The Experts below are selected from a list of 300039 Experts worldwide ranked by ideXlab platform
Ling Liu - One of the best experts on this subject based on the ideXlab platform.
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adaptive Data Migration in multi tiered storage based cloud environment
International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
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IEEE CLOUD - Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment
2010 IEEE 3rd International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
Gong Zhang - One of the best experts on this subject based on the ideXlab platform.
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adaptive Data Migration in multi tiered storage based cloud environment
International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
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IEEE CLOUD - Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment
2010 IEEE 3rd International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
Bo Yang - One of the best experts on this subject based on the ideXlab platform.
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Live Data Migration Strategy with Stream Processing Framework
WSEAS Transactions on Computers archive, 2018Co-Authors: Qiuchen Cheng, Bo YangAbstract:Live Data Migration in the cloud is responsible to migrate blocks of Data of eMigration node to several imMigration node. However, live Data Migration strategy is a NP-hard problem like task scheduling. Recently, in-stream processing is the immediate need in many practical applications. Therefore, we explore a real-time live Data Migration strategy with stream processing framework in this paper. First, the Migration cost and balance model is introduced as the metrics to evaluate Data Migration strategy. Subsequently, a live Data Migration strategy with particle swarm optimization is proposed. Afterwards, we implement this method using stream processing framework. The experimental results show the best performance of our method in all
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Optimization of stream-based live Data Migration strategy in the cloud.
Concurrency and Computation: Practice and Experience, 2017Co-Authors: Bo YangAbstract:Summary Live Data Migration in the cloud is responsible to migrate blocks of Data from one eMigration node to several imMigration nodes. However, live Data Migration strategy is a NP-hard problem like task scheduling. Recently, in-stream processing is a new technique to process large-scale Data nearly instantaneously. This framework works fast that all decisions are made without a continuous stream of events. In this paper, we explore a real-time live Data Migration strategy with stream processing paradigm. First, the nonlinear Migration cost model and balance model are introduced as the metrics to evaluate the Data Migration strategy. Subsequently, a live Data Migration strategy with particle swarm optimization (PSO) is proposed. Two improvement measures called loop context and particle grouping are proposed. As an improvement of stream processing framework, nested loop context structure is a feedback to support iterative optimization algorithm. As an improvement of PSO, grouping particles before in-stream processing are to speed up the convergence rate of PSO. Afterwards, we rebuild stream processing framework to implement these methods. The experimental results show the best performance of our method.
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SoCPaR - Stream-based Particle Swarm Optimization for Data Migration decision
2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2015Co-Authors: Qiuchen Cheng, Bo YangAbstract:As the load in the cloud environment is always changing, Data Migration become a key technology to realize the load balance of clusters. A good Migration decision can make Data Migration more efficiency. To realize the Migration decision rapidly, parallel Particle Swarm Optimization (PSO) based on stream computing technology is presented in this paper. We use PSO to get a Migration plan with minimum overhead. Since the implementation of traditional PSO in serial is a huge waste of time in our scene, we design and accomplish Stream-based Particle Swarm Optimization (SPSO). SPSO utilizes stream computing technology to realize parallel PSO to make the process of Data Migration decision more rapidly and accurately, and realize real-time decisions on the basis of real-time status of nodes in the cloud. The average execution time of our SPSO is shorter than traditional serial PSO algorithm, and the Migration cost of Data Migration decision result is lower.
John Wilkes - One of the best experts on this subject based on the ideXlab platform.
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Algorithms for Data Migration
Algorithmica, 2008Co-Authors: Eric Anderson, Joseph Hall, Jason D. Hartline, M. Hobbes, Anna R. Karlin, Jared Saia, Ram Swaminathan, John WilkesAbstract:The Data Migration problem is the problem of computing a plan for moving Data objects stored on devices in a network from one configuration to another. Load balancing or changing usage patterns might necessitate such a rearrangement of Data. In this paper, we consider the case where the objects are fixed-size and the network is complete. Our results are both theoretical and empirical. Our main theoretical results are (1) a polynomial time algorithm for finding a near-optimal Migration plan in the presence of space constraints when a certain number of additional nodes is available as temporary storage, and (2) a 3/2-approximation algorithm for the case where Data must be migrated directly to its destination. We also run extensive experiments on several algorithms for various Data Migration problems and show that empirically, many algorithms perform better in practice than their theoretical bounds suggest. We conclude that many of the algorithms we present are both practical and effective for Data Migration.
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FAST - Aqueduct: Online Data Migration with Performance Guarantees
2002Co-Authors: Guillermo A. Alvarez, John WilkesAbstract:Modern computer systems are expected to be up continuously: even planned downtime to accomplish system reconfiguration is becoming unacceptable, so more and more changes are having to be made to "live" systems that are running production workloads. One of those changes is Data Migration: moving Data from one storage device to another for load balancing, system expansion, failure recovery, or a myriad of other reasons. Traditional methods for achieving this either require application down-time, or severely impact the performance of foreground applications - neither a good outcome when performance predictability is almost as important as raw speed. Our solution to this problem, Aqueduct, uses a control-theoretical approach to statistically guarantee a bound on the amount of impact on foreground work during a Data Migration, while still accomplishing the Data Migration in as short a time as possible. The result is better quality of service for the end users, less stress for the system administrators, and systems that can be adapted more readily to meet changing demands.
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aqueduct online Data Migration with performance guarantees
File and Storage Technologies, 2002Co-Authors: Guillermo A. Alvarez, John WilkesAbstract:Modern computer systems are expected to be up continuously: even planned downtime to accomplish system reconfiguration is becoming unacceptable, so more and more changes are having to be made to "live" systems that are running production workloads. One of those changes is Data Migration: moving Data from one storage device to another for load balancing, system expansion, failure recovery, or a myriad of other reasons. Traditional methods for achieving this either require application down-time, or severely impact the performance of foreground applications - neither a good outcome when performance predictability is almost as important as raw speed. Our solution to this problem, Aqueduct, uses a control-theoretical approach to statistically guarantee a bound on the amount of impact on foreground work during a Data Migration, while still accomplishing the Data Migration in as short a time as possible. The result is better quality of service for the end users, less stress for the system administrators, and systems that can be adapted more readily to meet changing demands.
Lawrence Chiu - One of the best experts on this subject based on the ideXlab platform.
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adaptive Data Migration in multi tiered storage based cloud environment
International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
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IEEE CLOUD - Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment
2010 IEEE 3rd International Conference on Cloud Computing, 2010Co-Authors: Gong Zhang, Lawrence Chiu, Ling LiuAbstract:Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated Data Migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective Data Migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated Data Migration in multi-tiered storage systems is how to fully release the power of Data Migration while guaranteeing the Migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead Data Migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive Data Migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead Migration efficiency in our formal model develop. Second, our Data Migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead Migration. Third, we formally and experimentally show that the adaptive Data Migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead Migration approach with the basic Migration model and show that the adaptive Migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.
