Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading (1704.00163v1)

Abstract: By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of the fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.

• The paper proposes mathematical frameworks for optimizing resource allocation across three computation models (local, edge, partial offloading) to minimize task latency in multi-user mobile-edge computing systems.
• Numerical simulations demonstrate that the partial compression offloading model significantly reduces end-to-end latency, especially for devices with limited local resources or in systems with many users.
• The proposed optimization methods offer practical solutions for enhancing quality of service in 5G networks and are applicable to latency-sensitive scenarios like video surveillance and security applications.

Latency Optimization in Mobile-Edge Computation Offloading

The paper authored by Jinke Ren et al., titled "Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading," addresses a critical issue in the enhancement of mobile-edge computing (MEC) systems: minimizing computational latency through optimal resource allocation. The paper is situated in the context of 5G networks, where the need for swift data processing has become paramount due to the proliferation of Internet of Things (IoT) devices and services with stringent latency requirements.

Core Contributions

The research is focused on a multi-user time-division multiple access (TDMA) system where intensive computational tasks are offloaded from mobile devices to edge cloud servers positioned at cellular base stations. The authors explore three distinct computational models: local compression, edge cloud compression, and partial compression offloading. They provide a comprehensive analytical framework for resource allocation that minimizes system delay for each model.

1. Local Compression Model: The authors derive closed-form solutions for optimal resource allocation by modeling the minimization of weighted-sum delay of tasks given the constraints of local device resources.
2. Edge Cloud Compression Model: This model presents a more centralized approach where data is sent to the edge cloud server for compression. Again, closed-form expressions are derived that optimize the allocation of both communication time slots and computational capacity.
3. Partial Compression Offloading Model: This model is perhaps the most novel aspect of the paper. Tasks are split between local devices and the edge cloud based on a derived optimal data segmentation strategy with a piecewise structure. This involves a complex piecewise optimization problem resulting in a solution that significantly reduces end-to-end latency in various scenarios.

Key Results and Verification

Numerical simulations confirm the effectiveness of the proposed algorithms. Notably, it is shown that the partial compression offloading model offers substantial improvements in reducing latency compared to the other two models, particularly as the number of devices increases or when the local computational resources of devices are constrained.

Implications and Future Research

The proposed optimization frameworks cater to practical scenarios such as video surveillance and security applications where large data volumes need timely compression and analysis. The implications of this research extend to enhancing the quality of service in 5G networks by reducing latency and improving energy efficiency through optimal resource usage.

From a theoretical perspective, this paper lays the groundwork for exploring more complex MEC system models including those with non-orthogonal multiple access schemes or dynamic user task arrivals. Future research could develop on this basis, investigating the integration of energy-efficient algorithms and expanding the scope to incorporate edge learning paradigms or real-time adaptive resource allocation under uncertainty.

Overall, this paper makes significant contributions to the field of mobile-edge computing by offering concrete, mathematical solutions to improve latency performance, paving the way for creating more responsive and capable 5G network environments.