Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency

Abstract :  Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Networ k (D-RAN) that relies on non-cooperative ENs equipped with oneway uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finitecapacity two-way fronthaul links.Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.
 ? To achieve this, the MNOs would be required to incur exceptionally high operational expenditures (OPEX) and capital expenditures (CAPEX) for deploying more base stations or maintaining and operating the existing stations. ? In, a comprehensive assessment was carried out to compare the existing radio resource management schemes proposed for LTE/LTE-A femtocell and relay networks, in terms of interference mitigation, radio resource utilization, fairness, complexity, and QoS. ? The complexity analysis of the algorithms and performance comparison with other existing schemes in terms of the QoS (number of expensive inter-cluster handover) and resource (RRH, hosts, and energy) consumption was demonstrated.
 ? In, the authors tackled the optimization of functional split for collaborative computing systems equipped with a packet-based fronthaul network. In, the authors addressed the task allocation and traffic path planning problem for a C-RAN system under the assumption that the service latency consists of task processing delay and path delay only on fronthaul links. ? To tackle the formulated problems, which turn out to be non-convex, we adopt fractional programming (FP) and matrix FP . We present extensive numerical results that confirm the convergence of the proposed optimization algorithms, the advantages of C-RAN architecture as compared to D-RAN, and the impact of collaborative cloud and edge computing on latency with C-RAN.
 • Several research works have proposed different methodologies for effective resource management in C-RAN. • This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. • The objective of resource management is to utilize the limited radio frequency spectrum resources and radio network infrastructure with maximum efficiency. Different resource allocation mechanisms have been proposed for efficient resource management in C-RAN. • They proposed a combined bin packing method (BPM) and modified the best fit decreasing (MFBD) algorithm to solve this problem.
 ? In a D-RAN, ENs perform local signal processing for channel encoding and decoding. Thus, the overall performance 2 can be degraded by interference in dense networks. ? In this paper, we propose integrating collaborative fractional cloudedge offloading within a cloud radio access network (CRAN) architecture, while accounting for the contributions of both uplink and downlink. In a C-RAN, joint signal processing, in the form of cooperative precoding and detection, at the CP enables effective interference management. ? We provide extensive numerical results in Sec. V to validate the performance gain of the proposed architecture as compared to the D-RAN solution.

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