Hyunjoo Kim, Shivangi Chaudhari, M. Parashar, Christopher Marty
In todays turbulent market conditions, the ability to generate accurate and timely risk measures has become critical to operating successfully, and necessary for survival. Value-at-Risk (VaR) is a market standard risk measure used by senior management and regulators to quantify the risk level of a firm's holdings. However, the time-critical nature and dynamic computational workloads of VaR applications, make it essential for computing infrastructures to handle bursts in computing and storage resources needs. This requires on-demand scalability, dynamic provisioning, and the integration of distributed resources. While emerging utility computing services and clouds have the potential for cost-effectively supporting such spikes in resource requirements, integrating clouds with computing platforms and data centers, as well as developing and managing applications to utilize the platform remains a challenge. In this paper, we focus on the dynamic resource requirements of online risk analytics applications and how they can be addressed by cloud environments. Specifically, we demonstrate how the CometCloud autonomic computing engine can support online multi-resolution VaR analytics using and integration of private and Internet cloud resources.
{"title":"Online Risk Analytics on the Cloud","authors":"Hyunjoo Kim, Shivangi Chaudhari, M. Parashar, Christopher Marty","doi":"10.1109/CCGRID.2009.82","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.82","url":null,"abstract":"In todays turbulent market conditions, the ability to generate accurate and timely risk measures has become critical to operating successfully, and necessary for survival. Value-at-Risk (VaR) is a market standard risk measure used by senior management and regulators to quantify the risk level of a firm's holdings. However, the time-critical nature and dynamic computational workloads of VaR applications, make it essential for computing infrastructures to handle bursts in computing and storage resources needs. This requires on-demand scalability, dynamic provisioning, and the integration of distributed resources. While emerging utility computing services and clouds have the potential for cost-effectively supporting such spikes in resource requirements, integrating clouds with computing platforms and data centers, as well as developing and managing applications to utilize the platform remains a challenge. In this paper, we focus on the dynamic resource requirements of online risk analytics applications and how they can be addressed by cloud environments. Specifically, we demonstrate how the CometCloud autonomic computing engine can support online multi-resolution VaR analytics using and integration of private and Internet cloud resources.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121502379","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 propose information aggregation as a method for summarizing the resource-related information, used by the task scheduler. Through this method the information of a set of resources can be uniformly represented, reducing at the same time the amount of information transferred in a Grid network. A number of techniques are described for aggregating the information of the resources belonging to a hierarchical Grid domain. This information includes the cpu and storage capacities at a site, the number of tasks queued, and other resource-related parameters. The quality of the aggregation scheme affects the efficiency of the scheduler’s decisions. We use as a metric of aggregation efficiency the Stretch Factor (SF), defined as the ratio of the task delay when the task is scheduled using complete resource information over the task delay when an aggregation scheme is used. The simulation experiments performed show that the proposed aggregation schemes achieve large information reduction, while enabling good task scheduling decisions as indicated by the SF achieved.
{"title":"Resource Information Aggregation in Hierarchical Grid Networks","authors":"P. Kokkinos, Emmanouel Varvarigos","doi":"10.1109/CCGRID.2009.63","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.63","url":null,"abstract":"We propose information aggregation as a method for summarizing the resource-related information, used by the task scheduler. Through this method the information of a set of resources can be uniformly represented, reducing at the same time the amount of information transferred in a Grid network. A number of techniques are described for aggregating the information of the resources belonging to a hierarchical Grid domain. This information includes the cpu and storage capacities at a site, the number of tasks queued, and other resource-related parameters. The quality of the aggregation scheme affects the efficiency of the scheduler’s decisions. We use as a metric of aggregation efficiency the Stretch Factor (SF), defined as the ratio of the task delay when the task is scheduled using complete resource information over the task delay when an aggregation scheme is used. The simulation experiments performed show that the proposed aggregation schemes achieve large information reduction, while enabling good task scheduling decisions as indicated by the SF achieved.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114933895","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 ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The GStrAP system, embedding the StrAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administratorwith a consolidated view of the workload, enabling the visual inspection of its long-term trends.
