Due to the heterogeneity and the multigrain parallelism of the heterogeneous multi-core computer, communication and memory access show hierarchical characteristics ignored by other models. In this paper, a new model named mPlogP, is presented on the basis of the PlogP model, in which communication and memory access is abstracted by considering these new characteristics of the heterogeneous multi-core computer. It uses memory access to model the behavior of computation, estimates the execution time of every part of applications and guides the optimization of effective parallel programs. Finally this proposed model is validated by experiments that it can precisely evaluate the execution of parallel applications under the heterogeneous multi-core computer.
{"title":"mPlogP: A Parallel Computation Model for Heterogeneous Multi-core Computer","authors":"Liang Li, Xingjun Zhang, Jinghua Feng, Xiaoshe Dong","doi":"10.1109/CCGRID.2010.60","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.60","url":null,"abstract":"Due to the heterogeneity and the multigrain parallelism of the heterogeneous multi-core computer, communication and memory access show hierarchical characteristics ignored by other models. In this paper, a new model named mPlogP, is presented on the basis of the PlogP model, in which communication and memory access is abstracted by considering these new characteristics of the heterogeneous multi-core computer. It uses memory access to model the behavior of computation, estimates the execution time of every part of applications and guides the optimization of effective parallel programs. Finally this proposed model is validated by experiments that it can precisely evaluate the execution of parallel applications under the heterogeneous multi-core computer.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123952417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatiha Bouabache, T. Hérault, Sylvain Peyronnet, F. Cappello
In grid computing, many scientific and engineering applications require access to large amounts of distributed data. The size and number of these data collections has been growing rapidly in recent years. The costs of data transmission take a significant part of the global execution time. When communication streams flow concurrently on shared links, transport control protocols have issues allocating fair bandwidth to all the streams, and the network becomes sub-optimally used. One way to deal with this situation is to schedule the communications in a way that will induce an optimal use of the network. We focus on the case of large data transfers that can be completely described at the initialization time. In this case, a plan of data migration can be computed at initialization time, and then executed. However, this computation phase must take a small time when compared to the actual execution of the plan. We propose a best effort solution, to compute approximately, based on the uniform random sampling of possible schedules, a communication plan. We show the effectiveness of this approach both theoretically and by simulations.
{"title":"Planning Large Data Transfers in Institutional Grids","authors":"Fatiha Bouabache, T. Hérault, Sylvain Peyronnet, F. Cappello","doi":"10.1109/CCGRID.2010.68","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.68","url":null,"abstract":"In grid computing, many scientific and engineering applications require access to large amounts of distributed data. The size and number of these data collections has been growing rapidly in recent years. The costs of data transmission take a significant part of the global execution time. When communication streams flow concurrently on shared links, transport control protocols have issues allocating fair bandwidth to all the streams, and the network becomes sub-optimally used. One way to deal with this situation is to schedule the communications in a way that will induce an optimal use of the network. We focus on the case of large data transfers that can be completely described at the initialization time. In this case, a plan of data migration can be computed at initialization time, and then executed. However, this computation phase must take a small time when compared to the actual execution of the plan. We propose a best effort solution, to compute approximately, based on the uniform random sampling of possible schedules, a communication plan. We show the effectiveness of this approach both theoretically and by simulations.