ContentCopyright © 2013 cloudbook: The Cloud Computing & SaaS Information Resource. All Rights Reserved.
 |
||||||||||
| login | | | About | | | Contact Us | ||||||
 |
|
|
|
|
|
|
|
|
| Featured Stories |
| Contributions |
| Videos |
| Papers |
| Research |
| News |
| Cloud Computing Technology with BPOS and Windows Azure |
|
| by Nidhi Bansal | |
| We see cloud computing offerings today that are suitable to host enterprise architectures. But while these offerings provide clear benefits to corporations by providing capabilities complementary to what they have, the fact that they can help to elastically scale enterprise architectures should not be understood to also mean that simply scaling in this way will meet twenty-first-century computing requirements. | |
| read the full story >> | |
Infrastructure Management and Monitoring in Hybrid Cloud Environment  |
|
|||
| September 30 2012 |
||||
|
||||
| In today’s scenario, enterprises are using diverse technology platforms for their application portfolios, and as part of their Cloud journey, they are adopting multiple Clouds. This paper provides an approach for unified management and monitoring of enterprise’s infrastructure, platforms, and applications deployed in a hybrid environment comprising of Public and Private Cloud platforms offered by different vendors. |
||||
| The Pop-Up Cloud |
|
|||
| November 22 2011 |
||||
|
||||
| How the cloud can support your limited life application needs! |
||||
| Applying ITIL to Cloud Operations |
|
|||
| August 25 2011 |
||||
|
||||
| Modern Enterprises entail many challenges like scalability, availability, agility, shorter time to market, increasing complexity, etc. Enterprises are adopting cloud to handle these challenges as well as derive business benefits by cost transformation and increased revenue. With cloud gaining momentum enterprise now have to manage applications in different environments namely on-premise and cloud. ITIL is a standard process that organizations follow in managing their data centers. They need to now see how ITIL can be applied to cloud operations without impacting the current on-premise set-up.
This paper is our experience report on making ITIL services cloud aware. We have come up with a platform for managing hybrid environment. We have configured ITSM tool to add cloud elements as configuration items and implemented the complete life cycle for various manageable items. We close this paper with a note on related work like Continuous delivery, DevOps and Behavior Driven Operations and how this platform is a step towards achieving these goals and helping business become more agile. |
||||
| Scaling Out |
|
|||
| May 03 2011 |
||||
|
||||
| This article describes some key issues in scaling on the cloud and the solutions to this problem. |
||||
| PHP’s Forecast: Partly Cloudy |
|
|||
| June 22 2010 |
||||
|
||||
| You’ve probably heard about “the cloud”. It’s the latest buzzword circulating in Silicon Valley and beyond. In some ways, “cloud” is a fitting name - everyone sees something different when they look at it. For example, offerings as dissimilar as force.com and Amazon’s MapReduce service have worked their way in to “the cloud” moniker at some point. Even the cloud’s biggest advocates can’t agree on what “the cloud” is. But in teasing the facts out of all the hype, you’ll find some services that could change how you write and deploy your PHP applications forever. |
||||
| NASA's Nebula Rolls Out in the Cloud |
|
|||
| March 25 2010 |
||||
|
||||
| Agencies across the federal government are exploring cloud computing, but NASA's Nebula Cloud Computing Platform could become the model for its use. The Chief Information Officer at NASA Ames Research Center, explains the benefits of the Nebula platform. |
||||
| Some Thoughts on Cloud Adoption Methodology |
|
|||
| February 26 2010 |
||||
|
||||
| Cloud data centers focus on two main areas: time to market and cost management. Technically it provides an abstract platform to deploy services and APIs to manipulate those services. The enabler is automation and the ability to systematically optimize the platforms and services. Granted the focus here is moving forward with a transformational platform but its useful to look backwards to address other challenges beyond technology. |
||||
| Don't Pass on PaaS in 2010 |
|
|||
| February 22 2010 |
