two components: 1. A Web proxy based on the open source proxy server Nginx 8. The web proxy forwards HTTP requests to the CloudStore application. 2. The CloudStore application, which is implemented as a Model-View-Controller (MVC) pattern web application using the open source java application framework called Spring Framework 9. The CloudStore application communicates with the Database server via Hibernate ORM 10, using the JDBC driver. It also
4 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) makes the HTTP requests for payment via the Payment gateway. The application was modified to be able to handle a multi-instance database, in order to make use of an Amazon database cluster. Note that EC2 can be set to enable Auto Scaling, implying that AWS will automatically select a suitable number of instances of the specified instance type and size. Depending on the purpose of a measurement, Auto Scaling is intentionally enabled or disabled. This in particular applies to the EC2 instances where the CloudScale application is deployed. The Database server is realized by Amazon Relational Database Services (RDS) running a MySQL cluster in EC2. Based on the selected RDS instance type and size, the RDS database server can be configured to have one master and several replicas. The Image server is realized using Amazon CloudFront and the S3 service in AWS. For the Payment gateway a simple response time generator is introduced instead of an actual interface to a real external payment service. In order to mimic a real service that is independent from our system, the synthetic code introduces a response delay with a defined statistical distribution. In order to have the response time generator on a completely independent external platform, it was deployed on the PaaS Heroku 11. In order to effectively act as an external service, as it is deployed in a different AWS region Generating load with JMeter In order to test different deployments we prepared JMeter 7 scenario scripts adhering to the TPC-W specifications 4. By developing a distributed version of the workload generator JMeter (Distributed JMeter 6 ), we have made it possible to create more traffic than what is possible with a single JMeter instance, which would otherwise impose a limitation of around 2000 simultaneous virtual clients (users). In order to measure metrics related to the scalability and elasticity of a specific implementation/deployment, we need to define load variations. To that end, we created well-defined Usage Profiles that we call Usage Evolutions, from which JMeter scripts for load generation can be created and used in different systems to be able to compare them systematically and reproducibly. Different Usage Evolutions are used for different measurements. For instance, if we want to evaluate how a deployed system responds to load increases, a Usage Evolution with a simple ramp-up is appropriate, as depicted to the left in Figure 3. Adjusting the slope of the ramp-up helps determine characteristics such as the fastest growth rate that the system can handle (see Users Elasticity Speed in section 4.3). 4. Evaluating metrics with CloudStore Fig. 3. Two Usage Evolutions; a single ramp (for Elasticity), and a sustained load after a warm-up and smooth growth (for Capacity). The CloudStore setup can be used to measure and validate a number of different metrics. The measurements can show how capacity, scalability, elasticity and efficiency are a function of the variation in synthetic user load, and are determined by the application and a deployment configuration. We define the following concepts:
5 82 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Virtual User is an abstract entity that represents a user in real life. Usage Profiles: define the succession of actions that a user is expected to perform; a short interaction story with different calls and waiting times between them (TCP-W 4 defines different Usage Profiles for Browsing, Ordering and Shopping). Usage Evolution is a function over time of the load that the system is expected to handle, defined as number of virtual users over time, or their arrival rate over time. The usage profile is designed to represent the kind of load that reflects the real-life situation one wishes to evaluate. Minimum Deployment is the smallest (cheapest) possible deployment which is powerful enough to run the application (regardless of its capacity). For CloudStore running on AWS it consists of one instance of the virtual machine type t2.small EC2 for the Web server and one instance of type db.t2.small for the Database server. However, note that we have used the type db.m3.large for the database server in the example measurements in this article, as db.t2.small only supports a very small number of users. We disregard the Payment gateway, since this is a dummy service. SLO Violations: TPC-W 4 defines the share of requests that are allowed to fail or time-out (i.e. exceed response time requirements); observing a higher rate constitutes a breach (violation) of the Service Level Objectives (SLOs). Time limits are defined for each of the 14 operations, e.g. 3 seconds for the operation "Buy Request". At least 90% of all response times for each operation must be under the value specified for the operations. When we in the following say the SLOs are violated, we mean that the violation rate has exceeded the threshold for one of more of the operations. Below we provide examples of metrics along with examples of what measurements should be taken in each case Capacity Metrics Capacity metrics are typically a scalar that represents the amount of work a system can respond to sustainably without resulting in SLO Violations. Capacity (#U): The number of simultaneous virtual users that a fixed amount of Cloud resources can handle without SLO Violations, given a Usage Profile. Example from Table 1: The three last measurements show a Capacity of 6471 users with a Browsing usage profile. Arrival Rate Capacity (#U/s): the maximum rate of users' arrivals for which a non-elastic system can cope without SLO Violations, for a given usage profile. Example: 3 new Shopping users per second. With the CloudStore deployed in AWS we can measure the number of virtual users that the system can handle by making several runs with different sustained Usage Evolutions, in order to find the highest number for which the system does not violate the SLOs. This highest number is the Capacity.
