A Budget-constrained Service Deployment Strategy based on Cost Allocation in Cloud-Edge Environment

Cloud-edge collaboration is an emerging approach that combines cloud computing and edge computing. This combination holds great potential for enhancing system efficiency and reliability, as well as mitigating processing costs and latency. Existing research focuses on task scheduling and service deployment in cloud or edge environments, aiming to achieve different optimization goals. However, traditional deployment methods are no longer suitable for the current used cloud-edge collaboration model, which does not consider the heterogeneity of the cloud-edge, the constrained relationship between cost and latency, and the distribution of user access. To this end, this paper proposes a service deployment scheme to minimize latency in a heterogeneous cloud-edge environment. The scheme first introduces a cloud-edge pre-distribution mechanism (ACEP) to reasonably divide the services among the cloud or edge servers, and then employs a global heuristic latency optimization algorithm (GLOA) and a cost allocation strategy (CACS) to find the best deployment strategy. The effectiveness of our proposed method is evaluated by comparing it with existing algorithms, which shows its superiority in terms of latency.