Resource Allocation Method with Workload-time Windows for Serverless Applications inCloud-edge Collaborative Environment

With the increasingly diverse computational demands in the cloud-edge collaborative environment,the traditional computing architecture based on virtual machines as the smallest unit of resources exhibits inflexibility and low cost-effectiveness.Serverless computing,as an emerging computing architecture with excellent scalability and flexibility,provides a new perspective to address these issues.In response to the resource allocation problem with workload-time windows for serverless applications in cloud-edge collaborative environments,this study proposes a resource allocation method based on rule-driven co-evolution algorithm(RARCA).This method considers the workload at a certain resource adjustment moment and in the foreseeable future,employing a rule-driven distributed resource updating mechanism to achieve dynamic allocation and adjustment of computing resources.Additionally,by leveraging the information sharing and cooperative optimization capabilities of the co-evolution mechanism,the algorithm efficiently searches for globally optimal resource allocation solutions,significantly improving the real-time and effectiveness of the overall resource allocation method.Experimental results demonstrate that RARCA can achieve superior resource allocation solutions with decision times in seconds,outperforming baseline methods by 2.8% to 14.5% in resource allocation performance.