Joint Offloading and Resource Allocation for Collaborative Cloud Computing With Dependent Subtask Scheduling on Multi-Core Server

Collaborative cloud computing (CCC) has emerged as a promising paradigm to support computation-intensive and delay-sensitive applications by leveraging MEC and MCC technologies. However, the coupling between multiple variables and subtask dependencies within an application poses significant challenges to the computation offloading mechanism. To address this, we investigate the computation offloading problem for CCC by jointly optimizing offloading decisions, resource allocation, and subtask scheduling across a multi-core edge server. First, we exploit latency to design a subtask dependency model within the application. Next, we formulate a System Energy-Time Cost (<inline-formula><tex-math notation="LaTeX">$SETC$</tex-math></inline-formula>) minimization problem that considers the trade-off between time and energy consumption while satisfying subtask dependencies. Due to the complexity of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Maximum Local Searching Offloading (MLSO) and Sequential Searching Offloading (SSO), to jointly optimize offloading decisions and resource allocation. We then model dependent subtask scheduling across the multi-core edge server as a Job-Shop Scheduling Problem (JSSP) and propose a Genetic-based Task Scheduling (GTS) algorithm to achieve optimal dependent subtask scheduling on the multi-core edge server. Finally, our simulation results demonstrate the effectiveness of the proposed MLSO, SSO, and GTS algorithms under different parameter settings.