Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (1809.05239v1)

Abstract: Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we first design an approximation algorithm based on Markov approximation to seek a near-optimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes.

• The paper presents a dynamic service placement framework leveraging Lyapunov optimization to balance latency reduction with migration cost constraints.
• It employs both a centralized Markov approximation and a distributed best response update to achieve near-optimal service placement in MEC environments.
• Empirical evaluations demonstrate significant latency reductions compared to traditional algorithms while effectively managing cost trade-offs.

Mobility-Aware Dynamic Service Placement in Mobile Edge Computing

The paper "Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing" by Tao Ouyang, Zhi Zhou, and Xu Chen systematically addresses the quintessential challenge of dynamic service placement in Mobile Edge Computing (MEC) as influenced by user mobility. MEC signifies a paradigm shift, extending cloud computing capabilities to the network's edge to mitigate latency issues inherent in centralized cloud models. While beneficial for resource-demanding applications such as augmented reality and interactive gaming, this paper identifies a critical operational challenge: dynamic service migration due to user mobility.

Problem Statement and Framework

The primary objective outlined in this paper is optimizing user-perceived latency in MEC environments under a long-term average cost constraint. User mobility necessitates frequent service migrations across edge nodes to maintain optimal service performance. However, such migrations incur substantial operational costs, demanding an efficient trade-off strategy between performance enhancement and cost restriction.

The authors introduce a dynamic service placement framework leveraging Lyapunov optimization to transform long-term optimization tasks into real-time decision-making problems. This approach effectively incorporates budget constraints into immediate control actions without foreknowledge of mobility patterns.

Methodology

To address the NP-hard nature of the decomposed real-time optimization, the paper employs two approximation strategies:

1. Markov Approximation: This centralized mechanism utilizes a Markov decision process to derive near-optimal service placement strategies. The method evaluates multiple service placement policies probabilistically to identify minimal latency solutions.
2. Best Response Update: As a distributed alternative, this approach employs a game-theoretic perspective, where individual users iteratively update their placement policies. This method achieves faster convergence, making it scalable for large-scale and ultra-dense MEC networks.

Both schemes integrate robust theoretical underpinnings, emphasizing cost-efficient service continuity while minimizing latency through adaptive resource allocation.

Theoretical Analysis

The authors employ rigorous theoretical analysis to demonstrate the efficacy of their solutions, particularly focusing on the trade-off between latency and migration cost. They introduce a performance-cost trade-off of $[O(1/V),O(V)]$, wherein a larger control parameter $V$ prioritizes latency reduction, providing a formal guarantee on approaching optimal performance levels. The findings suggest the proposed algorithms achieve equilibrium in latency performance and cost management within feasible computational overheads.

Empirical Evaluation

An expansive set of simulations corroborate the theoretical findings, showcasing significant latency reductions over traditional AM and GK algorithms and substantiating the long-term maintenance of cost constraints. The paper highlights the algorithms' adaptability across various network densities, underscoring their practical viability for real-world MEC deployments.

Implications and Future Directions

The implications of this work extend beyond immediate service placement optimization for MEC. The methods applied here may encourage further exploration into integrated resource optimization across federated edge networks and the development of more advanced, collaborative frameworks involving device-to-device (D2D) resource sharing.

Looking ahead, potential research avenues include the incorporation of federated cloud capabilities augmented by D2D engagements and refining prediction models for mobility patterns to enhance service continuity further. Such enhancements may broaden the horizon for next-generation MEC, providing robust, low-latency services in an increasingly mobile-dependent digital ecosystem.