EQGSA-DPW: A Quantum-GSA Algorithm-Based Data Placement for Scientific Workflow in Cloud Computing Environment

The processing of scientific workflow (SW) in geo-distributed cloud computing holds significant importance in the placement of massive data between various tasks. However, data movement across storage services is a main concern in the geo-distributed data centers, which entails issues related to the cost and energy consumption of both storage services and network infrastructure. Aiming to optimize data placement for SW, this paper proposes EQGSA-DPW a novel algorithm leveraging quantum computing and swarm intelligence optimization to intelligently reduce costs and energy consumption when a SW is processed in multi-cloud. EQGSA-DPW considers multiple objectives (e.g., transmission bandwidth, cost and energy consumption of both service and communication) and improves the GSA algorithm by using the log-sigmoid transfer function as a gravitational constant G and updating agent position by quantum rotation angle amplitude for more diversification. Moreover, to assist EQGSA-DPW in finding the optima, an initial guess is proposed. The performance of our EQGSA-DPW algorithm is evaluated via extensive experiments, which show that our data placement method achieves significantly better performance in terms of cost, energy, and data transfer than competing algorithms. For instance, in terms of energy consumption, EQGSA-DPW can on average achieve up to \(25\%\), \(14\%\), and \(40\%\) reduction over that of GSA, PSO, and ACO-DPDGW algorithms, respectively. As for the storage services cost, EQGSA-DPW values are the lowest.