Dynamic grid load sharing with adaptive dissemination protocols

Scheduling in large scale dynamic grids comprising eclectic collections of resources is increasingly difficult. Autonomous resource neighborhoods may wish to determine the level of grid offered load that they can or will accept; different sites may wish to attract different amounts of load, to satisfy some desired property within a grid economy. This changes the traditional notion of load sharing, which generally assumes that the desired equilibrium should be an equal distribution of load across all participating machines, because they are under the jurisdiction of a single site, and therefore more likely to implement one common policy. In large-scale grids, nodes and neighborhoods should instead get a portion of the load that best matches their local policies for supporting and admitting grid jobs. This article describes information dissemination protocols that can distribute load in this way, without using load rebalancing through job migration, which is more difficult and costly in large-scale heterogeneous grids. Essentially, nodes adjust their advertising rates and aggressiveness to influence where jobs get scheduled. We report experimental results with example resource configurations in which each resource neighborhood determines its ideal grid load and disseminates accordingly. In turn, each neighborhood attracts the requisite amount of resource requests from the grid. Moreover, performance does not degrade: overall query satisfaction rates are within 9% of both adaptive dissemination protocols that use static adaptation policies, and static dissemination protocols that may be custom-tailored to specific resource and load distributions.