{"title":"Multi-scale Real-Time Grid Monitoring with Job Stream Mining","authors":"Xiangliang Zhang, M. Sebag, C. Germain","doi":"10.1109/CCGRID.2009.20","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.20","url":null,"abstract":"The ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The GStrAP system, embedding the StrAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administratorwith a consolidated view of the workload, enabling the visual inspection of its long-term trends.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132444563","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}
Yingwen Song, Yoshio Tanaka, H. Takemiya, A. Nakano, S. Ogata, S. Sekiguchi
The understanding of H diffusion in materials is pivotal to designing suitable processes. Though a nudged elastic band (NEB)+molecular dynamics (MD)/quantum mechanics (QM) algorithm has been developed to simulate H diffusion in materials by our group, it is often not computationally feasible for large-scale models on a conventional single system. We thus gridify the NEB+MD/QM algorithm on the top of an integrated framework developed by our group. A two days simulation on H diffusion in alumina has been successfully carried out over a Trans-Pacific Grid infrastructure consisting of supercomputers provided by TeraGrid and AIST. In this paper, we describe the NEB+MD/QM algorithm, briefly introduce the framework middleware, present the grid enablement work, and report the techniques to achieve fault-tolerance and load-balance for sustainable simulation. We believe our experience is of benefit to both middleware developers and application users.
{"title":"The Grid Enablement and Sustainable Simulation of Multiscale Physics Applications","authors":"Yingwen Song, Yoshio Tanaka, H. Takemiya, A. Nakano, S. Ogata, S. Sekiguchi","doi":"10.1109/CCGRID.2009.33","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.33","url":null,"abstract":"The understanding of H diffusion in materials is pivotal to designing suitable processes. Though a nudged elastic band (NEB)+molecular dynamics (MD)/quantum mechanics (QM) algorithm has been developed to simulate H diffusion in materials by our group, it is often not computationally feasible for large-scale models on a conventional single system. We thus gridify the NEB+MD/QM algorithm on the top of an integrated framework developed by our group. A two days simulation on H diffusion in alumina has been successfully carried out over a Trans-Pacific Grid infrastructure consisting of supercomputers provided by TeraGrid and AIST. In this paper, we describe the NEB+MD/QM algorithm, briefly introduce the framework middleware, present the grid enablement work, and report the techniques to achieve fault-tolerance and load-balance for sustainable simulation. We believe our experience is of benefit to both middleware developers and application users.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461088","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}
This paper presents a framework that extends traditional visualization for supporting distributed visualization in Grid environments. This framework adopts the emerging Web Services Resource Framework (WSRF) to deploy visualization algorithms as Web Service on Grid nodes. These visualization algorithms are developed by Visualization ToolKit (VTK) library. Triana, an open source problem solving environment, is used to fulfill a user’s requests for any deployed visualization algorithm on local or remote Computing node. And Ganglia, a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids, by getting state information of each Grid node, can help a user to select the appropriate Grid nodes as Computing nodes executing distributed visualization tasks. To evaluate the feasibility of the proposed framework, a case study is presented.
{"title":"WSRF-Based Distributed Visualization","authors":"Yi Liu, Shu Gao","doi":"10.1109/CCGRID.2009.64","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.64","url":null,"abstract":"This paper presents a framework that extends traditional visualization for supporting distributed visualization in Grid environments. This framework adopts the emerging Web Services Resource Framework (WSRF) to deploy visualization algorithms as Web Service on Grid nodes. These visualization algorithms are developed by Visualization ToolKit (VTK) library. Triana, an open source problem solving environment, is used to fulfill a user’s requests for any deployed visualization algorithm on local or remote Computing node. And Ganglia, a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids, by getting state information of each Grid node, can help a user to select the appropriate Grid nodes as Computing nodes executing distributed visualization tasks. To evaluate the feasibility of the proposed framework, a case study is presented.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122988306","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}
In large-scale clusters and computational grids, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs have become an increasingly important concern to system designers and administrators. In this paper, we study how to efficiently utilize system resources for high-availability clusters with the support of the virtual machine (VM) technology. We design a reconfigurable distributed virtual machine (RDVM) infrastructure for clusters computing. We propose failure-aware node selection strategies for the construction and reconfiguration of RDVMs. We leverage the proactive failure management techniques in calculating nodes' reliability status. We consider both the performance and reliability status of compute nodes in making selection decisions. We define a capacity-reliability metric to combine the effects of both factors in node selection, and propose Best-fit algorithms to find the best qualified nodes on which to instantiate VMs to run parallel jobs. We have conducted experiments using failure traces from production clusters and the NAS Parallel Benchmark programs on a real cluster. The results show the enhancement of system productivity and dependability by using the proposed strategies. With the Best-fit strategies, the job completion rate is increased by 17.6% compared with that achieved in the current LANL HPC cluster, and the task completion rate reaches 91.7%.