6 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Web proxy and CloudStore application server #EC2 instances Table 1. Cost and capacities of different configurations Database server EC2 Size CPU use (%) # RDS size # RDS Database use (%) Capacity Cost/h 1 t2.small 100 db.m3.large t2.small 100 db.m3.large t2.small 100 db.m3.large t2.small 100 db.m3.large t2.small 90 db.m3.large t2.small 100 db.m3.large While measuring the behavior of a particular configuration, one can gather important information that will help us save time and obtain reliable results. In the case of the CloudStore, the number and type of the virtual machines used for both the Web server (EC2) and Database server (RDS) can and will affect the final Capacity, and/or the associated cost. Monitoring the percentage utilization of each of these components can help to avoid measuring combinations that make no sense. For instance, if the Web server utilization is close to 100% and the database utilization is far below 100%, increasing the database size will probably yield no benefit in terms of Capacity, and will certainly cost more; hence one can consider skipping running configurations with a larger or duplicated database Scalability Metrics Scalability metrics characterize how much more load a system can handle once resources have been allocated. For scalable systems the slope of the load increase grows linearly with the increase in amount of resources, while in systems that don't scale well this line curve flattens out (and might even start dropping). Scalability Range ([#U, #U]): segment between the number of users that the Minimum Deployment can handle, and the maximum achievable Capacity (the point where adding resources doesn't increase Capacity). Scalability Rate (#U / Δ Resource): function of the increase in Capacity given an increase in resources (e.g. Database server Scalability Rate, Web server instances Scalability Rate...), see example in Figure 4 below. This metric will usually be a function rather than a scalar (though it can be a scalar in a particular load range). Cost scalability: describes how the Capacity increases depending on the (minimum) cost of the configuration. Measuring the Scalability Range consists of measuring the Capacity of the Minimum deployment, and the Capacity of deployments with increased resources until the Capacity flattens out. To calculate the Scalability Rate, we measure the Capacity at different resource configurations and plot them against the number of resources until a maximum range is reached. In the graph of Figure 4, the Capacity measurements from Table 1 have been plotted (the points in the diagram). Based on the same data, we observe that the Scalability range is from 1250 to 6471 simultaneous virtual users, and is indicated to the right in Figure 4. The Scalability rate at each point is indicated by the continuous line between the points, while the capacity function is a step function between the points.
