Resource calendaring for Mobile Edge Computing: Centralized and decentralized optimization approaches

Abstract

Mobile Edge Computing (MEC) is a key technology for the deployment of next generation (5G and beyond) mobile networks. The computational power it provides at the edge could allow providers to fulfill the requirements of use cases in need of ultra-low latency, high bandwidth, as well as real-time access to the radio network. However, this potential needs to be carefully administered as the edge is certainly limited in terms of computation capability, as opposed to the cloud which holds the promise of a virtually infinite power. MECs, though, could still try to exploit not only their local capacity, but also the one that the neighbor MECs could offer. Considering that the 5G scenario assumes an ultra-dense distribution of MECs, this possibility could be feasible, provided that we find an effective way to carefully allocate the resources available at each edge node.In this paper, we provide an optimization framework that considers several key aspects of the resource allocation problem with cooperating MECs. We carefully model and optimize the allocation of resources, including computation and storage capacity available in network nodes as well as link capacity. Specifically, our proposed model jointly optimizes (1) the user requests admission decision (2) their scheduling, also called calendaring (3) and routing as well as ( 4) the decision of which nodes will serve such user requests and (5) the amount of processing and storage capacity reserved on the chosen nodes. Both an exact optimization model and an effective heuristic, based on sequential fixing, are provided.Furthermore, we propose a distributed approach for our problem, based on the Alternating Direction Method of Multipliers (ADMM), so that resource allocation decisions can be made in a distributed fashion by edge nodes with limited overhead.We perform an extensive numerical analysis in several real-size network scenarios, using real positions for radio access points of a mobile operator in the Milan area. Results demonstrate that the heuristic performs close to the optimum in all considered network scenarios, while exhibiting a low computing time. This provides an evidence that our proposal is an effective framework for optimizing resource allocation in next-generation mobile networks.

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

“…Similarly, the location of an end-user running an application may be modeled by a suitable latency distribution. The convex optimization method has been used a lot to solve the problem of reducing energy use (Wu, 2019), (Fan, 2021), (Xiang, 2021). Under the time constraint, the offloading policy has been optimized based on the channel gains and the amount of energy used by local computing.…”

A mathematical optimization model using red deer algorithm for resource discovery in CloudIoT

“…Xiang et al 20 first solved the problem of resource calendars in mobile networks that can conduct mobile edge computing. Then, a detailed optimization model was considered to achieve an almost optimal result in actual‐size network scenarios.…”

“…The second group of studies were conducted by Alli et al, 19 Xiang et al, 20 Apostolopoulous et al, 21 and Murturi et al 22 on the discovery and allocation of resources in fog/edge platforms to optimize bandwidth, energy consumption, and latency. The third group aimed to reduce the overhead of cloud devices, energy consumption, and cost of discovering and allocating resources in combination with IoT and fog/edge by Kalantary et al, 23 Ramzanpoor et al, 24 and Islam et al, 25 Li et al, 26 and Goudarzi et al 27 In the fourth group, the studies Bharti et al, 28,29 Alzubi et al, 30 and Su et al 31 were conducted to examine the discovery and allocation of resources in IoT for optimizing memory consumption, response time, and power usage in transmissions between IoT devices.…”

Slice-Aware Resource Calendaring in Cloud-based Radio Access Networks

“…The paper [9] provide an optimization framework that considers several key aspects of the resource allocation problem with cooperating Mobile Edge Computing nodes. Proposed model jointly optimizes (1) the user requests admission decision (2) their scheduling, also called calendaring (3) and routing as well as (4) the decision of which nodes will serve such user requests and (5) the amount of processing and storage capacity reserved on the chosen nodes.…”