||||
|
||||
| There is no question that enterprises today are looking to the clouds to create efficiencies, gain competitive advantage and realize significant cost savings. Where the debate often emerges is around the cloud approach selected and employed - namely, Infrastructure as a Service (IaaS) or Platform as a Service (PaaS). While IaaS solutions are interesting, they fail to fully deliver on the scalability, elasticity and cost improvements enterprises seek to gain by moving applications to the cloud. This is due to the fact that the tru costs of delivering applications and services in a cloud environment relate not to the racking and stacking of infrastructure, but rather to the development, deployment and management of applications - and only PaaS solutions can address these costs. Here is a look at what exactly a platform-oriented cloud solution provides. |
||||
| Application Packaging for Cloud Computing: A Proposal |
|
|||
| January 05 2010 |
||||
|
||||
| Here is a vision of what software packaging architectures in cloud computing environments may look like. Specifically, Infrastructure-as-a-Service (IaaS) and Platform as a Service (PaaS) offerings, and the enabling infrastructure that will handle application deployment to these services in the future. How they may possibly evolve to make deployment and operations as easy as possible. |
||||
| Overcast Show: Focus on PaaS |
|
|||
| December 11 2009 |
||||
|
||||
| James and Geva catch up on some of the latest developments in cloud computing, with a particular emphasis on Platform-as-a-Service. Some of the companies, products and technologies mentioned include: Amazon, Salesforce.com, Engine Yard, Heroku, Canonical, Eucalyptus, Chef, Sauce Labs and Microsoft Azure. |
||||
| Optimizing Applications for Cloud Computing Environments |
|
|||
| November 19 2009 |
||||
|
||||
| Cloud Computing can help you reduce costs, increase flexibility, and reduce risk. You can leverage the cloud to host applications ranging from the business critical to the experimental. But not all applications are suited for cloud computing environments. When deciding whether and how to move an application to the cloud, you must first assess the expected risks and rewards. Once you've determined that an application can run in the cloud, you next should determine whether it can be further optimized to harness the energy of the cloud. This paper presents a methodology for determining when and how to refactor applications for cloud computing environments. |
||||
| A Performance and Usability Comparison of Hadoop and Relational Database Systems |
|
|||
| October 22 2009 |
||||
|
||||
| A Performance and Usability Comparison of Hadoop and Relational Database Systems |
||||
| HadooDB An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads |
|
|||
| October 22 2009 |
||||
|
||||
| HadooDB An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads |
||||
| A Comparison of Approaches to Large-Scale Data Analysis |
|
|||
| October 22 2009 |
||||
|
||||
| A Comparison of Approaches to Large-Scale Data Analysis |
||||
| Large-Scale Data Cleaning Using Hadoop |
|
|||
| October 22 2009 |
||||
|
||||
| Large-Scale Data Cleaning Using Hadoop |
||||
| Relaxed Synchronization and Eager Scheduling in MapReduce |
|
|||
| October 22 2009 |
||||
|
||||
| Relaxed Synchronization and Eager Scheduling in MapReduse |
||||
| The Purpose Driven Cloud Part 2 - Technology Framework |
|
|||
| September 22 2009 |
||||
|
||||
| Framework based cloud platforms are designed to be used with a specific programming language, product or technology stack. They enable developers who subscribe to that particular programming model to ficus on the innovative aspect of their application and let the platform handle the problems of scalability, reliability, and performance (and in some cases, integration). |
||||
| ElasTraS: An Elastic Transactional Data Store in the Cloud |
|
|||
| July 22 2009 |
||||
|
||||
| By Sudipto Das, Divyakant Agrawal, and Amr El Abbadi.
Abstract: Over the last couple of years, “Cloud Computing” or “Elastic Computing” has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud. |
||||
| Towards Optimizing Hadoop Provisioning in the Cloud |
|
|||
| July 22 2009 |
||||
|
||||
| By Karthik Kambatla and Abhinav Pathak, Purdue University; Himabindu Pucha, IBM Research Almaden.
