An Evolutionary Multitasking Algorithm for Cloud Computing Service Composition

Service composition is a convincing approach for rapidly constructing large-scale distributed applications in public clouds. With the rapid increase of the composite service requests from many concurrent clients in public clouds, it is critical to perform quality of service (QoS) aware cloud computing service composition (CCSC) efficiently. To address this issue, many approaches have been proposed. However, it remains a key challenge to improve the throughput and the solution quality of a CCSC solver. In this paper, we propose a novel algorithm, namely evolutionary multitasking algorithm for CCSC problem (EMA-CCSC), based on the evolutionary multitasking algorithm. Unlike existing CCSC solvers which have to pool the composite service requests in the waiting queue first and then solve them once a time, the proposed EMA-CCSC is able to optimize two CCSC tasks concurrently. As a result, it can deal with more requests at a fixed period of time. Based on the QWS data set including 2507 real Web services, experiments have been conducted by solving a sequence of 1188 randomly generated CCSC tasks with different sizes and structures. The results indicate that EMA-CCSC outperforms 7 out of 9 compared algorithms with different characteristics, even though it spends only half of their computing costs. We can draw the conclusion from the extensive experiments that the EMA-CCSC approach is competitive in both solution quality and time efficiency.