Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach

Yuqing Li, Wenkuan Dai, Xiaoying Gan, Haiming Jin, Luoyi Fu, Huadong Ma, Xinbing Wang

Abstract: Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursues <i>social cost minimization</i> over time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting the <i>spatial-temporal diversities</i> in <i>workload and resource cost</i> among federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges: <i>user deadline preference</i> and <i>ECs’ strategic behaviors</i> . We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hard. By leveraging user deadline tolerance, we develop a Lyapunov-based <i>deadline-driven</i> joint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. The <i>service priority</i> imposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicit <i>truthful bids</i> on workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.