Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems (1906.00711v3)

Abstract: In mobile edge computing (MEC) systems, edge service caching refers to pre-storing the necessary programs for executing computation tasks at MEC servers. At resource-constrained edge servers, service caching placement is in general a complicated problem that highly correlates to the offloading decisions of computation tasks. In this paper, we consider a single edge server that assists a mobile user (MU) in executing a sequence of computation tasks. In particular, the MU can run its customized programs at the edge server, while the server can selectively cache the previously generated programs for future service reuse. To minimize the computation delay and energy consumption of the MU, we formulate a mixed integer non-linear programming (MINLP) that jointly optimizes the service caching placement, computation offloading, and system resource allocation. We first derive the closed-form expressions of the optimal resource allocation, and subsequently transform the MINLP into an equivalent pure 0-1 integer linear programming (ILP). To further reduce the complexity in solving the ILP, we exploit the underlying structures in optimal solutions, and devise a reduced-complexity alternating minimization technique to update the caching placement and offloading decision alternately. Simulations show that the proposed techniques achieve substantial resource savings compared to other representative benchmark methods.

• The paper introduces a framework that jointly optimizes service caching placement and computation offloading to reduce delay and energy consumption in MEC systems.
• It employs closed-form resource allocation, caching causality, and an alternating minimization approach to transform a complex MINLP into a tractable ILP.
• Simulation results demonstrate that the proposed method significantly outperforms conventional strategies under varying caching capacities and program generation delays.

Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

The paper, "Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems," addresses the complexities of improving mobile edge computing (MEC) performance by optimizing service caching placement alongside computation offloading strategies. The paper presents a robust framework that highlights the intertwined decisions of caching and offloading in resource-constrained environments, particularly focusing on how edge servers can pre-store necessary execution programs to reduce real-time delays and minimize energy consumption for mobile users (MUs).

Problem Context and Formulation

In the discussed MEC systems, the challenge arises from the limited caching space at edge servers, which necessitates strategic decisions on which programs to cache over time. The paper emphasizes the joint optimization of service caching placement, computation offloading decisions, and system resource allocation by considering these as a Mixed Integer Non-Linear Programming (MINLP) problem. The goal is to minimize computation delay and energy expenditure for MUs, specifically by:

• Determining whether computation tasks should be offloaded.
• Strategically placing service caches to reduce initialization delay.
• Allocating system resources such as CPU processing frequency and transmission power efficiently.

Methodological Approach

The researchers approached the problem with a transformation strategy. They first derived the closed-form solutions for optimal resource allocation, which led to an equivalent pure 0-1 Integer Linear Programming (ILP) problem. This transformation simplifies the complexity inherent in solving MINLP by focusing on binary variables, thereby making the problem more tractable with existing integer optimization methods.

Key steps include:

1. Separate Optimization: Resource allocation was optimized independently, with the authors deriving closed-form expressions to identify optimal solutions regarding the offloading and caching factors.
2. Complexity Reduction: Utilizing problem structure, such as caching causality and task dependencies, the paper reduced complexity further through a multidimensional knapsack problem approach—focusing on caching placements for tasks that are actively engaged in computation.
3. Alternating Minimization: An iterative strategy was devised, allowing for alternating updates between caching decisions and offloading strategies, thus iteratively refining solutions and achieving near-optimal results.

Numerical Results and Implications

The simulations validated that the proposed methods substantially decrease the computational delay and resources needed compared to existing benchmarks. By assessing variable scenarios in program generation times, caching capacities, and path loss factors, the paper demonstrates that the joint optimization often yields a significant performance advantage. Moreover, the paper finds that in cases where program generation delays are substantial, popular caching strategies that do not consider offloading interactions underperform.

Theoretical and Practical Implications

The implications of this paper are far-reaching for MEC design:

1. Theoretical Insights: This research contributes to understanding complex dependencies in caching and computation decisions, offering a comprehensive methodology for reduction and transformation techniques applicable to other complex MINLP challenges.
2. Practical Deployment: By enabling more efficient task processing and resource allocation in MEC environments, this work can be immediately impactful in real-world deployments, where minimizing latency is crucial for emerging applications like mobile gaming and augmented reality.
3. Framework Extensibility: The framework presents an adaptable base that can be extended to multi-user and multi-server MEC scenarios, potentially leading to collaborative caching and offloading strategies that cater to more diverse and heterogeneous computing environments.

Future Directions

Moving forward, research can explore extending this caching and offloading synergy in more diversified network structures, advance real-time adaptive algorithms that handle dynamic task arrivals more effectively, and incorporate privacy and security constraints into the optimization models. Furthermore, exploration into leveraging machine learning techniques for predictive caching within the proposed optimization framework may be a promising avenue to enrich the MEC ecosystem. This paper sets the stage for a future where intelligent edge servers can dynamically accommodate the computational demands of next-generation wireless applications.