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125769830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Resource discovery is a challenging issue in unstructured peer-to-peer networks. Blind search approaches, including flooding and random walks, are the two typical algorithms used in such systems. Blind flooding is not scalable because of its high communication cost. On the other hand, the performance of random walks approaches largely depends on the random choice of walks. Some informed mechanisms use additional information, usually obtained from previous queries, for routing. Such approaches can reduce the traffic overhead but they limit the query coverage. Furthermore, they usually rely on complex protocols to maintain information at each peer. In this paper, we propose two schemes which can be used to improve the search performance in unstructured peer-to-peer networks. The first one is a simple caching mechanism based on resource descriptions. Peers that offer resources send periodic advertisement messages. These messages are stored into a cache and are used for routing requests. The second is a dynamic Time-To-Live (TTL) which enables messages to break their horizon. Instead of decreasing the query TTL by 1 at each hop, it is decreased by a value v such as 0
{"title":"Dynamic TTL-Based Search in Unstructured Peer-to-Peer Networks","authors":"Imen Filali, F. Huet","doi":"10.1109/CCGRID.2010.66","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.66","url":null,"abstract":"Resource discovery is a challenging issue in unstructured peer-to-peer networks. Blind search approaches, including flooding and random walks, are the two typical algorithms used in such systems. Blind flooding is not scalable because of its high communication cost. On the other hand, the performance of random walks approaches largely depends on the random choice of walks. Some informed mechanisms use additional information, usually obtained from previous queries, for routing. Such approaches can reduce the traffic overhead but they limit the query coverage. Furthermore, they usually rely on complex protocols to maintain information at each peer. In this paper, we propose two schemes which can be used to improve the search performance in unstructured peer-to-peer networks. The first one is a simple caching mechanism based on resource descriptions. Peers that offer resources send periodic advertisement messages. These messages are stored into a cache and are used for routing requests. The second is a dynamic Time-To-Live (TTL) which enables messages to break their horizon. Instead of decreasing the query TTL by 1 at each hop, it is decreased by a value v such as 0","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124564907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. We propose an efficient resource management policy for virtualized Cloud data centers. The objective is to continuously consolidate VMs leveraging live migration and switch off idle nodes to minimize power consumption, while providing required Quality of Service. We present evaluation results showing that dynamic reallocation of VMs brings substantial energy savings, thus justifying further development of the proposed policy.
{"title":"Energy Efficient Allocation of Virtual Machines in Cloud Data Centers","authors":"A. Beloglazov, R. Buyya","doi":"10.1109/CCGRID.2010.45","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.45","url":null,"abstract":"Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. We propose an efficient resource management policy for virtualized Cloud data centers. The objective is to continuously consolidate VMs leveraging live migration and switch off idle nodes to minimize power consumption, while providing required Quality of Service. We present evaluation results showing that dynamic reallocation of VMs brings substantial energy savings, thus justifying further development of the proposed policy.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.
{"title":"Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS","authors":"Lizhe Wang, G. Laszewski, Jai Dayal, Fugang Wang","doi":"10.1109/CCGRID.2010.19","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.19","url":null,"abstract":"Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126682075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vasilis Sourlas, G. Paschos, P. Flegkas, L. Tassiulas
In a publish/subscribe (pub/sub) network, message delivery is guaranteed for all connected subscribers at publish time. However, in a dynamic mobile scenario where users join and leave the network, it is important that content published at the time they are disconnected is still delivered when they reconnect from a different point. In this paper, we enhance the caching mechanisms in pub/sub networks to enable client mobility. We build our mobility support with minor changes in the caching scheme while preserving the main principles of loose coupled and asynchronous communication of the pub/sub communication model. We also present a new proactive mechanism to reduce the overhead of duplicate responses. The evaluation of our proposed scheme is performed via simulations and testbed measurements.
