Efficient file query is important to the overall performance of Peer-to-Peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. On the other hand, clustering peers by their physical proximity can also improve file query performance. Few current works are able to cluster peers based on both peer interest and physical proximity. It is even harder to realize it in structured P2Ps due to their strictly defined topologies, although they provide higher file query efficiency than unstructured P2Ps. In this paper, we introduce a proximity-aware and interest-clustered P2P file sharing system (PAIS) based on a structured P2P. It groups peers based on both interest and proximity. PAIS supports sophisticated routing and clustering strategies based on a hierarchical topology. Theoretical analysis and simulation results demonstrate that PAIS dramatically reduces the overhead and enhances efficiency in file sharing.
{"title":"PAIS: A Proximity-Aware Interest-Clustered P2P File Sharing System","authors":"Haiying Shen","doi":"10.1109/CCGRID.2009.17","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.17","url":null,"abstract":"Efficient file query is important to the overall performance of Peer-to-Peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. On the other hand, clustering peers by their physical proximity can also improve file query performance. Few current works are able to cluster peers based on both peer interest and physical proximity. It is even harder to realize it in structured P2Ps due to their strictly defined topologies, although they provide higher file query efficiency than unstructured P2Ps. In this paper, we introduce a proximity-aware and interest-clustered P2P file sharing system (PAIS) based on a structured P2P. It groups peers based on both interest and proximity. PAIS supports sophisticated routing and clustering strategies based on a hierarchical topology. Theoretical analysis and simulation results demonstrate that PAIS dramatically reduces the overhead and enhances efficiency in file sharing.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"418 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":"122862105","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}
Daniel Nurmi, R. Wolski, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, D. Zagorodnov
Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.
{"title":"The Eucalyptus Open-Source Cloud-Computing System","authors":"Daniel Nurmi, R. Wolski, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, D. Zagorodnov","doi":"10.1109/CCGRID.2009.93","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.93","url":null,"abstract":"Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"25 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":"133979204","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}
A common technique for result verification in grid computing is to delegate a computation redundantly to different workers and apply majority voting to the returned results. However, the technique is sensitive to "collusion" where a majority of malicious workers collectively returns the same incorrect result. In this paper, we propose a mechanism that identifies groups of colluding workers. The mechanism is based on the fact that colluders can succeed in a vote only when they hold the majority. This information allows us to build clusters of workers that voted similarly in the past, and so detect collusion. We find that the more strongly workers collude, the better they can be identified.
{"title":"Collusion Detection for Grid Computing","authors":"Eugen Staab, T. Engel","doi":"10.1109/CCGRID.2009.12","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.12","url":null,"abstract":"A common technique for result verification in grid computing is to delegate a computation redundantly to different workers and apply majority voting to the returned results. However, the technique is sensitive to \"collusion\" where a majority of malicious workers collectively returns the same incorrect result. In this paper, we propose a mechanism that identifies groups of colluding workers. The mechanism is based on the fact that colluders can succeed in a vote only when they hold the majority. This information allows us to build clusters of workers that voted similarly in the past, and so detect collusion. We find that the more strongly workers collude, the better they can be identified.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"30 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":"114874514","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}
Workflow execution time predictions for Grid infrastructures is of critical importance for optimized workflow executions, advance reservations of resources, and overhead analysis. Predicting workflow execution time is complex due to multeity of workflow structures, involvement of several Grid resources in workflow execution, complex dependencies of workflow activities and dynamic behavior of the Grid. In this paper we present an online workflow execution time prediction system exploiting similarity templates. The workflows are characterized considering the attributes describing their performance at different Grid infrastructural levels. A “supervised exhaustive search” is employed to find suitable templates. We also make a provision of including expert user knowledge about the workflow performance in the procession of our methods. Results for three real world applications are presented to show the effectiveness of our approach.
{"title":"Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid","authors":"F. Nadeem, T. Fahringer","doi":"10.1109/CCGRID.2009.77","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.77","url":null,"abstract":"Workflow execution time predictions for Grid infrastructures is of critical importance for optimized workflow executions, advance reservations of resources, and overhead analysis. Predicting workflow execution time is complex due to multeity of workflow structures, involvement of several Grid resources in workflow execution, complex dependencies of workflow activities and dynamic behavior of the Grid. In this paper we present an online workflow execution time prediction system exploiting similarity templates. The workflows are characterized considering the attributes describing their performance at different Grid infrastructural levels. A “supervised exhaustive search” is employed to find suitable templates. We also make a provision of including expert user knowledge about the workflow performance in the procession of our methods. Results for three real world applications are presented to show the effectiveness of our approach.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"28 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":"117311507","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}
Mark Stillwell, D. Schanzenbach, F. Vivien, H. Casanova
We propose a novel approach for sharing cluster resources among competing jobs. The key advantage of our approach over current solutions is that it increases cluster utilization while optimizing a user-centric metric that captures both notions of performance and fairness. We motivate and formalize the corresponding resource allocation problem, determine its complexity, and propose several algorithms to solve it in the case of a static workload that consists of sequential jobs. Via extensive simulation experiments we identify an algorithm that runs quickly, that is always on par with or better than its competitors, and that produces resource allocations that are close to optimal. We find that the extension of our approach to parallel jobs leads to similarly good results. Finally, we explain how to extend our work to dynamicworkloads.
