Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services

TLDR

Real‑time cloud services for vehicular clients must meet strict delay and jitter constraints, and fog computing distributes small self‑powered data centers between remote clouds and vehicles to deliver such services. This work proposes and evaluates an energy‑efficient adaptive resource scheduler for Networked Fog Centers that maximizes communication‑plus‑computing energy efficiency while satisfying hard QoS requirements. The scheduler, operating at the vehicular edge via I2V TCP/IP links, jointly performs admission control, minimum‑energy traffic dispatch, adaptive VM reconfiguration/consolidation, and traffic injection control based on locally measured TCP/IP states. The adaptive scheduler is scalable, guarantees hard QoS on rates, jitter, and delays, and outperforms state‑of‑the‑art schedulers under client mobility, wireless fading, and reconfiguration costs in both synthetic and real‑world workloads.

Abstract

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.