{"title":"Failure-Aware Construction and Reconfiguration of Distributed Virtual Machines for High Availability Computing","authors":"S. Fu","doi":"10.1109/CCGRID.2009.21","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.21","url":null,"abstract":"In large-scale clusters and computational grids, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operation costs have become an increasingly important concern to system designers and administrators. In this paper, we study how to efficiently utilize system resources for high-availability clusters with the support of the virtual machine (VM) technology. We design a reconfigurable distributed virtual machine (RDVM) infrastructure for clusters computing. We propose failure-aware node selection strategies for the construction and reconfiguration of RDVMs. We leverage the proactive failure management techniques in calculating nodes' reliability status. We consider both the performance and reliability status of compute nodes in making selection decisions. We define a capacity-reliability metric to combine the effects of both factors in node selection, and propose Best-fit algorithms to find the best qualified nodes on which to instantiate VMs to run parallel jobs. We have conducted experiments using failure traces from production clusters and the NAS Parallel Benchmark programs on a real cluster. The results show the enhancement of system productivity and dependability by using the proposed strategies. With the Best-fit strategies, the job completion rate is increased by 17.6% compared with that achieved in the current LANL HPC cluster, and the task completion rate reaches 91.7%.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126843565","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}
Federated storage resources in geographically distributed environments are becoming viable platforms for data-intensive cloud and grid applications. To improveI /O performance in such environments, we propose a novel model-based I/O performance optimization algorithm for data-intensive applications running on a virtual cluster, which determines virtual machine (VM) migration strategies,i.e., when and where a VM should be migrated, while minimizing the expected value of file access time. We solve this problem as a shortest path problem of a weighted direct acyclic graph (DAG), where the weighted vertex represents a location of a VM and expected file access time from the location, and the weighted edge represents a migration of a VM and time. We construct the DAG from our markov model which represents the dependency of files. Our simulation-based studies suggest that our proposed algorithm can achieve higher performance than simple techniques, such as ones that never migrate VMs: 38% or always migrate VMs onto the locations that hold target files: 47%.
{"title":"A Model-Based Algorithm for Optimizing I/O Intensive Applications in Clouds Using VM-Based Migration","authors":"Kento Sato, Hitoshi Sato, S. Matsuoka","doi":"10.1109/CCGRID.2009.24","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.24","url":null,"abstract":"Federated storage resources in geographically distributed environments are becoming viable platforms for data-intensive cloud and grid applications. To improveI /O performance in such environments, we propose a novel model-based I/O performance optimization algorithm for data-intensive applications running on a virtual cluster, which determines virtual machine (VM) migration strategies,i.e., when and where a VM should be migrated, while minimizing the expected value of file access time. We solve this problem as a shortest path problem of a weighted direct acyclic graph (DAG), where the weighted vertex represents a location of a VM and expected file access time from the location, and the weighted edge represents a migration of a VM and time. We construct the DAG from our markov model which represents the dependency of files. Our simulation-based studies suggest that our proposed algorithm can achieve higher performance than simple techniques, such as ones that never migrate VMs: 38% or always migrate VMs onto the locations that hold target files: 47%.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"575 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086019","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}
This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities.” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).
