Abstract

With the rise in state-of-the-art communication modes for vehicles such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to cloud (V2C), modern vehicles are increasingly being connected to cloud and fog/edge nodes. These vehicle connectivity modes have enabled the realization of Vehicular Edge Computing (VEC) paradigm, whereby vehicles can leverage fog/edge node resources for storage/computation. In a VEC system, vehicles receive very important and large quantity of data from edge nodes, which is termed as data delivery. In addition, edge nodes can execute some services and send the results back to the vehicle, which is called service delivery. Fast and efficient edge resource allocation for data/service delivery is important in order to serve as many vehicles as possible in the VEC system. However, edge resource allocation is complex with large number of edges and vehicles, while also considering vehicle flow parameters. In this work, we propose Edge-Pairwise Optimal Data/Service Delivery (E-PODS), which is a fast and efficient heuristic for data/service delivery. Through experiments with synthetic and real vehicular traces, we demonstrate that E-PODS is considerably faster than the optimal approach, while making resource allocations that are close to optimal in terms of total edge bandwidth cost and number of serviced vehicles.

Abstract

he paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.

Abstract

In many application scenarios, data consumed by real-time tasks are required to meet a maximum age, or freshness, guarantee. In this paper, we consider the end-to-end freshness constraint of data that is passed along a chain of tasks in a uniprocessor setting. We do so with few assumptions regarding the scheduling algorithm used. We present a method for selecting the periods of tasks in chains of length two and three such that the end-to-end freshness requirement is satisfied, and then extend our method to arbitrary chains. We perform evaluations of both methods using parameters from an embedded benchmark suite (E3S) and several schedulers to support our

Abstract

Executing multiple applications on a single MPSoC brings the major challenge of satisfying multiple quality requirements regarding real-time, energy, and so on. Hybrid application mapping denotes the combination of design-time analysis with run-time application mapping. In this article, we present such a methodology, which comprises a design space exploration coupled with a formal performance analysis. This results in several resource reservation configurations, optimized for multiple objectives, with verified real-time guarantees for each individual application. The Pareto-optimal configurations are handed over to run-time management, which searches for a suitable mapping according to this information. To provide any real-time guarantees, the performance analysis needs to be composable and the influence of the applications on each other has to be bounded. We achieve this either by spatial or a novel temporal isolation for tasks and by exploiting composable networks-on-chip (NoCs). With the proposed temporal isolation, tasks of different applications can be mapped to the same resource, while, with spatial isolation, one computing resource can be exclusively used by only one application. The experiments reveal that the success rate in finding feasible application mappings can be increased by the proposed temporal isolation by up to 30% and energy consumption can be reduced compared to spatial isolation.