{"title":"Mobility Support Through Caching in Content-Based Publish/Subscribe Networks","authors":"Vasilis Sourlas, G. Paschos, P. Flegkas, L. Tassiulas","doi":"10.1109/CCGRID.2010.22","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.22","url":null,"abstract":"In a publish/subscribe (pub/sub) network, message delivery is guaranteed for all connected subscribers at publish time. However, in a dynamic mobile scenario where users join and leave the network, it is important that content published at the time they are disconnected is still delivered when they reconnect from a different point. In this paper, we enhance the caching mechanisms in pub/sub networks to enable client mobility. We build our mobility support with minor changes in the caching scheme while preserving the main principles of loose coupled and asynchronous communication of the pub/sub communication model. We also present a new proactive mechanism to reduce the overhead of duplicate responses. The evaluation of our proposed scheme is performed via simulations and testbed measurements.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The MPI programming model hides network type and topology from developers, but also allows them to seamlessly distribute a computational job across multiple cores in both an intra and inter node fashion. This provides for high locality performance when the cores are either on the same node or on nodes closely connected by the same network type. The streaming model splits a computational job into a linear chain of decoupled units. This decoupling allows the placement of job units on optimal nodes according to network topology. Furthermore, the links between these units can be of varying protocols when the application is distributed across a heterogeneous network. In this paper we study how to integrate the MPI and Stream programming models in order to exploit network locality and topology. We present a hybrid MPI-Stream framework that aims to take advantage of each model's strengths. We test our framework with a financial application. This application simulates an electronic market for a single financial instrument. A stream of buy and sell orders is fed into a price matching engine. The matching engine creates a stream of order confirmations, trade confirmations, and quotes based on its attempts to match buyers with sellers. Our results show that the hybrid MPI-Stream framework can deliver a 32% performance improvement at certain order transmission rates.
{"title":"An MPI-Stream Hybrid Programming Model for Computational Clusters","authors":"E. Mancini, Gregory Marsh, D. Panda","doi":"10.1109/CCGRID.2010.33","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.33","url":null,"abstract":"The MPI programming model hides network type and topology from developers, but also allows them to seamlessly distribute a computational job across multiple cores in both an intra and inter node fashion. This provides for high locality performance when the cores are either on the same node or on nodes closely connected by the same network type. The streaming model splits a computational job into a linear chain of decoupled units. This decoupling allows the placement of job units on optimal nodes according to network topology. Furthermore, the links between these units can be of varying protocols when the application is distributed across a heterogeneous network. In this paper we study how to integrate the MPI and Stream programming models in order to exploit network locality and topology. We present a hybrid MPI-Stream framework that aims to take advantage of each model's strengths. We test our framework with a financial application. This application simulates an electronic market for a single financial instrument. A stream of buy and sell orders is fed into a price matching engine. The matching engine creates a stream of order confirmations, trade confirmations, and quotes based on its attempts to match buyers with sellers. Our results show that the hybrid MPI-Stream framework can deliver a 32% performance improvement at certain order transmission rates.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124363426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We develop a novel framework for supporting e-Science applications that require streaming of information between sites. Using a Synchronous Dataflow (SDF) model, our framework incorporates the communication times inherent in large scale distributed applications, and can be used to formulate the bandwidth allocation problem with throughput constraints as a multi-commodity linear programming problem. Our algorithms determine how much bandwidth is allocated to each edge while satisfying temporal constraints on collaborative tasks. Simulation results show that the bandwidth allocation by the formulated linear programming outperforms the bandwidth allocation by simple heuristics.
{"title":"Bandwidth Allocation for Iterative Data-Dependent E-science Applications","authors":"Eun-Sung Jung, S. Ranka, S. Sahni","doi":"10.1109/CCGRID.2010.114","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.114","url":null,"abstract":"We develop a novel framework for supporting e-Science applications that require streaming of information between sites. Using a Synchronous Dataflow (SDF) model, our framework incorporates the communication times inherent in large scale distributed applications, and can be used to formulate the bandwidth allocation problem with throughput constraints as a multi-commodity linear programming problem. Our algorithms determine how much bandwidth is allocated to each edge while satisfying temporal constraints on collaborative tasks. Simulation results show that the bandwidth allocation by the formulated linear programming outperforms the bandwidth allocation by simple heuristics.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127872221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud Computing allows us to abstract distributed, elastic IT resources behind an interface that promotes scalability and dynamic resource allocation. The boundary of this cloud sits outside the application and the hardware that hosts it. For the end user, a web application deployed on a cloud is presented no differently to a web application deployed on a stand-alone web server. This model works well for web applications but fails to cater for distributed applications containing components that execute both locally for the user and remotely using non-local resources. This research proposes extending the concept of the cloud to encompass not only server-farm resources but all resources accessible by the user. This brings the resources of the home PC and personal mobile devices into the cloud and promotes the deployment of highly-distributed component based applications with fat user interfaces. This promotes the use of the Internet itself as a platform. We compare this to the standard Web 2.0 approach and show the benefits that deploying fat-client component based systems provide over classic web applications. We also describe the benefits that expanding the cloud provides to component migration and resources utilisation.
