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        <datestamp>2024-03-06T10:57:16Z</datestamp>
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          <dc:title>Memoryless Worker-Task Assignment with Polylogarithmic Switching Cost</dc:title>
          <dc:creator>Berger, Aaron</dc:creator>
          <dc:creator>Kuszmaul, William</dc:creator>
          <dc:creator>Polak, Adam</dc:creator>
          <dc:creator>Tidor, Jonathan</dc:creator>
          <dc:creator>Wein, Nicole</dc:creator>
          <dc:subject>Distributed Task Allocation</dc:subject>
          <dc:subject>Metric Embeddings</dc:subject>
          <dc:subject>Probabilistic Method</dc:subject>
          <dc:description>We study the basic problem of assigning memoryless workers to tasks with dynamically changing demands. Given a set of w workers and a multiset T ⊆ [t] of |T| = w tasks, a memoryless worker-task assignment function is any function ϕ that assigns the workers [w] to the tasks T based only on the current value of T. The assignment function ϕ is said to have switching cost at most k if, for every task multiset T, changing the contents of T by one task changes ϕ(T) by at most k worker assignments. The goal of memoryless worker task assignment is to construct an assignment function with the smallest possible switching cost.&#13;
In past work, the problem of determining the optimal switching cost has been posed as an open question. There are no known sub-linear upper bounds, and after considerable effort, the best known lower bound remains 4 (ICALP 2020). &#13;
We show that it is possible to achieve polylogarithmic switching cost. We give a construction via the probabilistic method that achieves switching cost O(log w log (wt)) and an explicit construction that achieves switching cost polylog (wt). We also prove a super-constant lower bound on switching cost: we show that for any value of w, there exists a value of t for which the optimal switching cost is w. Thus it is not possible to achieve a switching cost that is sublinear strictly as a function of w. &#13;
Finally, we present an application of the worker-task assignment problem to a metric embeddings problem. In particular, we use our results to give the first low-distortion embedding from sparse binary vectors into low-dimensional Hamming space.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Aaron Berger and William Kuszmaul and Adam Polak and Jonathan Tidor and Nicole Wein</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 229, 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)</dc:relation>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2022.19</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-163608</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2022.19</dc:identifier>
          <dc:language>eng</dc:language>
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