Abstract
The assignment and execution of tasks over the Internet is an inexpensive solution in contrast with supercomputers. We consider an Internet-based Master-Worker task computing approach, such as SETI@home. A master process sends tasks, across the Internet, to worker processors. Workers execute, and report back a result. Unfortunately, the disadvantage of this approach is the unreliable nature of the worker processes. Through different studies, workers have been categorized as either malicious (always report an incorrect result), altruistic (always report a correct result), or rational (report whatever result maximizes their benefit). We develop a reputation-based mechanism that guarantees that, eventually, the master will always be receiving the correct task result. We model the behavior of the rational workers through reinforcement learning, and we present three different reputation types to choose, for each computational round, the most reputable from a pool of workers. As workers are not always available, we enhance our reputation scheme to select the most responsive workers. We prove sufficient conditions for eventual correctness under the different reputation types. Our analysis is complemented by simulations exploring various scenarios. Our simulation results expose interesting trade-offs among the different reputation types, workers availability, and cost.
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Notes
- 1.
We call these workers malicious for compliance with Volunteer Computing [4] literature. This must not be confused with Byzantine malice assumed in classical distributed computing.
- 2.
In BOINC, honesty means that the worker’s task result agrees with the majority, while in our work this decision is well-founded, since the master audits.
- 3.
The omitted proofs can be found at http://arxiv.org/abs/1603.04394.
- 4.
As we have seen experimentally, first the system reaches a reliable state and then \(p_{\mathcal {A}} =p_{\mathcal {A}} ^{min}\).
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Acknowledgments
Supported in part by MINECO grant TEC2014- 55713-R, Regional Government of Madrid (CM) grant Cloud4BigData (S2013/ICE-2894, co- funded by FSE & FEDER), NSF of China grant 61520106005, EC H2020 grants ReCred and NOTRE, U. of Cyprus (ED-CG2015), the MECD grant FPU2013-03792 and Kean University RTR2016.
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Christoforou, E., Fernández Anta, A., Georgiou, C., Mosteiro, M.A. (2016). Internet Computing: Using Reputation to Select Workers from a Pool. In: Abdulla, P., Delporte-Gallet, C. (eds) Networked Systems. NETYS 2016. Lecture Notes in Computer Science(), vol 9944. Springer, Cham. https://doi.org/10.1007/978-3-319-46140-3_11
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