Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments. • We introduce a novel two-stage sequential queue model tailored for partial computation offloading within Mobile Edge Cloud Computing (MECC) networks. • We employ the Deep Deterministic Policy Gradient (DDPG) algorithm to dynamically allocate computational resources between edge and cloud servers. Enhancements to the conventional DDPG framework include the integration of Gated Recurrent Unit (GRU) layers, adept at capturing temporal dependencies among queues. • The extensive simulations conducted under fluctuating network conditions, incorporating variable task arrival rates (TAR), service times (ST), and computational speeds, vividly demonstrate the robustness and superior performance of the proposed approach.