Data Orchestration Service Placement and Resource Allocation Scheme for Cloud-Edge System

Orchestration of Data as Services (ODS) at the Edge Layer (EL) in a Cloud-Edge-Seamless System (CESS) facilitates user access and avoids the long-distance transmission of massive raw data to Cloud Servers (CSs), thereby reducing network load. Building on this foundation, we argue that deploying both services and Data/Services Orchestration Programs (DSOPs), together with performing resource allocation at Edge Servers (ESs), further minimizing data processing time, service response time, and placement costs. To this end, we propose a novel service network architecture that integrates Service/DSOP placement and edge resource allocation to enhance overall system performance. First, a new service network architecture is proposed to jointly optimize Service/DSOP placement and resource allocation. Then, we design a Data-Driven Service/DSOP Placement (DDSDP) scheme that employs a Parameterized Deep$Q$-Network (P-DQN) to effectively tackle the hybrid action space optimization of such joint problem. Moreover, we develop a Demand-Calibrated Service/DSOP Placement (DCSDP) approach, which first leverages Long Short-Term Memory (LSTM) networks at the CS to capture spatio-temporal patterns of data and service demands, enabling ESs to dynamically train a demand-aware service/DSOP deployment model. Extensive simulations demonstrate that DDSDP and DCSDP significantly reduce service response time and adapt more effectively to network dynamics, achieving reductions of 47.89% and 53.88% compared to the baseline IFSP and P-DQN methods, respectively.