{"title":"Resource Allocation Using Virtual Clusters","authors":"Mark Stillwell, D. Schanzenbach, F. Vivien, H. Casanova","doi":"10.1109/CCGRID.2009.23","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.23","url":null,"abstract":"We propose a novel approach for sharing cluster resources among competing jobs. The key advantage of our approach over current solutions is that it increases cluster utilization while optimizing a user-centric metric that captures both notions of performance and fairness. We motivate and formalize the corresponding resource allocation problem, determine its complexity, and propose several algorithms to solve it in the case of a static workload that consists of sequential jobs. Via extensive simulation experiments we identify an algorithm that runs quickly, that is always on par with or better than its competitors, and that produces resource allocations that are close to optimal. We find that the extension of our approach to parallel jobs leads to similarly good results. Finally, we explain how to extend our work to dynamicworkloads.","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":"123530916","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}
To address coordination and complexity issues, we formulate a grid task allocation problem as a bargaining based self-adaptive auction and propose the BarSAA grid task-bundle allocation algorithm. During the auction, prices are iteratively negotiated and dynamically adjusted until market equilibrium is reached. The BarSAA algorithm features decentralized bidding decision making in a heterogeneous distributed environment so that scheduler can offload its duty onto participating computing nodes and significantly reduces scheduling overheads. When a BarSAA auction converges, the equilibrium point is {Pareto Optimal} and achieves social efficient outcome and double-sided revenue maximization. In addition, BarSAA promotes truthful behavior among selfish nodes. Through game theoretical analysis, we demonstrate that truthful revelation is beneficial to bidders in making bidding strategies. Extensive simulation results are presented to demonstrate the efficiency of the BarSAA strategy and validate several important analytical properties.
{"title":"Efficient Grid Task-Bundle Allocation Using Bargaining Based Self-Adaptive Auction","authors":"Han Zhao, Xiaolin Li","doi":"10.1109/CCGRID.2009.86","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.86","url":null,"abstract":"To address coordination and complexity issues, we formulate a grid task allocation problem as a bargaining based self-adaptive auction and propose the BarSAA grid task-bundle allocation algorithm. During the auction, prices are iteratively negotiated and dynamically adjusted until market equilibrium is reached. The BarSAA algorithm features decentralized bidding decision making in a heterogeneous distributed environment so that scheduler can offload its duty onto participating computing nodes and significantly reduces scheduling overheads. When a BarSAA auction converges, the equilibrium point is {Pareto Optimal} and achieves social efficient outcome and double-sided revenue maximization. In addition, BarSAA promotes truthful behavior among selfish nodes. Through game theoretical analysis, we demonstrate that truthful revelation is beneficial to bidders in making bidding strategies. Extensive simulation results are presented to demonstrate the efficiency of the BarSAA strategy and validate several important analytical properties.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"28 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":"125430373","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 a grid application, some requirements may change while the execution is in progress. This paper presents in-vivo enhancement--updating running grid applications to facilitate their perfective maintenance. Because applications in this domain are not only typically long-running, but also time-consuming to deploy, we propose a dynamic update technique that can change a running application flexibly and efficiently. Specifically, this paper presents a novel technique for dynamically updating grid applications deployed on the Java Virtual Machine (JVM). Our technique overcomes constraints of JVM HotSwap, a facility for replacing classes at runtime. While HotSwap precludes the programmer from adding new methods and fields, changing the signatures of existing methods, and has no support for transferring state between old and new objects, our approach effectively removes these constraints by rewriting program bytecode. Further, the rewritten programs incur only minimal performance overhead (less than 2% on average). We demonstrate the efficiency and extensibility of our approach through micro and macro benchmarks, as well as through a case study of dynamically updating a parallel bioinformatics application.
