Big Data Mining Using Public Distributed Computing

Abstract

puting resources to projects. Research show that public distributed computing has the required potential and capabilities to handle big data mining tasks. Considering that one of the biggest advantages of such computational model is low computational resource costs, this raises the question of why this method is not widely used for solving such today's computational challenges as big data mining. The purpose of this paper is to overview public distributed computing capabilities for big data mining tasks. The outcome of this paper provides the foundation for future research required to bring back attention to this low-cost public distributed computing method and make it a suitable platform for big data analysis.

Application of a Task Stalling Buffer in Distributed Hybrid Cloud Computing

“…It can run virtualised, in parallel, or for GPU-based applications. Furthermore, it can perform big data mining tasks using consumer devices or company servers [25]. BOINC performs computations only when the CPU is idle.…”

“…This solution can allow organisations to use the computer resources available from employees of the company without disrupting any ongoing work. Since company employee computer CPUs are idle 99 % of the time [25], this solution may solve the computational resource demand problem.…”