Abstract: Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs, given its ability to scale-out and leverage several machines to parallely process data. In this work we argue that such MapReduce-based analytics are particularly synergistic with the pay-as-you-go model of a cloud platform. However, a key challenge facing end-users in this environment is the ability to provision MapReduce applications to minimize the incurred cost, while obtaining the best performance. This paper firstmotivates the importance of optimally provisioning a MapReduce job, and demonstrates that existing approaches can result in far from optimal provisioning. We then present a preliminary approach that improves MapReduce provisioning by analyzing and comparing resource consumption of the application at hand with a database of similar resource consumption signatures of other applications. |
||||
| In Search of an API for Scalable File Systems: Under the Table or Above It? |
|
|||
| July 22 2009 |
||||
|
||||
| By Swapnil Patil, Garth A Gibson, Gregory R Ganger, Julio Lopez, Milo Polte, Wittawat Tantisiroj, and Lin Xiao.
Abstract: “Big Data” is everywhere – both the IT industry and the scientific computing community are routinely handling terabytes to petabytes of data. This preponderance of data has fueled the development of data-intensive scalable computing (DISC) systems that manage, process and store massive data-sets in a distributed manner. For example, Google and Yahoo have built their respective Internet services stack to distribute processing (MapReduce and Hadoop), to program computation (Sawzall and Pig) and to store the structured output data (Bigtable and HBase). Both these stacks are layered on their respective distributed file systems, GoogleFS and Hadoop distributed FS, that are designed “from scratch” to deliver high performance primarily for their anticipated DISC workloads. However, cluster file systems have been used by the high performance computing (HPC) community at even larger scales for more than a decade. These cluster file systems, including IBM GPFS, Panasas PanFS, PVFS and Lustre, are required to meet the scalability demands of highly parallel I/O access patterns generated by scientific applications that execute simultaneously on tens to hundreds of thousands of nodes.
Thus, given the importance of scalable storage to both the DISC and the HPC world, we take a step back and ask ourselves if we are at a point where we can distill the key commonalities of these scalable file systems. This is not a paper about engineering yet another “right” file system or database, but rather about how do we evolve the most dominant data storage API – the file system interface – to provide the right abstraction for both DISC and HPC applications. What structures should be added to the file system to enable highly scalable and highly concurrent storage? Our goal is not to define the API calls per se, but to identify the file system abstractions that should be exposed to programmers to make their applications more powerful and portable. This paper highlights two such abstractions. First, we show how commodity large-scale file systems can support distributed data processing enabled by the Hadoop/MapReduce style of parallel programming frameworks. And second, we argue for an abstraction that supports indexing and searching based on extensible attributes, by interpreting BigTable as a file system with a filtered directory scan interface. |
||||
| Run Your Core Business in the Cloud: SuiteCloud |
|
|||
| October 25 2010 |
||||
|
||||
| NetSuite CEO Zach Nelson provides an introduction to SuiteCloud. |
||||
|
|
||||
| Learn How to Connect Java to the Cloud with jclouds |
|
|||
| July 07 2010 |
||||
|
||||
| The founder of jclouds, Adrian Cole, explains how to connect Java apps to the cloud. His open-source framework makes it possible to write cloud-portable apps for Amazon, VMWare, Azure, and Rackspace. |
||||
| An Introduction to Heroku |
|
|||
| June 24 2010 |
||||
|
||||
| Heroku Co-Founder, James Lindenbaum, introduces us to Heroku, and Heroku's focus as an application platform. |
||||
|
|
||||
| Cloud Abstractions |
|
|||
| June 24 2010 |
||||
|
||||
| James Lindenbaum, CEO and Co-Founder of Heroku, discusses various distractions at play in the Cloud Computing market, including platform abstraction. |
||||
|
|
||||
| Heroku Developer Example |
|
|||
| June 24 2010 |
||||
|
||||
