5 research outputs found
Applying the dynamics of evolution to achieve reliability in master-worker computing
We consider Internet-based master-worker task computations, such as SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy, and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics, and we study the conditions under which the master can reliably obtain task results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured and bounds the expected convergence time to that behavior. The analysis is complemented with illustrative simulations.This work is supported by the Cyprus Research Promotion Foundation grant TΠE/ΠΛHPO/0609(BE)/05, the National Science Foundation (CCF-0937829, CCF-1114930), Comunidad de Madrid grants S2009TIC-1692 and MODELICO-CM, Spanish PRODIEVO and RESINEE grants and MICINN grant EC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002.Publicad
Crowd computing as a cooperation problem: an evolutionary approach
Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. Here, we take a step forward by studying cooperation in the context of crowd computing. We introduce a model loosely based on Principal-agent theory in which people (workers) contribute to the solution of a distributed problem by computing answers and reporting to the problem proposer (master). To go beyond classical approaches involving the concept of Nash equilibrium, we work on an evolutionary framework in which both the master and the workers update their behavior through reinforcement learning. Using a Markov chain approach, we show theoretically that under certain----not very restrictive-conditions, the master can ensure the reliability of the answer resulting of the process. Then, we study the model by numerical simulations, finding that convergence, meaning that the system reaches a point in which it always produces reliable answers, may in general be much faster than the upper bounds given by the theoretical calculation. We also discuss the effects of the master's level of tolerance to defectors, about which the theory does not provide information. The discussion shows that the system works even with very large tolerances. We conclude with a discussion of our results and possible directions to carry this research further.This work is supported by the Cyprus Research Promotion Foundation grant TE/HPO/0609(BE)/05, the National Science Foundation (CCF-0937829, CCF-1114930), Comunidad de Madrid grant S2009TIC-1692 and MODELICO-CM, Spanish MOSAICO, PRODIEVO and RESINEE grants and MICINN grant TEC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002.Publicad
Achieving reliability and fairness in online task computing environments
Mención Internacional en el título de doctorWe consider online task computing environments such as volunteer computing platforms running
on BOINC (e.g., SETI@home) and crowdsourcing platforms such as Amazon Mechanical
Turk. We model the computations as an Internet-based task computing system under the masterworker
paradigm. A master entity sends tasks across the Internet, to worker entities willing to
perform a computational task. Workers execute the tasks, and report back the results, completing
the computational round. Unfortunately, workers are untrustworthy and might report an incorrect
result. Thus, the first research question we answer in this work is how to design a reliable masterworker
task computing system. We capture the workers’ behavior through two realistic models:
(1) the “error probability model” which assumes the presence of altruistic workers willing to
provide correct results and the presence of troll workers aiming at providing random incorrect
results. Both types of workers suffer from an error probability altering their intended response.
(2) The “rationality model” which assumes the presence of altruistic workers, always reporting
a correct result, the presence of malicious workers always reporting an incorrect result, and the
presence of rational workers following a strategy that will maximize their utility (benefit). The
rational workers can choose among two strategies: either be honest and report a correct result,
or cheat and report an incorrect result. Our two modeling assumptions on the workers’ behavior
are supported by an experimental evaluation we have performed on Amazon Mechanical Turk.
Given the error probability model, we evaluate two reliability techniques: (1) “voting” and (2)
“auditing” in terms of task assignments required and time invested for computing correctly a set
of tasks with high probability. Considering the rationality model, we take an evolutionary game
theoretic approach and we design mechanisms that eventually achieve a reliable computational
platform where the master receives the correct task result with probability one and with minimal
auditing cost. The designed mechanisms provide incentives to the rational workers, reinforcing
their strategy to a correct behavior, while they are complemented by four reputation schemes that
cope with malice. Finally, we also design a mechanism that deals with unresponsive workers by
keeping a reputation related to the workers’ response rate. The designed mechanism selects the
most reliable and active workers in each computational round. Simulations, among other, depict
the trade-off between the master’s cost and the time the system needs to reach a state where
the master always receives the correct task result. The second research question we answer in
this work concerns the fair and efficient distribution of workers among the masters over multiple computational rounds. Masters with similar tasks are competing for the same set of workers at
each computational round. Workers must be assigned to the masters in a fair manner; when the
master values a worker’s contribution the most. We consider that a master might have a strategic
behavior, declaring a dishonest valuation on a worker in each round, in an attempt to increase its
benefit. This strategic behavior from the side of the masters might lead to unfair and inefficient assignments
of workers. Applying renown auction mechanisms to solve the problem at hand can be
infeasible since monetary payments are required on the side of the masters. Hence, we present an
alternative mechanism for fair and efficient distribution of the workers in the presence of strategic
masters, without the use of monetary incentives. We show analytically that our designed mechanism
guarantees fairness, is socially efficient, and is truthful. Simulations favourably compare
our designed mechanism with two benchmark auction mechanisms.This work has been supported by IMDEA Networks Institute and the Spanish Ministry of Education grant FPU2013-03792.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Alberto Tarable.- Secretario: José Antonio Cuesta Ruiz.- Vocal: Juan Julián Merelo Guervó
Combinatorial Agency with Audits ∗
This paper studies the question of how to overcome inefficiencies due to hidden actions in a rational milieu, such as a grid computing system with open clientele. We consider the so-called principal-agent model known from economic theory, where the members (or agents) of a distributed system collaborate in complex ways. We adopt the perspective of the principal and investigate auditing mechanisms that incentivize participants to contribute more to a common project. The paper analyzes the tradeoff between auditing costs and the scarce effort exerted by the participants, and presents optimal solutions for this optimization problem in different scenarios.