Competitive routing in multiuser communication networks
IEEE/ACM Transactions on Networking (TON)
Task assignment with unknown duration
Journal of the ACM (JACM)
The power of two choices in randomized load balancing
The power of two choices in randomized load balancing
The Natural Work-Stealing Algorithm is Stable
SIAM Journal on Computing
Bounds for the convergence rate of randomized local search in a multiplayer load-balancing game
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Distributed selfish load balancing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Convergence time to Nash equilibrium in load balancing
ACM Transactions on Algorithms (TALG)
Algorithmic Game Theory
Stability of Load Balancing Algorithms in Dynamic Adversarial Systems
SIAM Journal on Computing
Nashification and the coordination ratio for a selfish routing game
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Convergence to equilibria in distributed, selfish reallocation processes with weighted tasks
ESA'07 Proceedings of the 15th annual European conference on Algorithms
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In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach themselves to an arbitrary server, but may switch servers independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.We first analyze the natural Random Local Search (RLS) strategy. Under this strategy, after sampling a new server randomly, clients only switch to it if their service rate is improved. In closed systems, where the client population is fixed, we derive tight estimates of the time it takes under RLS strategy to balance the load across servers. We then study open systems where clients arrive according to a random process and leave the system upon service completion. In this scenario, we analyze how client migrations within the system interact with the system dynamics induced by client arrivals and departures. We compare the load-aware RLS strategy to a load-oblivious strategy in which clients just randomly switch server without accounting for the server loads. Surprisingly, we show that both load-oblivious and load-aware strategies stabilize the system whenever this is at all possible. We use large-system asymptotics to characterize system performance, and augment this with simulations, which suggest that the average client sojourn time under the load-oblivious strategy is not considerably reduced when clients apply smarter load-aware strategies.