Service Configuration Optimization in Edge–Cloud Networks Leveraging Log Analysis

The edge–cloud collaboration network is promising to support complex requirements with temporal constraints, where a requirement can be achieved through the composition of computation-demanding and delay-sensitive services. In this setting, most services should be optimally configured at the network edge, in order to decrease service response latency and reducing network resource consumption. To address this challenge, this article proposes an optimal service configuration mechanism, where temporal constraints between services are mined from event logs through our temporal interval discovery mechanism. Service configuration is formulated as a constrained multiobjective optimization problem, which is solved by our improved nondominated sorting genetic
algorithm II
. Extensive experiments are conducted, and evaluation results demonstrate that our approach can find the close-to-optimal service configuration in comparison with the state-of-the-art techniques in terms of delay sensitivity and energy efficiency, especially when edge nodes can co-host a relatively large number of services.