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.

The purpose of the research is to create a hybrid cloud platform that performs distributed computing tasks using high-performance servers and volunteer computing resources. The proposed platform uses a new task scheduling method, which is also presented in this paper. It uses a task stalling buffer to manage workload among the two grids without any additional information about the tasks. Since efficient task scheduling in these distributed systems is the actual problem, the system reliability issue is solved using a hybrid cloud architecture when both high-performance servers and volunteer computing resources are combined. The results of the experiment showed that the proposed solution solves the problem of balancing workload between two grids better than the standard scheduling algorithm. Computer study and experiments also showed that the proposed hybrid cloud tasks scheduling method with a task stalling buffer reduces up to 47.3 % of total task execution time. The outcome of this paper provides a background for future research on a task stalling buffer in hybrid cloud computing.

This paper presents a new algorithm for a batch of task makespan minimisation in heterogeneous multigrid computing. Heterogeneous grids are known to cause straggling task problem that increases task execution makespan. Existing task distribution algorithms solve this problem by using information about the compute node capacities or task sizes. However, such information may not always be available. Task stalling solves both problems. However, this method is described for queuing systems consisting of only two heterogeneous servers or grids. Our proposed algorithm is based on an improved task stalling method, allowing it to distribute tasks in systems consisting of two or more grids. Experiment results show reduced task execution makespan by up to 19,92% compared to FIFO. This allows us to conclude that the new algorithm is suitable for a batch of task makespan minimisation in heterogeneous multigrid computing.

Hybrid distributed computing sharing platform