Department of Electrical and Electronics Engineering
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925
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Item Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services(IEEE, 2023-03) Tripathi, ShardaThe combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networksItem VERA: Resource Orchestration for Virtualized Services at the Edge(IEEE, 2022) Tripathi, ShardaThe combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios, and show an improvement of around 60% in the CPU budget violation rate with respect to RL based standard multi-agent framework.Item A Context-Aware Radio Resource Management in Heterogeneous Virtual RANs(IEEE, 2022-03) Tripathi, ShardaNew-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.