A Distributed Drafting Algorithm for Load Balancing
IEEE Transactions on Software Engineering
A comparison of receiver-initiated and sender-initiated adaptive load sharing (extended abstract)
SIGMETRICS '85 Proceedings of the 1985 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Static Assignment of Stochastic Tasks Using Majorization
IEEE Transactions on Computers
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Convergence of the diffusion method for weighted torus graphs using Fourier analysis
Theoretical Computer Science
Optimum diffusion for load balancing in mesh networks
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
The nine neighbor extrapolated diffusion method for weighted torus graphs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
| Hi-index | 0.00 |
This paper will focus on the issue of load balancing on a hypercube network of N processors. We will investigate a typical nearest neighbor balancing strategy - in which workloads among neighboring processors are averaged at discrete time steps. The computation model allows tasks, described by independent random variables, to be generated and terminated at all times.We assume that the random variables at all nodes have equal expected value and their variances are bounded by a constant d2, and we let the difference DIFF between the actual load on each node and the average load on the system describe the deviation of the load on a node from the global average value. The following analytical results are obtained:The expected value of DIFF, denoted by E(DIFF), is 0.The variance of DIFF, denoted by Var(DIFF), is independent of time t, and Var(DIFF)≤ 1.386d2 + 0.231 logN.