Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks (1901.08946v1)

Abstract: The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

• The paper presents a novel optimization formulation that addresses joint service placement and request routing under multidimensional constraints in overlapping multi-cell networks.
• It analyzes the problem's complexity by generalizing the knapsack problem and reveals the limitations of traditional greedy approaches due to non-submodular characteristics.
• Employing a randomized rounding technique, the proposed algorithm achieves near-optimal performance in simulations, significantly reducing cloud load and enhancing local service delivery.

Overview of Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

The paper "Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks" advances the field of Mobile Edge Computing (MEC) by tackling the problem of optimizing service placement and request routing in multi-cell networks with multidimensional constraints. As the demand for low-latency services—such as augmented reality and networked gaming—grows, MEC offers a viable solution by facilitating computation closer to end-users.

Summary of Contributions

1. Problem Formulation: The authors present a nuanced optimization problem addressing both the placement of services and the routing of requests in MEC networks. This involves a comprehensive analysis incorporating storage, computation, and communication constraints across multiple overlapping coverage areas of base stations (BSs).
2. Complexity Analysis: They explore the JSPRR (Joint Service Placement and Request Routing) problem's computational complexity, revealing it as a generalization of knapsack and other well-known placement problems. The paper confirms that JSPRR is indeed challenging as it includes non-submodular characteristics, which makes traditional optimization approaches inadequate.
3. Proposed Algorithm: The authors introduce a novel approximation algorithm based on randomized rounding techniques. By employing this method, the paper guarantees performance that is provably close to optimal with bounded resource constraint violations—a marked advancement over existing greedy algorithms traditionally used in simpler scenarios without multidimensional constraints.
4. Evaluation: Through simulations, the proposed algorithm demonstrates superior performance compared to conventional greedy approaches, achieving significant reductions in the cloud load. This implies that more user requests can be satisfied locally at the edge (BSs), illustrating both theoretical and practical efficacy.

Insights and Future Directions

The research offers significant insights into the intricate dynamics of service placement and routing within MEC infrastructures. The integration of multidimensional constraints and the inclusion of overlapping coverage areas represent a comprehensive evolution from simpler models such as single-cell or purely storage-focused systems.

Practical Implications: With its capacity to significantly offload demands from centralized clouds to localized edge servers, this work can contribute to the architectural design of more efficient and scalable MEC systems. This is increasingly critical in the advent of 5G networks, where latency demands are ever more stringent.

Theoretical Implications: The identification of the JSPRR problem as non-submodular yet approximately submodular expands the boundaries of current optimization paradigms in network systems. It invites further exploration into approximation techniques beyond traditional approaches.

Future Developments: While this paper makes commendable advancements, future research could explore the synergy between MEC and backhaul coordination to further optimize resource utilization. Additionally, modeling dynamic or adaptive service chains proposes an avenue for enhancing the algorithms' applicability in real-world deployments with more complex service logic.

In summary, the authors’ contributions push forward the understanding and capabilities of optimal service placements and routing in multi-dimensional and multi-cell MEC settings, offering tangible benefits in reducing cloud dependency and enhancing service latency benchmarks.