EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks (1709.02582v1)

Abstract: Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.

• The paper introduces an online energy-aware mobility management algorithm that minimizes delay while adhering to user energy constraints.
• It integrates Lyapunov optimization with Multi-Armed Bandit theory to handle dynamic base station availability in ultra dense networks.
• Extensive simulations validate near-optimal performance and offer actionable guidelines for practical MEC deployment.

Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

The integration of Ultra Dense Networks (UDNs) and Mobile Edge Computing (MEC) presents a promising future for mobile networks, offering increased computing resources and reduced latency through the dense deployment of base stations (BSs). However, this vision brings forth significant challenges, particularly in the field of mobility management (MM). Traditional MM approaches, designed primarily for radio access, fall short in the MEC-enabled UDN context due to the need to manage dynamic radio access and computation provisioning.

This paper addresses these challenges by proposing an innovative user-centric Energy-Aware Mobility Management (EMM) scheme. The primary goal is to optimize delay performance while adhering to the user's long-term energy consumption constraints. Notably, the proposed EMM algorithm operates online, which is crucial since it does not rely on future system state information and can accommodate imperfect current system states and random BS availability.

The paper identifies major challenges in MEC-enabled UDN MM, including the lack of accurate information on candidate BSs, the absence of future information for effective decision-making, and the volatile nature of UDNs where BSs may dynamically switch on and off. To tackle these issues, the authors utilize a combination of Lyapunov optimization and Multi-Armed Bandit (MAB) theory, crafting an algorithm that effectively balances the trade-off between delay minimization and energy consumption.

Key contributions of the EMM algorithm include:

1. An innovative framework that incorporates Lyapunov optimization and MAB to efficiently manage mobility under stringent energy constraints.
2. Theoretical performance bounds demonstrating that the EMM algorithm can achieve near-optimal delay performance, bounded by a quantifiable deviation from the energy budget.
3. Extensive simulation results validating the algorithm's efficacy and providing insights into parameter impacts, offering practical guidelines for MEC deployment in real-world UDN scenarios.

The paper's implications are profound for both practical deployments and theoretical advancements. Practically, this work offers a robust framework that mobile network operators can employ to ensure efficient service delivery despite the dynamic nature of UDNs and the limited energy resources of mobile devices. Theoretically, the integration of Lyapunov optimization and MAB theory into MM presents a novel approach to handling uncertainty and dynamism in next-generation networks.

Future work could build on this research by exploring MM schemes for high-mobility scenarios where user movement during task processing is significant, and examining cooperative computing among multiple BSs to further enhance performance.

In summary, this paper provides a comprehensive, well-grounded approach to energy-aware MM in MEC-enabled UDNs, bridging a critical gap in current research and paving the way for advanced mobile network solutions.