7 84 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Elasticity Metrics Fig. 4. Scalability metrics graph from example data. By the term Elasticity we mean the ability of a system to increase (and decrease) its provisioned resources automatically in order to cope with the fluctuations in workload. There are several different methods available 12. A useful approach is counting the number of SLO Violations that occur during a realistic Usage Evolution scenario, giving us a relevant figure and comparison tool (Usage Evolution Elasticity). Another approach is to find the steepest load growth ramp within which our system is able to keep violations under the defined SLO threshold (Users Elasticity Speed). In cases where the usage profile in real life is unpredictable, it may be more useful to know how long it takes the system to get response times back below the allowed SLO Violation rates after a sudden increase in workload (Mean Time to Quality Repair). Usage Evolution Elasticity (#violations): #SLO Violations for the duration of the scenario. Users Elasticity Speed (#U/t): rate of additional users that the system can handle without violating the SLOs (e.g. we can add 1000 users / hour). Mean Time To Quality Repair (t): time needed for the elastic system to return to normal (not violating the SLOs) after a sudden (predefined) increase in usage. To measure the Usage Evolution Elasticity, we define a Usage Evolution of interest, and run it on a system configuration with Auto-scaling enabled, counting the number of SLO Violations in which it incurs. This simple metric can be used to compare different configurations directly, since a system with poor elasticity is expected to produce more SLO Violations than a system that can quickly react to the increases of demand. Nevertheless, the comparison can only be understood as an order and not quantitatively since, for instance, twice as many violations does not mean twice as elastic. To find Users Elasticity Speed for a system one defines Usage Evolutions with the same Usage profile but with different ramps-up in usage, and search for the steepest ramp-up for which the system doesn t exceed the SLO Violation threshold. This is exemplified in Figure 5. Here the Usage Evolutions (ramp-ups) that exceed the SLO Violation threshold are coloured in orange/red, and the ones that don't in green. The fastest growth the system can handle is thus approximately 6.25 additional users per second. For Mean Time to Quality Repair we define a Usage Evolution with a work-load increase step (the amount being relevant to what can be expected in a real-life situation), turn on Auto Scaling, and measure the time it takes, on average, to return to a normal state without SLO Violations.
8 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) We can then compare the Elasticity of different deployment configurations by comparing the resulting number of SLO Violations, Elasticity Speed and Repair Times between them. Analysis of Elasticity should be combined with an analysis of Efficiency, since reserving resources long before they are needed yields high elasticity, but poor efficiency and thus results in unnecessary costs Efficiency Metrics Fig. 5. Different Usage Evolutions used to obtain the Users Elasticity Speed, coloured by SLO violation rate. Finally, Efficiency metrics give us information about cost and resource efficiency. These results can help us understand which platforms make better use of our private infrastructure and where there s room for improvement. Cost metrics are the main results that we use when choosing a Cloud provider or deployment architectures, since it is usually the factor that determines which options are the most economical. Resource Provisioning Efficiency (0% - 100%): over-use of resources, i.e. the ratio between the cost of the Minimum Deployment for a usage profile and the cost of the actual resources used (minimum/actual cost). Marginal Cost ( ): cost of an additional sustained user on the system (usually per month or per year). Total Cost ( ): cost of running a scenario without SLO Violations on a Cloud Provider using particular technologies and deployment architecture. Resource Provisioning Efficiency is related to Elasticity metrics. For instance, we create a pyramid-shaped usage evolution (see Figure 6) and define the auto-scaling policies for increasing and decreasing the number of instances (i.e. % of CPU utilization) so that the system does not surpass the SLO Violation limits. We then measure the number of instances across time (which we can translate into Capacity and Cost with the information we already have from Table 1). Next we calculate the minimum number of instances needed for the load present at any given time using the Capacity information we already have (see Table 2). Finally, we calculate the Overhead; that is, the difference between the actual number of instances (or Cost) and the minimum needed. The ratio (in percent) between the ideal and real resource usage (or Cost) is the Resource Provisioning Efficiency of the system for that run.
