DCCP: an effective data placement strategy for data-intensive computations in distributed cloud computing systems

Wang, Tao; Yao, Shihong; Wang, Chao; Jia, Shan

doi:10.1007/s11227-015-1511-z

Cited by 24 publications

(25 citation statements)

References 26 publications

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“…Therefore, in future work, we plan to develop a type of composite method that uses only a user's current status to make predictions when a system is new, but then switches to using traces to mine the relationships among data after sufficient traces are available. Meanwhile, aiming at the drawbacks of conventional distributed computing in computing-intensive application [34], an integrated method that comprehensively considers computational efficiency and access speed also needs to be developed.…”

Section: Discussionmentioning

confidence: 99%

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

et al. 2017

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A web geographical information system is a typical service-intensive application. Tile prefetching and cache replacement can improve cache hit ratios by proactively fetching tiles from storage and replacing the appropriate tiles from the high-speed cache buffer without waiting for a client’s requests, which reduces disk latency and improves system access performance. Most popular prefetching strategies consider only the relative tile popularities to predict which tile should be prefetched or consider only a single individual user's access behavior to determine which neighbor tiles need to be prefetched. Some studies show that comprehensively considering all users’ access behaviors and all tiles’ relationships in the prediction process can achieve more significant improvements. Thus, this work proposes a new global user-driven model for tile prefetching and cache replacement. First, based on all users’ access behaviors, a type of expression method for tile correlation is designed and implemented. Then, a conditional prefetching probability can be computed based on the proposed correlation expression mode. Thus, some tiles to be prefetched can be found by computing and comparing the conditional prefetching probability from the uncached tiles set and, similarly, some replacement tiles can be found in the cache buffer according to multi-step prefetching. Finally, some experiments are provided comparing the proposed model with other global user-driven models, other single user-driven models, and other client-side prefetching strategies. The results show that the proposed model can achieve a prefetching hit rate in approximately 10.6% ~ 110.5% higher than the compared methods.

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Section: Discussionmentioning

confidence: 99%

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

et al. 2017

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“…For simplicity, the experiments assume that all of the computation centers and storage nodes have the same abilities. Experiments are performed using different passive caching strategy (PC) algorithms (such as LRU), data placement strategy (DP) algorithms (such as the DCCP algorithm [4]) and active caching strategy (AC) algorithms (such as the GUDC algorithm [26] and CPR). The PC algorithms store the data in the storage nodes randomly and then obtain and cache the data from the storage nodes based on the behaviors of the applications.…”

Section: Number Of Clients (Users) 100mentioning

confidence: 99%

“…A PC method that uses LRU to replace the cached data (labeled PC_LRU). 4. A method that uses the DCCP [4] data placement strategy to place the data and does not use active caching (labeled DP_DCCP).…”

Section: Number Of Clients (Users) 100mentioning

confidence: 99%

“…In those cases, GIS must process large amounts of both spatial data and single mapping data; a large amount of those data may be in real time [2]. Thus, several algorithms have been proposed to meet the requirements of data computation in distributed GIS [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The data transfer rate strategy is a method to reduce network transmission costs [7] that stores all related data in the same node to reduce the data transfer rate between distributed nodes, thus saving data transfer time costs between nodes [4,15]. Dynamic computation correlation cata placement (DCCP) [4] distributes and stores data that have high dynamic computation correlations in the same data center by considering not only the I/O load but also the capacity of the data centers. Access pattern-based distributed storage algorithm (APSA) [15] also distributes and stores data that have high access correlations in different data centers to allow concurrent access.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced active caching strategy for data-intensive computations in distributed GIS

et al. 2017

Self Cite

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Caching can prepare data for computational tasks in advance by tracking the requirements and behaviors of distributed geographical information systems to reduce network latency and improve computational performance. This paper presents an enhanced method to actively cache data for data-intensive computations that considers both data relationships and the timeliness of those relationships. First, the access correlations, the correlation steps and the times of the correlations are computed based on the behaviors of the computational tasks. Because the influence of historically accessed records will decrease gradually over time, only recently accessed records are used. To track changes in the relationships and prevent cache waste problems, each record is given a different age-based weight. A conditional caching probability can then be computed based on the timeliness relationships, which can be used to find the appropriate data to compute simultaneously. Finally, we present several experiments that compare the proposed method with techniques that use other data placement strategies, active caching strategies and passive caching algorithms. The results show that the proposed model has better performance than other algorithms in all respects. In addition, the proposed model results in a lower cache replacement ratio. The experiments with different data sets on different data scales indicate that the proposed algorithm can also be used in large-scale distributed environments. B Shaoming Pan

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Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

Chen

Min

et al. 2019

Concurrency and Computation

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Mobile edge computing (MEC) necessitates cost-effective deployment for executing scientific workflows with different tasks and datasets, which provides computing, storage and network control at the network edge. However, the execution of scientific workflows in MEC results in heavy costs of data placement including data transmission and data storage. Although there are solutions for data placement in traditional cloud computing, they cannot effectively respond to the latency-sensitive property of scientific workflows, which leads to the excessive costs of data placement. To cope with this problem, we combine the advantages of MEC and cloud computing and propose a genetic algorithm particle swarm optimization (GAPSO) based method to explore the optimal strategy of data placement for scientific workflows in MEC. First, a unified model of data placement is designed to explore a cost-effective strategy, which considers the different characteristics between MEC and cloud computing as well as the impact of latency constraint on transmission costs. Next, the advantages of genetic algorithm (GA) and particle swarm optimization (PSO) are integrated to optimize the proposed model, which utilities the fast convergence of PSO and the crossover and mutation operations of GA. Simulations using real-world scientific workflows show the effectiveness of the proposed method for reducing data placement costs in MEC.

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DCCP: an effective data placement strategy for data-intensive computations in distributed cloud computing systems

Cited by 24 publications

References 26 publications

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

A Global User-Driven Model for Tile Prefetching in Web Geographical Information Systems

An enhanced active caching strategy for data-intensive computations in distributed GIS

Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

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