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| Tracking System Solutions' Decision to Deploy on Virsto vDisks |
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| How a Focus on Storage Optimization Enables Aggressively Priced Cloud Offerings from SSI | |
| by Eric Burgener | |
| Because storage makes up such a large part of the costs of any cloud-based infrastructure, it is an obvious place to look for cost reductions. System Solutions, Inc. (SSI), a King of Prussia, Pa.-based IT solutions provider, took that advice to heart in building MySecureCloud, a hosted infrastructure solution targeted at small and medium businesses (SMBs). | |
| read the full story >> | |
| Tracking System Solutions' Decision to Deploy on Virsto vDisks |
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| How a Focus on Storage Optimization Enables Aggressively Priced Cloud Offerings from SSI | |
| by Eric Burgener | |
| Because storage makes up such a large part of the costs of any cloud-based infrastructure, it is an obvious place to look for cost reductions. System Solutions, Inc. (SSI), a King of Prussia, Pa.-based IT solutions provider, took that advice to heart in building MySecureCloud, a hosted infrastructure solution targeted at small and medium businesses (SMBs). | |
| read the full story >> | |
| Cloud Storage and Higher Education |
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| January 11 2011 |
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| See one of my use cases for cloud services in Higher Education. |
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Cloud-Based Disaster Recovery  |
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| December 08 2010 |
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| Many corporate IT departments have used tape technology for data backup and recovery for years. It may be time for a change. Terms such as backup service, cloud backup and data vaulting describe the process of electronically sending data off-site, where it can be protected from hardware failure, unplanned downtime, loss, and other systems risks. In today’s business environment, continuous access to corporate data is a vital step to supporting your external customers and internal users. Gaining access to and providing availability of this data is crucial to support your business continuity plan in the event of a disaster. |
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ElasTraS: An Elastic Transactional Data Store in the Cloud |
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| July 22 2009 |
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| 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. |
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In Search of an API for Scalable File Systems: Under the Table or Above It? |
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| July 22 2009 |
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| 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. |
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| SNIA Cloud Storage |
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| July 22 2009 |
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| SNIA Cloud Storage |
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Open Grid & SNIA Paper on Cloud Storage for Cloud Computing |
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| June 22 2009 |
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| Open Grid & SNIA Paper on Cloud Storage for Cloud Computing |
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Cloud-Based Disaster Recovery |
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| December 08 2010 |
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| Many corporate IT departments have used tape technology for data backup and recovery for years. It may be time for a change. Terms such as backup service, cloud backup and data vaulting describe the process of electronically sending data off-site, where it can be protected from hardware failure, unplanned downtime, loss, and other systems risks. In today’s business environment, continuous access to corporate data is a vital step to supporting your external customers and internal users. Gaining access to and providing availability of this data is crucial to support your business continuity plan in the event of a disaster. |
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ElasTraS: An Elastic Transactional Data Store in the Cloud |
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| July 22 2009 |
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| 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. |
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In Search of an API for Scalable File Systems: Under the Table or Above It? |
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| July 22 2009 |
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| 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. |
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Open Grid & SNIA Paper on Cloud Storage for Cloud Computing |
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| June 22 2009 |
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| Open Grid & SNIA Paper on Cloud Storage for Cloud Computing |
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| Hierarchically-Redundant, Decoupled Storage Project (HaRD) |
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| The Wisconsin Hierarchically-Redundant, Decoupled storage project (HaRD) investigates the next generation of storage software for hybrid Flash/disk storage clusters. The main objective of the project is to improve the performance of storage in a variety of diverse scenarios, including new application environments such as photo storage as found in Facebook and Flickr, high-end scientific processing as found in government labs, and large-scale data processing such as that found in Google and Microsoft. | ||||
| MetaCDN |
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| Content Delivery Networks (CDNs) such as Akamai and Mirror Image place web server clusters in numerous geographical locations to improve the responsiveness and locality of the content it hosts for end-users. However, their services are priced out of reach for all but the largest enterprise customers. An alternative approach to content delivery could be achieved by leveraging existing infrastructure provided by ’storage cloud’ providers, at a fraction of the cost. MetaCDN is a system that leverages several existing ’storage clouds’, creating an integrated overlay network that provides a low cost, high performance content delivery network for content creators. MetaCDN intelligently places content onto one or many storage providers based on the quality of service, coverage and budget preferences of participants. | ||||
| Tsinghua Cloud |
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| Tsinghua Cloud is an all-in-one Cloud Computing solution developed at the Grid Computing Division of Tsinghua University in China. The Cloud is composed of three components: Nova (virtual computation system: computing cloud), Carrier (distributed file system) and Corsair (distributed file manager based on Carrier: storage cloud), which can be utilized independently or in combination. | ||||
| VISION Cloud |
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| VISION Cloud (Virtualized Storage Services Foundation for the Future Internet) is a research project with the objective to advance the competitiveness of the EU economy by introducing a powerful ICT infrastructure for reliable and effective delivery of data-intensive storage services, facilitating the convergence of ICT, media and telecommunications. | ||||
| The Cloud Storage Conundrum |
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| April 08 2011 - CTOEdge | ||||
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| read the full article >> | ||||