Minimal cost reconfiguration of data placement in a storage area network

Shachnai, Hadas; Tamir, Gal; Tamir, Tami

doi:10.1016/j.tcs.2012.06.018

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Shachnai et al [21] investigate how to determine a placement of Video-on-Demand (VoD) file copies on the servers and the amount of load capacity assigned to each file copy so as to minimize the communication cost while ensuring the user experience. Agarwal et al [22] propose an automated data placement mechanism Volley for geo-distributed cloud services with the consideration of WAN bandwidth cost, data center capacity limits, data inter-dependencies, etc.…”

Section: Data Placementmentioning

confidence: 99%

Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

Zeng

et al. 2014

IEEE Trans. Emerg. Topics Comput.

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The explosive growth of demands on big data processing imposes a heavy burden on computation, storage, and communication in data centers, which hence incurs considerable operational expenditure to data center providers. Therefore, cost minimization has become an emergent issue for the upcoming big data era. Different from conventional cloud services, one of the main features of big data services is the tight coupling between data and computation as computation tasks can be conducted only when the corresponding data are available. As a result, three factors, i.e., task assignment, data placement, and data movement, deeply influence the operational expenditure of data centers. In this paper, we are motivated to study the cost minimization problem via a joint optimization of these three factors for big data services in geo-distributed data centers. To describe the task completion time with the consideration of both data transmission and computation, we propose a 2-D Markov chain and derive the average task completion time in closed-form. Furthermore, we model the problem as a mixed-integer nonlinear programming and propose an efficient solution to linearize it. The high efficiency of our proposal is validated by extensive simulationbased studies.

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Section: Data Placementmentioning

confidence: 99%

Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

Zeng

et al. 2014

IEEE Trans. Emerg. Topics Comput.

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“…There have been a number of papers investigating the complexity of "dynamic problems" in various application domains [19][20][21] , but only one (to the best of our knowledge) that investigates the parameterized complexity of an dynamic problem -the recent pioneering paper of Hartung and Niedermeier [22] that inspired this paper.…”

Section: Almost a Dominating Setmentioning

confidence: 99%

Dynamic dominating set and turbo-charging greedy heuristics

Downey

Egan

Fellows

et al. 2014

Tinshhua Sci. Technol.

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The main purpose of this paper is to exposit two very different, but very general, motivational schemes in the art of parameterization and a concrete example connecting them. We introduce a dynamic version of the DOMINATING SET problem and prove that it is fixed-parameter tractable (FPT). The problem is motivated by settings where problem instances evolve. It also arises in the quest to improve a natural greedy heuristic for the DOMINATING SET problem.

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“…In order to satisfy new client requests, the content of the storage system needs to be modified. The new storage allocation needs to satisfy the current demand; also, due to the cost of file migrations, this should be achieved by using a minimum number of reassignments of file copies to servers [20]. In communication networks, the set of demands to connect sources to destinations changes over time.…”

Section: Introductionmentioning

confidence: 99%

A Theory and Algorithms for Combinatorial Reoptimization

et al. 2017

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Many real-life applications involve systems that change dynamically over time. Thus, throughout the continuous operation of such a system, it is required to compute solutions for new problem instances, derived from previous instances. Since the transition from one solution to another incurs some cost, a natural goal is to have the solution for the new instance close to the original one (under a certain distance measure). In this paper we develop a general model for combinatorial reoptimization, encompassing classical objective functions as well as the goal of minimizing the transition cost from one solution to the other. Formally, we say that A is an (r, ρ)-reapproximation algorithm if it achieves a ρapproximation for the optimization problem, while paying a transition cost that is at most r times the minimum required for solving the problem optimally. When ρ = 1 we get an (r, 1)-reoptimization algorithm. Using our model we derive reoptimization and reapproximation algorithms for several important classes of optimization problems. This includes fully polynomial time reapproximation schemes for DP-benevolent problems, a class introduced by Woeginger (Proc. Tenth ACM-SIAM Symposium on Discrete Algorithms, 1999 ), reapproximation algorithms for metric Facility Location problems, and (1, 1)-reoptimization algorithm for polynomially solvable subset-selection problems. Thus, we distinguish here for the first time between classes of reoptimization problems, by their hardness status with respect to minimizing transition costs while guaranteeing a good approximation for the underlying optimization problem. ⋆

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Minimal cost reconfiguration of data placement in a storage area network

Cited by 12 publications

References 23 publications

Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

Dynamic dominating set and turbo-charging greedy heuristics

A Theory and Algorithms for Combinatorial Reoptimization

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