9 86 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Fig. 6. Actual versus minimal usage of resources. The numbers are given in Table 2. In our example, Resource Provisioning Efficiency is 45/54 = 83.33%, while Cost Efficiency is 5.06 / 5.29 = 95.65%. In this case cost efficiency is higher than resource efficiency, since the cost includes the relatively expensive database cluster, which is invariant in the configuration throughout the run. Table 2. Actual and minimal values for the number of EC2 instances, their capacity and cost. Marginal Costs per user is a discontinuous function. It makes more sense to plot the costs per users with slopes covering those steps. Thus the Marginal Cost per user (which will depend on which part of the curve we are analyzing)
10 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Fig. 7. Cost and Capacity plots for different configurations, with curves drawn for Minimum Cost and Marginal Cost rates. can be considered to be the slope between a pair of points. We can also calculate the average Marginal Costs by dividing the cost for the maximum number of users by the number of users. Consider the example in Fig. 7. Here Capacity points for different configurations are plotted; they include the values in Table 1 plus more points where the database configurations have been changed. The leftmost points in such a graph have the best capacity/cost ratio, while the points further to the right have a worse ratio. A continuous line between the "best" points indicates the Marginal Costs at each point, while an approximation of the Minimum Cost is the step function between these points. In this case we calculate the Average Marginal Cost to be /h divided by 7059 users = E-05 /hour, or roughly 0.59 per year per additional user. The Marginal Cost for an additional user in the range between the 3529 th and 5625 th user (these values are found in Table 1) would be [(0.263 /h /h) / ( )] = 1.24E-05 /hour, that is around 0.11 per year per additional user. This is a good example of economy of scale. We also see that catering for more than 6500 simultaneous users comes at a much higher price; a systems manager would find it beneficial to avoid such situations, or, better still, investigate why the system does not scale, and redesign the system or the deployment architecture so that it does. Lastly, the Total Cost tells us how much it would cost to handle a particular usage, defined according to expected work-loads. We achieve this by finding the cheapest configuration and elasticity parameters that can handle the usage of interest without violating the SLO threshold. The costs of each run can be calculated by adding up the usage of resources. Most cloud providers make this accumulated cost information available (see Table 2). 5. Discussion and conclusions CloudStore allows one to measure and compare different deployment configurations in terms of capacity, elasticity and costs. A set of methods and tools to perform measurements of a number of metrics have been created for Amazon Web Services, including a load generation that is distributed in order to produce load. Tools and measurement methods for other cloud providers can be created for CloudStore. A common problem with benchmarks is that they have to be representative 12, i.e. the results should have some general interest also for other services. TPC-W 4 was originally made as a benchmark, and much care was taken into making it a general e-commerce application. However, since TPC-W as a benchmark is deprecated, it may no longer be so representative. CloudStore represents a cloud-enabled modernized implementation of the functional and nonfunctional requirements defined in TPC-W that uses current technologies such as the Spring framework, and the more scalable MVC architecture, representing a much better the behaviour of a modern application.