| Heroku Co-Founder, James Lindenbaum, provides an example of how a developer can utilize Heroku's application platform. |
||||
|
|
||||
| Ecosystems are the New Killer Apps |
|
|||
| November 09 2009 |
||||
|
||||
| The CEO of Wavemaker Software discusses Ecosystems as the New Killer Apps at SIIA OnDemand. |
||||
| Owen O'Malley on the Future of MapReduce |
|
|||
| August 27 2009 |
||||
|
||||
| Owen O'Malley, Hadoop architect at Yahoo, describes the current state of affairs for MapReduce jobs in Hadoop, and walks through a variety of approaches that are being implemented to ensure better forward and backward compatibility. |
||||
| Microsoft on the Windows Azure Platform |
|
|||
| July 14 2009 |
||||
|
||||
| Doug Hauger, GM for Windows Azure at Microsoft, shares details of the business and channel model and pricing for windows Azure, SQL Azure, and .NET Services at WPC09. |
||||
| Talking with Joyent's Director of Platform Strategy |
|
|||
| June 23 2009 |
||||
|
||||
| Dell's Cloud Computing Evangelist, Barton George, talks to James Duncan, Director of Platform Strategy at Joyent. |
||||
| Joyent's JavaScript Smart Platform as a Service at the JSConf |
|
|||
| April 24 2009 |
||||
|
||||
| Joyent's Smart Platform is an open source Platform-as-a-Service, letting developers build scalable, complex web applications in JavaScript that runs on the server, and then deploy those applications into the Cloud with Git. James outlines some of the economic imperatives for the rise of Platform-as-a-Service, discusses the need for not only open standards but open source and demonstrates some of the Smart Platform's capabilities. |
||||
| Infrastructure Management and Monitoring in Hybrid Cloud Environment |
|
|||
| September 30 2012 |
||||
|
||||
| In today’s scenario, enterprises are using diverse technology platforms for their application portfolios, and as part of their Cloud journey, they are adopting multiple Clouds. This paper provides an approach for unified management and monitoring of enterprise’s infrastructure, platforms, and applications deployed in a hybrid environment comprising of Public and Private Cloud platforms offered by different vendors. |
||||
| Applying ITIL to Cloud Operations |
|
|||
| August 25 2011 |
||||
|
||||
| Modern Enterprises entail many challenges like scalability, availability, agility, shorter time to market, increasing complexity, etc. Enterprises are adopting cloud to handle these challenges as well as derive business benefits by cost transformation and increased revenue. With cloud gaining momentum enterprise now have to manage applications in different environments namely on-premise and cloud. ITIL is a standard process that organizations follow in managing their data centers. They need to now see how ITIL can be applied to cloud operations without impacting the current on-premise set-up.
This paper is our experience report on making ITIL services cloud aware. We have come up with a platform for managing hybrid environment. We have configured ITSM tool to add cloud elements as configuration items and implemented the complete life cycle for various manageable items. We close this paper with a note on related work like Continuous delivery, DevOps and Behavior Driven Operations and how this platform is a step towards achieving these goals and helping business become more agile. |
||||
| Optimizing Applications for Cloud Computing Environments |
|
|||
| November 19 2009 |
||||
|
||||
| Cloud Computing can help you reduce costs, increase flexibility, and reduce risk. You can leverage the cloud to host applications ranging from the business critical to the experimental. But not all applications are suited for cloud computing environments. When deciding whether and how to move an application to the cloud, you must first assess the expected risks and rewards. Once you've determined that an application can run in the cloud, you next should determine whether it can be further optimized to harness the energy of the cloud. This paper presents a methodology for determining when and how to refactor applications for cloud computing environments. |
||||
| A Comparison of Approaches to Large-Scale Data Analysis |
|
|||
| October 22 2009 |
||||
|
||||
| A Comparison of Approaches to Large-Scale Data Analysis |
||||
| ElasTraS: An Elastic Transactional Data Store in the Cloud |
|
|||
| July 22 2009 |
||||
|
||||
| By Sudipto Das, Divyakant Agrawal, and Amr El Abbadi.
