
Improved Bounds for Distributed Load Balancing
In the load balancing problem, the input is an nvertex bipartite graph ...
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Geometric Exploration for Online Control
We study the control of an unknown linear dynamical system under general...
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Simpler and Better Algorithms for MinimumNorm Load Balancing
Recently, Chakrabarty and Swamy (STOC 2019) introduced the minimumnorm...
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A unified framework for designing EPTAS's for load balancing on parallel machines
We consider a general load balancing problem on parallel machines. Our m...
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Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
We study online prediction where regret of the algorithm is measured aga...
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Nash Social Welfare in Selfish and Online Load Balancing
In load balancing problems there is a set of clients, each wishing to se...
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OnLine Balancing of Random Inputs
We consider an online vector balancing game where vectors v_t, chosen un...
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Improved algorithms for online load balancing
We consider an online load balancing problem and its extensions in the framework of repeated games. On each round, the player chooses a distribution (task allocation) over K servers, and then the environment reveals the load of each server, which determines the computation time of each server for processing the task assigned. After all rounds, the cost of the player is measured by some norm of the cumulative computationtime vector. The cost is the makespan if the norm is L_∞norm. The goal is to minimize the regret, i.e., minimizing the player's cost relative to the cost of the best fixed distribution in hindsight. We propose algorithms for general norms and prove their regret bounds. In particular, for L_∞norm, our regret bound matches the best known bound and the proposed algorithm runs in polynomial time per trial involving linear programming and second order programming, whereas no polynomial time algorithm was previously known to achieve the bound.
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