11 88 Richard Sanders et al. / Procedia Computer Science 68 ( 2015 ) Other efforts have been made to produce standards for the measurement comparison cloud services, such as the work performed within FP7 project Artist 14, but these efforts were in general focused in benchmarking performance and evaluating the benefits of the migration of legacy systems to the cloud, rather than effectively analyzing the scalability or elasticity capabilities of cloud services. Although TCP-W has been deprecated as in industry benchmark, it is often used in academic projects. A publicly available implementation like CloudStore can play a similar role, making it easier to compare results of measurements, e.g. in academic publications. Although we have so far only deployed CloudStore on two cloud computing platforms, Amazon Web Services and OpenStack 15, by extending the provided deployment scripts 16, the CloudStore can be deployed on any IaaS to measure and compare any number of cloud providers and deployment architectures. What s more, since CloudStore can be set-up to make use of Platform services such as Amazon s S3 or RDS, it can be deployed to a PaaS such as Heroku 11 or Google App Engine, allowing it to decide on the cost/benefit between different IaaS and PaaS CloudStore and the CloudScale tools are made available through the project s GitHub repository 17, which includes instructions of how to make use of them. Acknowledgements The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/ ) under grant no (CloudScale). References 1. Thomas Erl, Zaigham Mahmood, and Richardo Puttini. Cloud Computing: Concepts, Technology & Architecture. Prentice Hall, CloudScale. CloudScale Project. Last visited: 13 th July CloudScale. CloudStore, github.com/cloudscale-project/cloudstore. Last visited: 13 th July Transaction Processing Performance Council. TPC-W benchmark, Last visited: 13 th July Amazon. Amazon Web Services, aws.amazon.com. Last visited: 13 th July CloudScale. Distributed JMeter. github.com/cloudscale-project/distributed-jmeter. Last visited: 13 th July The Apache Software Foundation. Apache JMeter. jmeter.apache.org, Last visited: 13 th July Nginx. Nginx. nginx.org/en/. Last visited: 13 th July Spring. Spring Framework. projects.spring.io/spring-framework/. Last visited: 13 th July Hibernate. Hibernate ORM hibernate.org/orm/. Last visited: 13 th July Heroku. Heroku. Last visited: 13 th July Matthias Becker, Sebastian Lehrig, and Steffen Becker. Systematically deriving quality metrics for cloud computing systems. In International Conference on Performance Engineering. ACM, David A. Lilja, Measuring Computer Performance, Cambridge University Press, Artist. Artist publications. Last visited: 13 th August Openstack, OpenStack, Last visited: 13 th July CloudScale. CloudScale GitHub. github.com/cloudscale-project/deployment-scripts. Last visited: 13 th July CloudScale. CloudScale GitHub. github.com/cloudscale-project. Last visited: 13 th July 2015
BASEL UNIVERSITY COMPUTER SCIENCE DEPARTMENT Cloud Computing: Meet the Players. Performance Analysis of Cloud Providers Distributed Information Systems (CS341/HS2010) Report based on D.Kassman, T.Kraska,
P3 InfoTech Solutions Pvt. Ltd http://www.p3infotech.in July 2013 Created by P3 InfoTech Solutions Pvt. Ltd., http://p3infotech.in 1 Web Application Deployment in the Cloud Using Amazon Web Services From
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
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: firstname.lastname@example.org
Choosing the Right Cloud Provider for Your Business Abstract As cloud computing becomes an increasingly important part of any IT organization s delivery model, assessing and selecting the right cloud provider
CLOUD COMPUTING An Overview Abstract Resource sharing in a pure plug and play model that dramatically simplifies infrastructure planning is the promise of cloud computing. The two key advantages of this
Certified Cloud Computing Professional VS-1067 Certified Cloud Computing Professional Certification Code VS-1067 Vskills Cloud Computing Professional assesses the candidate for a company s cloud computing
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
RemoteApp Publishing on AWS WWW.CORPINFO.COM Kevin Epstein & Stephen Garden Santa Monica, California November 2014 TABLE OF CONTENTS TABLE OF CONTENTS... 2 ABSTRACT... 3 INTRODUCTION... 3 WHAT WE LL COVER...
CLOUD COMPUTING When It's smarter to rent than to buy Is it new concept? Nothing new In 1990 s, WWW itself Grid Technologies- Scientific applications Online banking websites More convenience Not to visit
ST 810, Advanced computing Eric B. Laber & Hua Zhou Department of Statistics, North Carolina State University January 30, 2013 Supercomputers are expensive. Eric B. Laber, 2011, while browsing the internet.