Abstract: Over the last couple of years, “Cloud Computing” or “Elastic Computing” has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud. |
||||
| Towards Optimizing Hadoop Provisioning in the Cloud |
|
|||
| July 22 2009 |
||||
|
||||
| By Karthik Kambatla and Abhinav Pathak, Purdue University; Himabindu Pucha, IBM Research Almaden.
Abstract: Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs, given its ability to scale-out and leverage several machines to parallely process data. In this work we argue that such MapReduce-based analytics are particularly synergistic with the pay-as-you-go model of a cloud platform. However, a key challenge facing end-users in this environment is the ability to provision MapReduce applications to minimize the incurred cost, while obtaining the best performance. This paper firstmotivates the importance of optimally provisioning a MapReduce job, and demonstrates that existing approaches can result in far from optimal provisioning. We then present a preliminary approach that improves MapReduce provisioning by analyzing and comparing resource consumption of the application at hand with a database of similar resource consumption signatures of other applications. |
||||
| In Search of an API for Scalable File Systems: Under the Table or Above It? |
|
|||
| July 22 2009 |
||||
|
||||
| By Swapnil Patil, Garth A Gibson, Gregory R Ganger, Julio Lopez, Milo Polte, Wittawat Tantisiroj, and Lin Xiao.
Abstract: “Big Data” is everywhere – both the IT industry and the scientific computing community are routinely handling terabytes to petabytes of data. This preponderance of data has fueled the development of data-intensive scalable computing (DISC) systems that manage, process and store massive data-sets in a distributed manner. For example, Google and Yahoo have built their respective Internet services stack to distribute processing (MapReduce and Hadoop), to program computation (Sawzall and Pig) and to store the structured output data (Bigtable and HBase). Both these stacks are layered on their respective distributed file systems, GoogleFS and Hadoop distributed FS, that are designed “from scratch” to deliver high performance primarily for their anticipated DISC workloads. However, cluster file systems have been used by the high performance computing (HPC) community at even larger scales for more than a decade. These cluster file systems, including IBM GPFS, Panasas PanFS, PVFS and Lustre, are required to meet the scalability demands of highly parallel I/O access patterns generated by scientific applications that execute simultaneously on tens to hundreds of thousands of nodes.
Thus, given the importance of scalable storage to both the DISC and the HPC world, we take a step back and ask ourselves if we are at a point where we can distill the key commonalities of these scalable file systems. This is not a paper about engineering yet another “right” file system or database, but rather about how do we evolve the most dominant data storage API – the file system interface – to provide the right abstraction for both DISC and HPC applications. What structures should be added to the file system to enable highly scalable and highly concurrent storage? Our goal is not to define the API calls per se, but to identify the file system abstractions that should be exposed to programmers to make their applications more powerful and portable. This paper highlights two such abstractions. First, we show how commodity large-scale file systems can support distributed data processing enabled by the Hadoop/MapReduce style of parallel programming frameworks. And second, we argue for an abstraction that supports indexing and searching based on extensible attributes, by interpreting BigTable as a file system with a filtered directory scan interface. |
||||
| Mochi: Visual Log-Analysis Based Tools for Debugging Hadoop |
|
|||
| July 22 2009 |
||||
|
||||
| By Jiaqi Tan, Xinghao Pan, Soila Kavulya, Rajeev Gandhi, and Priya Narasimhan.
