Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing (1803.11512v1)

Abstract: The concept of multi-access edge computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to a resourceful cloud, an MEC server has limited resources. When each MEC server operates independently, it cannot handle all of the computational and big data demands stemming from the users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction due to data exchange between users devices and remote cloud. Therefore, joint computing, caching, communication, and control (4C) at the edge with MEC server collaboration is strongly needed for big data applications. In order to address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to maximize the bandwidth saving while minimizing delay, subject to the local computation capability of user devices, computation deadline, and MEC resource constraints. However, the formulated problem is shown to be non-convex. To make this problem convex, a proximal upper bound problem of the original formulated problem that guarantees descent to the original problem is proposed. To solve the proximal upper bound problem, a block successive upper bound minimization (BSUM) method is applied. Simulation results show that the proposed approach increases bandwidth-saving and minimizes delay while satisfying the computation deadlines.

• The paper presents a proximal BSUM method that converts a non-convex 4C optimization problem into a convex one, significantly boosting bandwidth efficiency and reducing delays.
• It employs distributed computation and enhanced cache utilization to decompose complex tasks and effectively manage resource constraints of MEC servers.
• The study underlines a hybrid control framework’s practical benefits, enhancing performance for latency-sensitive applications like augmented reality and live streaming.

Overview of Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing

This paper presents a comprehensive exploration into optimizing multiple facets—communication, computation, caching, and control (denoted as 4C)—within the context of Big Data Multi-access Edge Computing (MEC). The authors tackle the intrinsic challenges associated with the limited resources of MEC servers positioned at the network edge, focusing on bandwidth-saving and delay-reduction solutions.

Problem Statement and Methodology

With the proliferation of connected devices generating increasingly voluminous data, handling these computational and storage demands at the edge rather than at centralized cloud servers has become imperative. The research scrutinizes the limitations of MEC servers operating autonomously, highlighting an optimized collaborative approach to enhance data computation and caching at these localized points. The specific objective is to maximize bandwidth-saving and minimize latency under given constraints such as local computation capabilities, defined computation deadlines, and resource limitations inherent to MEC servers.

This optimization problem is formulated as a non-convex problem. To address this, a proximal upper-bound problem is devised to approximate the original problem effectively, facilitating its transformation into a convex problem. The Block Successive Upper Bound Minimization (BSUM) method is employed to iteratively solve this proximal problem, allowing for a structured and parallelizable approach to optimization.

Numerical Results and Algorithms

Through meticulous simulation, the authors illustrate that the proposed approach significantly enhances bandwidth-saving and reduces computational delays while ensuring compliance with computation deadlines. Notably, BSUM's iteration framework supports decomposition into smaller subproblems, thereby enabling distributed computation pivotal for MEC environments where servers must operate with localized autonomy yet collaborative efficiency.

The simulation outcomes underscore the effectivity of cache utilization and collaborative task offloading, revealing substantial improvements in resource allocation over traditional edge computing approaches. The attention to a hybrid control approach blending hierarchical and distributed control offers practical insights into the real-world deployment of MEC solutions.

Theoretical and Practical Implications

Theoretically, this paper contributes to MEC literature by aligning edge-computing strategies with big data exigencies, emphasizing scalability within dynamic network environments. Practically, the implications extend to enhanced service delivery in latency-sensitive applications, such as augmented reality or live video streaming, by reducing dependency on centralized cloud data centers.

Future Directions

The research provides a foundation for further exploration into adaptive control frameworks and real-time resource management in MEC. Future developments could integrate machine learning algorithms for predictive resource allocation, adapting to the ever-changing demand paradigms and pushing boundaries on MEC server capabilities. Integrating such intelligent systems would further optimize the MEC framework, propelling forward the efficacy of edge computing in 5G and future-generation network architectures.

In conclusion, this paper presents a detailed and analytically rigorous approach to optimizing MEC systems in the era of big data, providing both a theoretical framework and practical insights for improved edge-computing solutions.