Mapping and Geographic Information Systems Professional Services G-Cloud Services RM 1557 Service Definition Esri UK GCloud 5 Lot 4 Specialist Services Government Procurement Service Acknowledgement Esri
Introduction to Cloud Computing Cloud Computing I (intro) 15 319, spring 2010 2 nd Lecture, Jan 14 th Majd F. Sakr Lecture Motivation General overview on cloud computing What is cloud computing Services
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
APP DEVELOPMENT ON THE CLOUD MADE EASY WITH PAAS This article looks into the benefits of using the Platform as a Service paradigm to develop applications on the cloud. It also compares a few top PaaS providers
ABSTRACT Crystal clear requirements before starting an activity are always helpful in achieving the desired goals. Achieving desired results are quite difficult when there is vague or incomplete information
EEDC Execution Environments for Distributed Computing 34330 Master in Computer Architecture, Networks and Systems - CANS Scalability Study of web apps in AWS Sergio Mendoza email@example.com
Data Centers and Cloud Computing Intro. to Data centers Virtualization Basics Intro. to Cloud Computing Data Centers Large server and storage farms 1000s of servers Many TBs or PBs of data Used by Enterprises
A white Success The performance testing helped the client identify and resolve performance bottlenecks which otherwise crippled the business. The ability to support 500 concurrent users was a performance
19.10.11 Amazon Elastic Beanstalk A Short History of AWS Amazon started as an ECommerce startup Original architecture was restructured to be more scalable and easier to maintain Competitive pressure for
Capping Server Cost and Energy Use with Hybrid Cloud Computing Richard Gimarc Amy Spellmann Mark Preston CA Technologies Uptime Institute RS Performance Connecticut Computer Measurement Group Friday, June
JIOS, VOL. 35, NO. 1 (2011) SUBMITTED 02/11; ACCEPTED 06/11 UDC 004.75 Comparison of Cloud vs. Tape Backup Performance and Costs with Oracle Database University of Ljubljana Faculty of Computer and Information
219322 Electronic Commerce Engineering Lecture 4 Laudon & Traver: Chapter 4 Building an E-commerce Web Site Copyright 2007 Pearson Education, Inc. Slide 4-1 Building an E-commerce Site: A Systematic Approach
Case Study - I Industry: Social Networking Website Technology : J2EE AJAX, Spring, MySQL, Weblogic, Windows Server 2008 Challenges The scalability of the database servers to execute batch processes under
Architectural Templates: Engineering Scalable SaaS Applications Based on Architectural Styles Sebastian Lehrig Software Engineering Group & Heinz Nixdorf Institute University of Paderborn, Paderborn, Germany
Data Centers and Cloud Computing Intro. to Data centers Virtualization Basics Intro. to Cloud Computing 1 Data Centers Large server and storage farms 1000s of servers Many TBs or PBs of data Used by Enterprises
Reference Architecture Cisco Hybrid Cloud Solution: Deploy an E-Business Application with Cisco Intercloud Fabric for Business Reference Architecture 2015 Cisco and/or its affiliates. All rights reserved.
Volume 1, Issue 1 ISSN: 2320-5288 International Journal of Engineering Technology & Management Research Journal homepage: www.ijetmr.org Analysis and Research of Cloud Computing System to Comparison of
Cloud Infrastructure as a Service Market Update, 2015 United States March 2015 Contents Section Slide Numbers Executive Summary 5 Market Overview 7 Definitions & Study Scope 8-10 Market Drivers 12-14 Market
CLOUD COMPUTING PRACTICE 82 Chapter 9 PUBLIC CLOUD LABORATORY Hand on laboratory based on AWS Sucha Smanchat, PhD Faculty of Information Technology King Mongkut s University of Technology North Bangkok
A Monitored Student Testing Application Using Cloud Computing R. Mullapudi and G. Hsieh Department of Computer Science, Norfolk State University, Norfolk, Virginia, USA firstname.lastname@example.org, email@example.com
Published in the 2nd USENIX Workshop on Hot Topics in Cloud Computing 2010 CiteSeer x in the Cloud Pradeep B. Teregowda Pennsylvania State University C. Lee Giles Pennsylvania State University Bhuvan Urgaonkar
OPTIMIZING PERFORMANCE IN AMAZON EC2 While the business decision to migrate to Amazon public cloud services can be an easy one, tracking and managing performance in these environments isn t so clear cut.