Abstract: Mochi, a new visual, log-analysis based debugging tool correlates Hadoop’s behavior in space, time and volume, and extracts a causal, unified control- and data- flow model of Hadoop across the nodes of a cluster. Mochi’s analysis produces visualizations of Hadoop’s behavior using which users can reason about and debug performance issues. We provide examples of Mochi’s value in revealing a Hadoop job’s structure, in optimizing real-world workloads, and in identifying anomalous Hadoop behavior, on the Yahoo! M45 Hadoop cluster. |
||||
| Cloud Servers Developer Guide |
|
|||
| July 14 2009 |
||||
|
||||
| This document is intended for software developers interested in developing applications using the Cloud Servers Application Programming Interface (API). |
||||
| Cloud Computing Infrastructure and Architecture |
|
|||
| June 02 2009 |
||||
|
||||
| Cloud computing promises to speed application deployment, increase innovation, and lower costs, all while increasing business agility. But it also transforms the way we design, build, and deliver applications. What are the architectural considerations that enterprises must make when adopting cloud computing technology? This white paper discusses the nature of cloud computing and how it is transforming the way that enterprises everywhere build and deploy applications. It proceeds to discuss the architectural considerations that cloud architects must make when designing cloud-based applications, and concludes with a discussion of Sun's technologies that support cloud computing. Also Available in PDF Format |
||||
| A Comparative Study of Approaches to Cluster-Based Large Scale Data Analysis |
|
|||
|
||||
| This is a collaborative study being conducted by MIT, University of Wisconsin, and Yale University. These three universities are using a National Science Foundation CLuE grants for a comparative study of approaches to cluster-based, large-scale data analysis. Both MapReduce and parallel database systems provide scalable data processing over hundreds to thousands of nodes, yet it's important for researchers to know the differences in performance and scalability of these two approaches to know which is more suitable when designing new data-intensive computing applications. This project is engaged in systems research, much of which requires the ability to change the operating environment. Since this is not possible on the IBM/Google cluster, the project is also hosted on the Cloud Computi .... | ||||
| A Hadoop Toolkit for Distributed Text Retrieval |
|
|||
|
||||
| Text search is a technology that is vital for modern information-based societies. Today's systems face the daunting challenge of handling quantities of text previously unimaginable. Cluster computing is the only practical solution for addressing the issue of scale. This project leverages the MapReduce framework (via the open-source Hadoop implementation) to tackle issues of robustness and scalability in processing large amounts of data for information retrieval applications. | ||||
| Aneka |
|
|||
|
||||
| Aneka is a market oriented Cloud development and management platform with rapid application development and workload distribution capabilities. Aneka is an integrated middleware package which allows you to seamlessly build and manage an interconnected network in addition to accelerating development, deployment and management of distributed applications using Microsoft .NET frameworks on these networks. It is market oriented since it allows you to build, schedule, provision and monitor results using pricing, accounting, QoS/SLA services in private and/or public (leased) network environments. | ||||
| AppScale |
|
|||
|
||||
| AppScale is an open-source implementation of the Google AppEngine cloud computing interface from the RACELab at UC Santa Barbara. AppScale enables execution of GAE applications on virtualized cluster systems. In Particular, AppScale enables users to execute GAE applications using their own clusters with greater scalability and reliability than the GAE SDK provides. Moreover, AppScale executes automatically and transparently over cloud infrastructures such as the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) and Eucalyptus, the open-source implementation of the AWS interfaces. .... | ||||
| Eager Maps and Lazy Folds for Graph Structured Applications |
|
|||
|
||||
| This project is investigating linguistic extensions to MapReduce abstractions for programming modern, large-scale systems, with special focus on applications that manipulate large, unstructured graphs. This will impact a broad class of scientific applications. | ||||
| The FLAMINGO Project on Data Cleaning |
|
|||
|
||||
| Abstract: In many applications, data-quality issues resulting from a variety of errors create inconsistencies in structures, representations or semantics. Dealing with these issues is becoming increasingly important as the value of data being processed increases. This project is providing support for efficient fuzzy queries on large text repositories. Supporting fuzzy queries can ultimately help applications mitigate their data quality issues because entities with different representations can be matched. .... | ||||
| Cloud computing underwhelms PHP developers |
|
|||
| November 02 2010 - InfoWorld | ||||
|
||||
| read the full article >> | ||||