The periodic balanced sorting network
Journal of the ACM (JACM)
Journal of the ACM (JACM)
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
How many random edges make a dense graph Hamiltonian?
Random Structures & Algorithms
Local Divergence of Markov Chains and the Analysis of Iterative Load-Balancing Schemes
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Journal of Parallel and Distributed Computing - Special issue: 18th International parallel and distributed processing symposium
On smoothed analysis in dense graphs and formulas
Random Structures & Algorithms
Beyond Hirsch Conjecture: Walks on Random Polytopes and Smoothed Complexity of the Simplex Method
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Worst-case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-means Method
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Smoothed analysis of integer programming
Mathematical Programming: Series A and B
Smoothed analysis of binary search trees
Theoretical Computer Science
The diameter of randomly perturbed digraphs and some applications
Random Structures & Algorithms
Refuting Smoothed 3CNF Formulas
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
The impact of randomization in smoothing networks
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
On smoothed k-CNF formulas and the Walksat algorithm
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Near-perfect load balancing by randomized rounding
Proceedings of the forty-first annual ACM symposium on Theory of computing
The Art of Multiprocessor Programming
The Art of Multiprocessor Programming
Hi-index | 0.00 |
In a load balancing network each processor has an initial collection of unit-size jobs, tokens, and in each round, pairs of processors connected by balancers split their load as evenly as possible. An excess token (if any) is placed according to some predefined rule. As it turns out, this rule crucially effects the performance of the network. In this work we propose a model that studies this effect. We suggest a model bridging the uniformly-random assignment rule, and the arbitrary one (in the spirit of smoothed-analysis) by starting from an arbitrary assignment of balancer directions, then flipping each assignment with probability *** independently. For a large class of balancing networks our result implies that after $\mathcal O(\log n)$ rounds the discrepancy is whp $\mathcal O( (1/2-\alpha) \log n + \log \log n)$. This matches and generalizes the known bounds for *** = 0 and *** = 1/2.