Joint Data Placement and Service Deployment in Distributed Cloud-Edge Environment

How to efficiently deploying the service components of a data-intensive application on cloud and edge servers to minimize its latency is one of the main challenges for service providers. Most existing studies consider either service deployment or data placement, rather than their joint optimization. This work considers the driving relationship between data and services in a heterogeneous environment including remote cloud and nearby edge servers, and aims to obtain a desired data placement and service deployment scheme while meeting user requirements for service quality. First, we formulate the problem and decouple data placement from service deployment by polynomial reduction. Then, a priority-based data placement strategy is proposed, which can generate a data placement scheme. After that, the original problem is transformed into a classical assignment problem, and a service deployment strategy based on an improved Hungarian algorithm is proposed to obtain a service deployment scheme. Then, a dynamic adjustment strategy based on response weight is proposed to dynamically adjust the data placement and service deployment scheme in order to reduce response latency, and obtain the final scheme. Finally, a series of comparative experiments were conducted, pitting our algorithms against several baseline and SOTA algorithms. The results show that the proposed algorithms, in comparison to other algorithms, is capable of generating superior data placement and service deployment schemes to significantly reduce response latency.