{"title":"Expanding the Cloud: A Component-Based Architecture to Application Deployment on the Internet","authors":"M. Wallis, F. Henskens, M. Hannaford","doi":"10.1109/CCGRID.2010.14","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.14","url":null,"abstract":"Cloud Computing allows us to abstract distributed, elastic IT resources behind an interface that promotes scalability and dynamic resource allocation. The boundary of this cloud sits outside the application and the hardware that hosts it. For the end user, a web application deployed on a cloud is presented no differently to a web application deployed on a stand-alone web server. This model works well for web applications but fails to cater for distributed applications containing components that execute both locally for the user and remotely using non-local resources. This research proposes extending the concept of the cloud to encompass not only server-farm resources but all resources accessible by the user. This brings the resources of the home PC and personal mobile devices into the cloud and promotes the deployment of highly-distributed component based applications with fat user interfaces. This promotes the use of the Internet itself as a platform. We compare this to the standard Web 2.0 approach and show the benefits that deploying fat-client component based systems provide over classic web applications. We also describe the benefits that expanding the cloud provides to component migration and resources utilisation.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Open Fabrics Enterprise Distribution (OFED) is open-source software, committed to provide common communication stack to all RDMA capable System Area Networks (SANs). It supports high performance MPIs and legacy protocols for HPC domain and Data Centre community. Currently, it supports InfiniBand (IB) and Internet Wide Area RDMA Protocol (iWARP). This paper presents a technique to support OFED software stack over non-IB RDMA capable SAN. We propose the design of Virtual Management Port (VMP) to enable IB subnet management model. Integration of VMP with IB-Verbs interface driver prevents hardware and OFED modifications and enables connection manager that is mandatory to run user applications. The performance evaluation shows that VMP is lightweight.
Open Fabrics Enterprise Distribution (OFED)是开源软件，致力于为所有支持RDMA的系统区域网络(san)提供通用通信堆栈。它支持高性能mpi和HPC领域和数据中心社区的遗留协议。目前支持IB (InfiniBand)和iWARP (Internet Wide Area RDMA Protocol)协议。本文提出了一种在非ib RDMA SAN上支持OFED软件栈的技术。我们提出设计虚拟管理端口(VMP)来实现IB子网管理模型。VMP与IB-Verbs接口驱动程序的集成防止了硬件和OFED的修改，并支持运行用户应用程序所必需的连接管理器。性能评估表明VMP是轻量级的。
{"title":"Supporting OFED over Non-InfiniBand SANs","authors":"Devesh Sharma","doi":"10.1109/CCGRID.2010.62","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.62","url":null,"abstract":"Open Fabrics Enterprise Distribution (OFED) is open-source software, committed to provide common communication stack to all RDMA capable System Area Networks (SANs). It supports high performance MPIs and legacy protocols for HPC domain and Data Centre community. Currently, it supports InfiniBand (IB) and Internet Wide Area RDMA Protocol (iWARP). This paper presents a technique to support OFED software stack over non-IB RDMA capable SAN. We propose the design of Virtual Management Port (VMP) to enable IB subnet management model. Integration of VMP with IB-Verbs interface driver prevents hardware and OFED modifications and enables connection manager that is mandatory to run user applications. The performance evaluation shows that VMP is lightweight.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116889128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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