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GreenEdge: Joint Green Energy Scheduling and Dynamic Task Offloading in Multi-Tier Edge Computing Systems

As mobile edge computing (MEC) emerges as a paradigm to meet the ever-increasing computation demands from real-time Internet of Things (IoT) applications in 5 G era, the development trends of which are mainly divided into two, with one being MEC with advanced computing architectures, and the other being MEC with high efficiency for sustainable operations. We are committed to taking advantage of these two trends to explore a novel multi-tier edge computing scenario with hierarchical task offloading and green energy provisioning via leveraging the energy harvesting (EH) technique. Specifically, we focus on the key problem of joint task offloading and energy scheduling in such green multi-tier edge computing systems. We aim to minimize the task execution cost by jointly considering the system cost that covers latency, energy consumption, and cloud rental fees. By formulating the problem as a stochastic optimization problem, we invoke the Lyapunov technique to decompose the long-term optimization problem into a series of one-slot optimization problems which only use the current system information. To solve the one-slot optimization problem which is a mixed-integer linear problem (MILP) proved to be NP-hard, we first relax the integer variables into real ones to obtain the optimal fractional solutions. Considering the capacity of the physical resources of each edge server, we propose a resource-constrained randomized dependent rounding algorithm to properly round up or down the fractional variables to get a feasible yet near-optimal solution. We conduct rigorous theoretical analysis and extensive simulations to verify the superior performance of the proposed schemes.