A new Shuffled Genetic-based Task Scheduling Algorithm in Heterogeneous Distributed Systems

Distributed systems such as Grid- and Cloud Computing provision web services to their users in all of the world. One of the most important concerns which service providers encounter is to handle total cost of ownership (TCO). The large part of TCO is related to power consumption due to inefficient resource management. Task scheduling module as a key component can has drastic impact on both user response time and underlying resource utilization. Such heterogeneous distributed systems have interconnected different processors with different speed and architecture. Also, the user application which is typically presented in the form of directed acyclic graph (DAG) must be executed on this type of parallel processing systems. Since task scheduling in such complicated systems belongs to NP-hard problems, existing heuristic approaches are no longer efficient. Therefore, the trend is to apply hybrid meta-heuristic approaches. In this paper, we extend a meta-heuristic shuffled genetic-based task scheduling algorithm to minimize total execution time, makespan, of user application. In this regard, we take benefit of other heuristics such as Heterogeneous Earliest Finish Time (HEFT) approach to generate smart initial population by applying a new shuffle operator which makes a fortune to explore feasible and promising individuals in the search space. We also conduct other genetic operators in right way to produce final near to optimal solution. To reach concrete results we have conducted several scenarios. Our proposed algorithm outperforms in term of average makespan compared with other existing approaches such as HEFT versions and QGARAR.