{"title":"Flexible and Efficient In-Vivo Enhancement for Grid Applications","authors":"Dong Kwan Kim, Yang Jiao, E. Tilevich","doi":"10.1109/CCGRID.2009.61","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.61","url":null,"abstract":"In a grid application, some requirements may change while the execution is in progress. This paper presents in-vivo enhancement--updating running grid applications to facilitate their perfective maintenance. Because applications in this domain are not only typically long-running, but also time-consuming to deploy, we propose a dynamic update technique that can change a running application flexibly and efficiently. Specifically, this paper presents a novel technique for dynamically updating grid applications deployed on the Java Virtual Machine (JVM). Our technique overcomes constraints of JVM HotSwap, a facility for replacing classes at runtime. While HotSwap precludes the programmer from adding new methods and fields, changing the signatures of existing methods, and has no support for transferring state between old and new objects, our approach effectively removes these constraints by rewriting program bytecode. Further, the rewritten programs incur only minimal performance overhead (less than 2% on average). We demonstrate the efficiency and extensibility of our approach through micro and macro benchmarks, as well as through a case study of dynamically updating a parallel bioinformatics application.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"35 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":"126071526","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 deluge of available data for analysis demands the need to scale the performance of data mining implementations. With the current architectural trends, one of the major challenges today is achieving programmability and performance for data mining applications on multi-core machines and cluster of multi-core machines. To address this problem, we have been developing a runtime framework, FREERIDE, that enables parallel execution of data mining and data analysis tasks.The contributions of this paper are two-fold: 1) This paper describes and evaluates various shared-memory parallelization techniques developed in our run-time system on a cluster of multi-cores, and 2) We report on a detailed performance study to understand why certain parallelization techniques out-perform othertechniques for a particular application.
{"title":"Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster","authors":"Vignesh T. Ravi, G. Agrawal","doi":"10.1109/CCGRID.2009.83","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.83","url":null,"abstract":"The deluge of available data for analysis demands the need to scale the performance of data mining implementations. With the current architectural trends, one of the major challenges today is achieving programmability and performance for data mining applications on multi-core machines and cluster of multi-core machines. To address this problem, we have been developing a runtime framework, FREERIDE, that enables parallel execution of data mining and data analysis tasks.The contributions of this paper are two-fold: 1) This paper describes and evaluates various shared-memory parallelization techniques developed in our run-time system on a cluster of multi-cores, and 2) We report on a detailed performance study to understand why certain parallelization techniques out-perform othertechniques for a particular application.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"1 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":"131350824","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}
Hanhua Chen, Hai Jin, Xucheng Luo, Yunhao Liu, L. Ni
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P search protocols to effectively locate items with guaranteed recall rate. Second, existing schemes to improve search successful rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage cost. In this paper we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. BloomCast is effective because it guarantees perfect recall rate with high probability. It is efficient because the overall communication cost of full-text search is reduced below a formal bound. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication cost and storage cost for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations. Results show that BloomCast outperforms existing schemes in terms of both recall rate and communication cost.
{"title":"BloomCast: Efficient Full-Text Retrieval over Unstructured P2Ps with Guaranteed Recall","authors":"Hanhua Chen, Hai Jin, Xucheng Luo, Yunhao Liu, L. Ni","doi":"10.1109/CCGRID.2009.50","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.50","url":null,"abstract":"Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P search protocols to effectively locate items with guaranteed recall rate. Second, existing schemes to improve search successful rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage cost. In this paper we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. BloomCast is effective because it guarantees perfect recall rate with high probability. It is efficient because the overall communication cost of full-text search is reduced below a formal bound. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication cost and storage cost for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations. Results show that BloomCast outperforms existing schemes in terms of both recall rate and communication cost.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"3 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":"127214586","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}
Workload modeling plays a significant role in performance evaluation of large-scale parallel systems such as clusters and grids. It helps to generate synthetic workloads which capture some dominant characteristics of traces (real workloads). Modeling job arrival process is an essential part of workload modeling. Although a job arrival process has many important characteristics such as long range dependence (LRD) and burstiness, most researchers, for simplicity, assume it as a poisson process in their evaluation work. Furthermore, there is currently almost no research focusing on both LRD and burstiness at the same time according to our investigation. With respect to this research trend, the multifractal wavelet model (MWM) recently has been introduced as a good choice to yield LRD for a job arrival process. Though LRD is well controlled, we observe that a job arrival process produced by MWM does not keep burstiness. In this paper, we present our study on modifying MWM so that not only LRD but also burstiness are kept in the job arrival process. In addition, our modification also fits the marginal distribution better than MWM.
{"title":"Modeling Job Arrival Process with Long Range Dependence and Burstiness Characteristics","authors":"T. Minh, L. Wolters","doi":"10.1109/CCGRID.2009.35","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.35","url":null,"abstract":"Workload modeling plays a significant role in performance evaluation of large-scale parallel systems such as clusters and grids. It helps to generate synthetic workloads which capture some dominant characteristics of traces (real workloads). Modeling job arrival process is an essential part of workload modeling. Although a job arrival process has many important characteristics such as long range dependence (LRD) and burstiness, most researchers, for simplicity, assume it as a poisson process in their evaluation work. Furthermore, there is currently almost no research focusing on both LRD and burstiness at the same time according to our investigation. With respect to this research trend, the multifractal wavelet model (MWM) recently has been introduced as a good choice to yield LRD for a job arrival process. Though LRD is well controlled, we observe that a job arrival process produced by MWM does not keep burstiness. In this paper, we present our study on modifying MWM so that not only LRD but also burstiness are kept in the job arrival process. In addition, our modification also fits the marginal distribution better than MWM.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"30 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":"125971624","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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