Market Analysis of MEC-Assisted Beyond 5G Ecosystem

Abstract

The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators' decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players' revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.

MEC/Cloud Orchestrator to Facilitate Private/Local Beyond 5G with MEC and Proof-of-Concept Implementation

“…However, there is a problem in that these references have many conceptual levels and are not yet mature, because there are many uncertainties with the methods of deploying an application possessed by third parties. Among these, authors have discussed the business model of MEC, proposed a new local/private operator who should hold MEC, and performed numerical analysis by simulation [ 25 , 26 , 27 , 28 ]. The present paper discusses who holds MEC/Cloud Orchestrator, which has not been discussed yet.…”

“…End users would like to select the more inexpensive computation environments. Here, the cost model based on [ 28 ] was calculated by numerical analysis conducted with the actual field measurement results. The actual results in the testbed field were obtained by measuring the performance on the RAN side using XCAL.…”

“…The cloud side needs to evaluate both the latency effect and cost increase, whether the network bandwidth or computing resource is added. The MEC latency [ 28 ] is expressed as where denotes the number of MEC resources decided by the private (local) telecom operator’s strategy, (CPU cycles) represents the task converted from information with task weight , computation resource is expressed as , and is the processing queue on the MEC side as seen in Equation ( 1 ). On the cloud side, the latency and cost are expressed as, where denotes backhaul capacity, is the number of cloud resources, is computing resources, and denotes the processing queue in the cloud, as seen in Equation ( 2 ).…”

“…Specifically, we first proposed a new Private/Local Operator to deploy MEC and analyzed the number of MEC that could maximize revenue [ 25 , 26 ]. In order to consider the impact of MEC on other operators, Private/Local Operator and Cloud Owner were discussed and analyzed using game theory [ 27 , 28 ]. The above results allowed us to establish an MEC ecosystem.…”

“…However, what remains is the uncertainty of MEC management, which will affect operators’ operational decision-making about how to manage the life cycle of third-party applications in MEC. In this light, this paper builds on previous work [ 28 ], proposes a detailed MEC/Cloud Orchestrator to make it work, and provides a PoC implementation of the E2E system. Specifically, the contributions of this paper are unique and distinctive in two aspects.…”