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Internet supercomputing is an approach to solving partitionable,computation-intensive problems by harnessing the power of a vast number ofinterconnected computers. For the problem of using network supercomputing toperform a large collection of independent tasks, prior work introduced adecentralized approach and provided randomized synchronous algorithms thatperform all tasks correctly with high probability, while dealing withmisbehaving or crash-prone processors. The main weaknesses of existingalgorithms is that they assume either that the \emph{average} probability of anon-crashed processor returning incorrect results is inferior to $\frac{1}{2}$,or that the probability of returning incorrect results is known to \emph{each}processor. Here we present a randomized synchronous distributed algorithm thattightly estimates the probability of each processor returning correct results.Starting with the set $P$ of $n$ processors, let $F$ be the set of processorsthat crash. Our algorithm estimates the probability $p_i$ of returning acorrect result for each processor $i \in P-F$, making the estimates availableto all these processors. The estimation is based on the $(\epsilon,\delta)$-approximation, where each estimated probability $\tilde{p_i}$ of $p_i$obeys the bound ${\sf Pr}[p_i(1-\epsilon) \leq \tilde{p_i} \leqp_i(1+\epsilon)] > 1 - \delta$, for any constants $\delta >0$ and $\epsilon >0$chosen by the user. An important aspect of this algorithm is that eachprocessor terminates without global coordination. We assess the efficiency ofthe algorithm in three adversarial models as follows. For the model where thenumber of non-crashed processors $|P-F|$ is linearly bounded the timecomplexity $T(n)$ of the algorithm is $\Theta(\log{n})$, work complexity $W(n)$is $\Theta(n\log{n})$, and message complexity $M(n)$ is $\Theta(n\log^2n)$.

Technical Report: Estimating Reliability of Workers for Cooperative Distributed Computing