Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

Abstract

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resource availability 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-based 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 numerically demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our realtime implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks.

Fair Resource Allocation in Virtualized O-RAN Platforms

“…Put it differently, these functions model the benefits from dispersing the load across multiple servers instead of using only one. In line with prior works, e.g., [85], and based on our measurements (Sec. 6) we consider these functions to be non-negative and concave on 𝒙.…”

“…Fairness is a key metric in resource management and has been extensively applied in cloud computing [27,87] and communication systems [5,52], among many others [68,71,75]. More recently, [59,60] focused on max-min throughput fairness in RANs, e.g., via spectrum management; [85] studied the fair allocation of computing capacity to vRAN functions and edge services; [67] considered cost-fairness in multi-tenant O-RANs where operators lease computing for their vBS functions; while [37,45,64] focus on virtualization and slicing. These interesting works, however, do not consider the inherent system and user dynamics in vRANs and/or do not provide fairness guarantees.…”

Fair Resource Allocation in Virtualized O-RAN Platforms

“…Fairness is a key metric in resource management and has been extensively applied in cloud computing [27,87] and communication systems [5,52], among many others [68,71,75]. More recently, [59,60] focused on max-min throughput fairness in RANs, e.g., via spectrum management; [85] studied the fair allocation of computing capacity to vRAN functions and edge services; [67] considered cost-fairness in multi-tenant O-RANs where operators lease computing for their vBS functions; while [37,45,64] focus on virtualization and slicing. These interesting works, however, do not consider the inherent system and user dynamics in vRANs and/or do not provide fairness guarantees.…”

AIRIC: Orchestration of Virtualized Radio Access Networks With Noisy Neighbours

“…SIRA is purposely designed to orchestrate optimal resources across vBS instances under the assumption of full computing isolation between instances. Consequently, SIRA represents upper bounds attainable by existing works on vRAN CPU orchestration such as [19], [49].…”

“…Making a decisive step forward towards cost-effective implementation of virtual and Open RAN, vrAIn [18] was the first work to jointly optimize the CPU allocation and radio policies for a given number vBSs deployment. More recently, [49] provided a solution to allocate computing resources among a vBS instance and a vertical service. They are considered as the most pioneer and relevant benchmark related to our work.…”