Benchmarking Amazon s EC2 Cloud Platform Goal of the project: The goal of this project is to analyze the performance of an 8-node cluster in Amazon s Elastic Compute Cloud (EC2). The cluster will be benchmarked
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
CHAPTER 8 CLOUD COMPUTING SE 458 SERVICE ORIENTED ARCHITECTURE Assist. Prof. Dr. Volkan TUNALI Faculty of Engineering and Natural Sciences / Maltepe University Topics 2 Cloud Computing Essential Characteristics
Amazon Web Services Primer William Strickland COP 6938 Fall 2012 University of Central Florida AWS Overview Amazon Web Services (AWS) is a collection of varying remote computing provided by Amazon.com.
Journal of Software Engineering and Applications, 2013, 6, 74-83 http://dx.doi.org/10.4236/jsea.2013.62012 Published Online February 2013 (http://www.scirp.org/journal/jsea) Performance Evaluation Approach
A Scalable Network Monitoring and Bandwidth Throttling System for Cloud Computing N.F. Huysamen and A.E. Krzesinski Department of Mathematical Sciences University of Stellenbosch 7600 Stellenbosch, South
Overview The purpose of this paper is to introduce the reader to the basics of cloud computing or the cloud with the aim of introducing the following aspects: Characteristics and usage of the cloud Realities
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 4, Ver. I (Jul-Aug. 2014), PP 39-46 Auto-Scaling, Load Balancing and Monitoring As service in public
Volume 2, Issue 2, February 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: A COMPARATIVE STUDY OF CLOUD
Relocating Windows Server 2003 Workloads An Opportunity to Optimize From Complex Change to an Opportunity to Optimize There is much you need to know before you upgrade to a new server platform, and time
ASCETiC Whitepaper Motivation The increased usage of ICT, together with growing energy costs and the need to reduce greenhouse gases emissions call for energy-efficient technologies that decrease the overall
Rapid Application Development Chapter 7: Development RAD with CASE tool: App Inventor And Cloud computing Technology Cr: appinventor.org Dr.Orawit Thinnukool College of Arts, Media and Technology, Chiang
Cloud computing - Architecting in the cloud firstname.lastname@example.org 1 Outline Cloud computing What is? Levels of cloud computing: IaaS, PaaS, SaaS Moving to the cloud? Architecting in the cloud Best practices
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 2 (2014), pp. 139-144 International Research Publications House http://www. irphouse.com /ijict.htm Cloud
TORRY HARRIS BUSINESS SOLUTIONS Performance Analysis of webmethods Integrations using Apache JMeter Information Guide for JMeter Adoption Ganapathi Nanjappa 4/28/2010 2010 Torry Harris Business Solutions.
Rapid Bottleneck Identification A Better Way to do Load Testing An Oracle White Paper June 2009 Rapid Bottleneck Identification A Better Way to do Load Testing. RBI combines a comprehensive understanding
E-commerce business. technology. society. Fourth Edition Kenneth C. Laudon Carol Guercio Traver Copyright 2007 Pearson Education, Inc. Slide 4-1 Chapter 4 Building an E-commerce Web Site Copyright 2007
Monitoring Nginx Server eg Enterprise v6 Restricted Rights Legend The information contained in this document is confidential and subject to change without notice. No part of this document may be reproduced
USER S GUIDE CONTENTS Contents... 2 Chapter 1: Welcome to Citus Cloud Load Test... 3 1. What is Citus Cloud Load Test?... 3 2. Why Citus Cloud Load Test?... 3 3. Before using this guide... 3 Chapter 2:
Lets SAAS-ify that Desktop Application Chirag Jog Clogeny 1 About me o Chirag Jog o Computer Science Passout, PICT o Currently CTO at Clogeny Technologies. o Working on some cutting-edge Products in Cloud
PLATFORM-AS-A-SERVICE: ADOPTION, STRATEGY, PLANNING AND IMPLEMENTATION White Paper May 2012 Abstract Whether enterprises choose to use private, public or hybrid clouds, the availability of a broad range
An Oracle White Paper February 2010 Rapid Bottleneck Identification - A Better Way to do Load Testing Introduction You re ready to launch a critical Web application. Ensuring good application performance
Web Applications Engineering: Performance Analysis: Operational Laws Service Oriented Computing Group, CSE, UNSW Week 11 Material in these Lecture Notes is derived from: Performance by Design: Computer
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
4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) Design and Implementation of IaaS platform based on tool migration Wei Ding State Key Laboratory
Building Success on Acquia Cloud: 10 Layers of PaaS TECHNICAL Guide Table of Contents Executive Summary.... 3 Introducing the 10 Layers of PaaS... 4 The Foundation: Five Layers of PaaS Infrastructure...
