Multi-objective Optimizations during Parallel Processing in a Dynamic Heterogeneous Cloud Environment

In the quest of fulfilling the rapidly increasing needs for processing power, while avoiding the expensive supercomputers, scientists look for cheaper parallel processing alternatives and technologies such as those found on GRID, clusters and similar ones. With the emergence of cloud, it was a matter of time when this will be done on cloud as well. However, cloud has its unique nature. On one side, its servers are hybrid in terms of their SOA as well as the maximum guaranteed processing power they offer. On the other side the Internet (the medium by which the cloud services are offered by default) is often unpredictable and dynamic. Furthermore, during our parallel processing, simultaneously we want to optimize the time and price, goals often in contradiction to each other. Three algorithms are studied and compared with the optimal solution, including our proposed cloud adopted feedback algorithm, which outperformed the others.