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Akshaj Gupta

Akshaj Gupta supervised by Dr. Deepak Gangadharan received his Master of Science – Dual Degree in Computer Science and Engineering (CSE). Here’s a summary of her research work on Fast Heuristic Algorithms for Efficient Allocation of RSU resources in Vehicular Edge

Computing:

The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. 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 modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers there improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data/service delivery, an efficient edge resource allocation framework needs to be developed. Prior syst level VEC optimization frameworks for edge resource allocation did not consider which vehicles are present in the edge coverage region around the same time (which we call vehicle overlaps), which resulted in overestimation of resources. In a VEC system, vehicles receive a 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 a large number of edges and vehicles, while also considering vehicle flow parameter In Chapter 3, 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. In Chapter 4, we propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data/service delivery to connected vehicles while considering the traffic flow and overlaps in vehicle presence at coverage region. Then, we propose an efficient heuristic to deliver data/service delivery based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering overlaps and also using the proposed heuristic with synthetic and real world traffic data. In Chapter 5, we present a novel scheduling technique for roadside clouds in vehicular networks, addressing high mobility and resource limitations. The Oldest-Request-First (ORF) algorithm prioritizes older requests and redirects those near RSU edges, reducing response times and VM migrations. Simulations show ORF outperforms traditional methods, enhancing resource management by serving more requests within delay constraints.

December 2024