15-319 / 15-619 Cloud Computing Recitation 5 September 29 th & October 1 st 2015 1 Overview Administrative Stuff Last Week s Reflection Project 2.1, OLI Modules 5 & 6, Quiz 3 New concepts This week s schedule
Approaching the Cloud: Using Palladio for Scalability, Elasticity, and Efficiency Analyses Sebastian Lehrig Software Engineering Chair Chemnitz University of Technology Straße der Nationen 62 09107 Chemnitz,
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
Preparing Your IT for the Holidays A quick start guide to take your e-commerce to the Cloud September 2011 Preparing your IT for the Holidays: Contents Introduction E-Commerce Landscape...2 Introduction
How AWS Pricing Works (Please consult http://aws.amazon.com/whitepapers/ for the latest version of this paper) Page 1 of 15 Table of Contents Table of Contents... 2 Abstract... 3 Introduction... 3 Fundamental
Software Engineering Competence Center TUTORIAL An Introduction to Cloud Computing Concepts Practical Steps for Using Amazon EC2 IaaS Technology Ahmed Mohamed Gamaleldin Senior R&D Engineer-SECC email@example.com
Towards Bridging the Gap Between Scalability and Elasticity Nicolas Ferry 1, Gunnar Brataas 2, Alessandro Rossini 1, Franck Chauvel 1, Arnor Solberg 1 1 Department of Networked Systems and Services, SINTEF,
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
Chapter 1 Cloud Computing Defined In This Chapter Examining the reasons for cloud Understanding cloud types Defining the elements of cloud computing Comparing private and public clouds In a dynamic economic
382 A STUDY OF WORKLOAD CHARACTERIZATION IN WEB BENCHMARKING TOOLS FOR WEB SERVER CLUSTERS Syed Mutahar Aaqib 1, Lalitsen Sharma 2 1 Research Scholar, 2 Associate Professor University of Jammu, India Abstract:
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
Visualisation in the Google Cloud by Kieran Barker, 1 School of Computing, Faculty of Engineering ABSTRACT Providing software as a service is an emerging trend in the computing world. This paper explores
Security Benefits of Cloud Computing FELICIAN ALECU Economy Informatics Department Academy of Economic Studies Bucharest ROMANIA e-mail: firstname.lastname@example.org Abstract: The nature of the Internet is
Introduction AppDynamics for Databases Version 2.9.4 Page 1 Introduction to AppDynamics for Databases.................................... 3 Top Five Features of a Database Monitoring Tool.............................
Platforms in the Cloud Where Will Your Next Application Run? Jazoon, Zurich June 2011 Copyright 2011 Chappell & Associates An Organization without Cloud Computing Users A A A VM VM VM A A A Application
1 of 10 5/4/2011 4:47 PM Chao He email@example.com (A paper written under the guidance of Prof. Raj Jain) Download Cloud computing is recognized as a revolution in the computing area, meanwhile, it also
THE WINDOWS AZURE PROGRAMMING MODEL DAVID CHAPPELL OCTOBER 2010 SPONSORED BY MICROSOFT CORPORATION CONTENTS Why Create a New Programming Model?... 3 The Three Rules of the Windows Azure Programming Model...
Variations in Performance and Scalability when Migrating n-tier Applications to Different Clouds Deepal Jayasinghe, Simon Malkowski, Qingyang Wang, Jack Li, Pengcheng Xiong, Calton Pu Outline Motivation
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,