Problem
Research questions and friction points this paper is trying to address.
latency-optimal
geo-distributed storage
weighted graphs
communication delays
file assignment
Latency-Optimal Geo-Distributed Storage over Structured Networks
๐ค AI Summary
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็ๅปถ่ฟไผๅ้ฎ้ข๏ผ้่ฟๅ ๆๅพๆจกๅๅๆๆ็ฎๆณ๏ผๅจ็นๅฎ็ฝ็ปๆๆไธๆพๅฐๆไผๆไปถๅ้
็ญ็ฅใ
๐ Abstract
We study latency-optimal file assignment in geo-distributed storage systems modeled as weighted graphs, where edge weights represent communication delays and each node stores one (possibly coded) file. Our goal is to minimize the average time required to retrieve an original file, taken uniformly over all nodes and files. We show that for every fixed number of files $k \geq 3$, computing a latency-minimizing assignment is NP-hard via a reduction from the domatic number problem. On the positive side, we identify natural network topologies that admit uncoded, structured optimal assignments in which, for every node, one can choose its $k$ closest nodes, including itself, so that they store distinct original files. We prove that every weighted tree, certain weighted cycles, and unit-weight graphs with sufficiently large minimum degree admit such assignments. For these graph classes, we provide efficient algorithms to construct latency-optimal file assignments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
latency-optimal file assignment
geo-distributed storage systems
weighted graphs
uncoded structured assignments
efficient algorithms
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