27 research outputs found
A FUNCTIONAL SKETCH FOR RESOURCES MANAGEMENT IN COLLABORATIVE SYSTEMS FOR BUSINESS
This paper presents a functional design sketch for the resource management module of a highly scalable collaborative system. Small and medium enterprises require such tools in order to benefit from and develop innovative business ideas and technologies. As computing power is a modern increasing demand and no easy and cheap solutions are defined, especially small companies or emerging business projects abide a more accessible alternative. Our work targets to settle a model for how P2P architecture can be used as infrastructure for a collaborative system that delivers resource access services. We are focused on finding a workable collaborative strategy between peers so that the system offers a cheap, trustable and quality service. Thus, in this phase we are not concerned about solutions for a specific type of task to be executed by peers, but only considering CPU power as resource. This work concerns the resource management module as a part of a larger project in which we aim to build a collaborative system for businesses with important resource demandsresource management, p2p, open-systems, service oriented computing, collaborative systems
Multi-round Master-Worker Computing: a Repeated Game Approach
We consider a computing system where a master processor assigns tasks for
execution to worker processors through the Internet. We model the workers
decision of whether to comply (compute the task) or not (return a bogus result
to save the computation cost) as a mixed extension of a strategic game among
workers. That is, we assume that workers are rational in a game-theoretic
sense, and that they randomize their strategic choice. Workers are assigned
multiple tasks in subsequent rounds. We model the system as an infinitely
repeated game of the mixed extension of the strategic game. In each round, the
master decides stochastically whether to accept the answer of the majority or
verify the answers received, at some cost. Incentives and/or penalties are
applied to workers accordingly. Under the above framework, we study the
conditions in which the master can reliably obtain tasks results, exploiting
that the repeated games model captures the effect of long-term interaction.
That is, workers take into account that their behavior in one computation will
have an effect on the behavior of other workers in the future. Indeed, should a
worker be found to deviate from some agreed strategic choice, the remaining
workers would change their own strategy to penalize the deviator. Hence, being
rational, workers do not deviate. We identify analytically the parameter
conditions to induce a desired worker behavior, and we evaluate experi-
mentally the mechanisms derived from such conditions. We also compare the
performance of our mechanisms with a previously known multi-round mechanism
based on reinforcement learning.Comment: 21 pages, 3 figure