Statistical selection algorithm for peer-to-peer system

Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo et. al. (2002) has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This improved algorithm is developed based on the simulation of the real multicast environment. Modifications have been added in the improved algorithm to ensure that the unique pivot that never been used before is selected every time, and to solve problem that involve rank for certain key value that occur in more than one participant. Four performance measures have been conducted for the purpose of performance analysis between original and improved algorithm. These measures include probability of failure, number of messages needed, number of rounds needed and execution time. As a result, the probability of failure for the newly improved algorithm is 3.2% while the original algorithm is 19.2% without much overhead in increasing the number of messages and number of rounds needed

Distributed selectsort sorting algorithms on broadcast communication networks

In this paper, a distributed selectsort algorithm and a parameterized selectsort algorithm are presented to be applied on distributed systems for cases when N>> P where N is the number of elements to be sorted and P is the number of processors in the system. The distributed system considered in this paper uses a broadcasting channel for communication between processors. We show that the number of messages required for the parameterized selectsort algorithm is independent of N and is of complexity O(P), which is optimal in a distributed system with P processors. Furthermore, the amount of communication required in terms of elements is N + O(P 3) and the computation time complexity is O((N/P)lgN + P21g(N/P)). Hence, when N>/p3, the computation time complexity is O((N/P)lgN), which is optimal using P processors. In addition, this parameterized algorithm provides us with a parameter K such that by choosing the value of K allows us to trade among processing requirement, memory requirement, and communication requirement. It is shown that this parameterized algorithm can reduce the communication requirements significantly while only slightly increasing the computation requirements