Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Abstract

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

TOFFEE: Task Offloading and Frequency Scaling for Energy Efficiency of Mobile Devices in Mobile Edge Computing

“…2: Obtain the ∑ n i=1 φ i D l i (t) by solving the convex optimization problem (26). 3: for all i ∈ N do…”

“…However, the cloud servers usually locate far from the mobile devices. Data transmission from the devices to cloud servers would incur a large amount of energy consumption and transmission delay [3]. To mitigate these drawbacks, mobile edge computing (MEC) emerges as a promising paradigm providing the cloud resources at the radio access network (i.e., base station) [4], [5].…”

Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing

“…Lyu et al [19] designed a selective offloading decision scheme in MEC to minimize the energy consumption of Internet of Things devices. Wang et al [20] optimized the resource allocation in MEC by means of a unifying framework for the power-performance tradeoff of a mobile service provider. You et al [21] investigated the resource allocation for a multiuser MEC system based on time-division multiple access and orthogonal frequency-division multiple access.…”

RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC

“…Offloading decision making and resource allocation have been studied in [1], [2], while MEC with Cloud Radio Access Network (C-RAN) has been investigated in [3], [4], [5]. The above works either consider there is only one MEC (e.g., [1], [7]), or consider the MECs have fixed location (e.g., [8], [3]), which may not be practical in some scenarios. For instance, the single MEC is normally resourcelimited and may not be able to meet the requirement of all the UEs at the same time.…”