{"title":"Energy-Efficient Cluster Computing via Accurate Workload Characterization","authors":"S. Huang, W. Feng","doi":"10.1109/CCGRID.2009.88","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.88","url":null,"abstract":"This paper presents an eco-friendly daemon that reduces power and energy consumption while better maintaining high performance via an accurate workload characterization that infers “processor stall cycles due to off-chip activities.” The eco-friendly daemon is an interval-based, run-time algorithm that uses the workload characterization to dynamically adjust a processor’s frequency and voltage to reduce power and energy consumption with little impact on application performance. Using the NAS Parallel Benchmarks as our workload, we then evaluate our eco-friendly daemon on a cluster computer. The results indicate that our workload characterization allows the power-aware daemon to more tightly control performance (5% loss instead of 11%) while delivering substantial energy savings (11% instead of 8%).","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217943","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}
Applications performing scientific computations or processing streaming media benefit from parallel I/O significantly, as they operate on large data sets that require large I/O. MPI-I/O is a commonly used library interface in parallel applications to perform I/O efficiently. Optimizations like collective-I/O embedded in MPI-I/O allow multiple processes executing in parallel to perform I/O by merging requests of other processes and sharing them later. In such a scenario, preserving confidentiality of disk-resident data from unauthorized accesses by processes without significantly impacting performance of the application is a challenging task. In this paper, we evaluate the impact of ensuring data-confidentiality in MPI-I/O on the performance of parallel applications and provide an enhanced interface, called MPISec I/O, which brings an average overhead of only 5.77% over MPI-I/O in the best case, and about 7.82% in the average case.
{"title":"MPISec I/O: Providing Data Confidentiality in MPI-I/O","authors":"R. Prabhakar, C. Patrick, M. Kandemir","doi":"10.1109/CCGRID.2009.53","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.53","url":null,"abstract":"Applications performing scientific computations or processing streaming media benefit from parallel I/O significantly, as they operate on large data sets that require large I/O. MPI-I/O is a commonly used library interface in parallel applications to perform I/O efficiently. Optimizations like collective-I/O embedded in MPI-I/O allow multiple processes executing in parallel to perform I/O by merging requests of other processes and sharing them later. In such a scenario, preserving confidentiality of disk-resident data from unauthorized accesses by processes without significantly impacting performance of the application is a challenging task. In this paper, we evaluate the impact of ensuring data-confidentiality in MPI-I/O on the performance of parallel applications and provide an enhanced interface, called MPISec I/O, which brings an average overhead of only 5.77% over MPI-I/O in the best case, and about 7.82% in the average case.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128040472","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}
Grid computing continues to hold promise for the high-availability of a wide range of computational systems and techniques. It is suggested that Grids will attain greater acceptance by a larger audience of commercial end-users if binding Service Level Agreements (SLAs) are provided. We discuss Grid commoditization, the use of Grid technologies for financial risk analysis, and the potential formulation of the Grid Economy. Our aim is to predict availability and capability for risk analysis in and of Grids. The considerations involved may be more widely applicable to the configuration and management of related architectures including those of P2P systems and Clouds. In this paper, we explore and evaluate some of the factors involved in the automatic construction of SLAs for the Grid Economy.
{"title":"Risk Informed Computer Economics","authors":"Bin Li, Lee Gillam","doi":"10.1109/CCGRID.2009.18","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.18","url":null,"abstract":"Grid computing continues to hold promise for the high-availability of a wide range of computational systems and techniques. It is suggested that Grids will attain greater acceptance by a larger audience of commercial end-users if binding Service Level Agreements (SLAs) are provided. We discuss Grid commoditization, the use of Grid technologies for financial risk analysis, and the potential formulation of the Grid Economy. Our aim is to predict availability and capability for risk analysis in and of Grids. The considerations involved may be more widely applicable to the configuration and management of related architectures including those of P2P systems and Clouds. In this paper, we explore and evaluate some of the factors involved in the automatic construction of SLAs for the Grid Economy.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127198982","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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