Abstract
Traffic jam has been and will remain a major problem in most cities around the world. We view this situation as a computation opportunity and propose to build the cloud-computing facilities on the top of jammed cars and other vehicles to turn the energy and other resources that otherwise would be wasted into computing power. Specifically, we define the vehicular mobile cloudlet as a group of nearby vehicular mobile devices congested in the traffic jams while connected by short-range communications. Based on local mobile cloudlets of congested vehicles and available remote cloud-computing resources, we propose and evaluate the JamCloud, a system to collect and aggregate the computation capacities of congested vehicles in the city. For this newly-conceived novel cloud system, the fundamental problems are how much computation capacity the mobile cloudlets have and what is the overall achievable performance of the whole JamCloud system. Based on the three realistic large-scale urban vehicular mobility traces, we analyze and model the vehicular mobility patterns as well as the computation capacity in both the mobile cloudlet and system wide. Specifically, by analyzing the patterns of staying time, resident number, and incoming and outgoing of vehicles in the regions with traffic jams, we model the mobile cloudlet as a periodic non-homogeneous immigration-death process, which predicts its computational capability with accuracy above 90%. Based on the observed strong Poisson features of mobile cloudlets, we further propose a queueing network model to characterize the overall performance of JamCloud with the computing resources of multiple mobile cloudlets and remote clouds. Our study thus reveals the microscopic computation capability of local cloudlets as well as the overall and asymptotic performance of the JamCloud, which provides foundational understanding to design, such systems in practice. With the inevitably growing trend of making vehicles electric, and in particular with the forthcoming 5th generation (5G) mobile communication technology, the time has finally come to turn JamCloud into reality.
Highlights
OUR CONTRIBUTIONS Against the above background, In this paper, we propose and evaluate JamCloud, a system based on local mobile cloudlets of congested vehicles and remote cloud computing centers, to collect and aggregate the computation capacities of congested vehicles
VEHICULAR MOBILITY MODEL FOR MOBILE CLOUDLET Since the computation capacity can be deduced from vehicular mobility, we propose a mathematical model to characterize it
Based on our observations in the analysis of vehicular mobile cloudlet, we propose a queueing network model for the whole system and define some appropriate metrics to evaluate the overall performance of satisfying computation demand
Summary
Rather than submitting its baseband signal processing tasks to a remote cloud center [9]–[11], a BS or RSU can send them to the mobile cloudlet formed by congested vehicles in a nearby intersection for completion This utilizes the idle computation power of the congested vehicles and helps to achieve low network latency which is one of the critical metrics of 5G networks. DATA TRACE AND PROCESSING We investigate the temporal and spatial dynamic computation capacity of the JamCloud system using realistic vehicular mobility traces of Beijing, Nanjing and Shanghai We choose these cities to study because traffic jam is a common scene around these three cities. Based on the above justifications, in the sequel, we will use the term vehicle, rather than taxi, in our discussions
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