Having multiple data stores that can potentially serve content is common in modern networked applications. Data stores often publish approximate summaries of their content to enable effective utilization. Since these summaries are not entirely accurate, forming an efficient access strategy to multiple data stores becomes a complex risk management problem. This paper formally models this problem as a cost minimization problem, while taking into account both access costs, the inaccuracy of the approximate summaries, as well as the penalties incurred by failed requests. We introduce practical algorithms with guaranteed approximation ratios and further show that they are optimal in various settings. We also perform an extensive simulation study based on real data and show that our algorithms are more robust than existing heuristics. That is, they exhibit near-optimal performance in various settings, whereas the efficiency of existing approaches depends upon system parameters that may change over time, or be otherwise unknown.

Access Strategies for Network Caching / Cohen, Itamar; Einziger, Gil; Friedman, Roy; Scalosub, Gabriel. - In: IEEE-ACM TRANSACTIONS ON NETWORKING. - ISSN 1063-6692. - ELETTRONICO. - 29:2(2021), pp. 609-622. [10.1109/TNET.2020.3043280]

Access Strategies for Network Caching

Cohen, Itamar;Einziger, Gil;
2021

Abstract

Having multiple data stores that can potentially serve content is common in modern networked applications. Data stores often publish approximate summaries of their content to enable effective utilization. Since these summaries are not entirely accurate, forming an efficient access strategy to multiple data stores becomes a complex risk management problem. This paper formally models this problem as a cost minimization problem, while taking into account both access costs, the inaccuracy of the approximate summaries, as well as the penalties incurred by failed requests. We introduce practical algorithms with guaranteed approximation ratios and further show that they are optimal in various settings. We also perform an extensive simulation study based on real data and show that our algorithms are more robust than existing heuristics. That is, they exhibit near-optimal performance in various settings, whereas the efficiency of existing approaches depends upon system parameters that may change over time, or be otherwise unknown.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2921032