Improved algorithms for online load balancing
This work addresses load balancing in computational systems, offering incremental improvements in algorithm efficiency for specific norms.
The paper tackles the online load balancing problem by proposing algorithms for general norms to minimize regret relative to the best fixed distribution in hindsight, achieving a regret bound that matches the best known bound for the L∞-norm with a polynomial-time algorithm, where no such algorithm existed before.
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 computation-time vector. The cost is the makespan if the norm is $L_\infty$-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 $L_\infty$-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.