  Collaborative Edge Computing (CEC) is an effective method that improves the
performance of Mobile Edge Computing (MEC) systems by offloading computation
tasks from busy edge servers (ESs) to idle ones. However, ESs usually belong to
different MEC service providers so they have no incentive to help others. To
motivate cooperation among them, this paper proposes a cooperative mechanism
where idle ESs can earn extra profits by sharing their spare computational
resources. To achieve the optimal resource allocation, we formulate the social
welfare maximization problem as a Markov Decision Process (MDP) and decompose
it into two stages involving the allocation and execution of offloaded tasks.
The first stage is solved by extending the well-known Deep Deterministic Policy
Gradient (DDPG) algorithm. For the second stage, we first show that we only
need to decide the processing order of tasks and the utilized computational
resources. After that, we propose a dynamic programming and a Deep
Reinforcement Learning (DRL)-based algorithm to solve the two types of
decisions, respectively. Numerical results indicate that our algorithm
significantly improves social welfare under various situations.
