A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (1703.10750v1)

Abstract: As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

• The paper presents a comprehensive survey on mobile edge networks, detailing architectures such as MEC, Fog Computing, and Cloudlets.
• It evaluates energy-efficient computational offloading and dynamic caching strategies that significantly reduce latency and backhaul congestion.
• It examines integration with 5G technologies like mmWave and D2D communications while outlining challenges in network heterogeneity and security.

Overview of "A Survey on Mobile Edge Networks: Convergence of Computing, Caching, and Communications"

Introduction

The evolution of mobile cellular networks over several decades has transitioned from 1G voice-only systems to 4G all-IP based LTE-Advanced networks with significant improvements in system capacity and data rates. However, with the surge in traffic volume, centralized network architectures face challenges in meeting user demands, particularly in terms of backhaul congestion and latency. This has led to the advent of mobile edge computing and caching. The paper "A Survey on Mobile Edge Networks: Convergence of Computing, Caching, and Communications" by Shuo Wang et al. provides an exhaustive review of the state-of-the-art research in mobile edge networks, discussing their architecture, advantages, key technologies, and open challenges.

Architectural Overview and Characteristics

Mobile edge networks (MENs) involve deploying computing and caching resources at the edge of mobile networks. The primary architectures discussed are Mobile Edge Computing (MEC), Fog Computing, Cloudlets, and Edge Caching. MEC, introduced by ETSI, employs virtualized platforms at the network edge, providing low latency and localized processing. Fog Computing, proposed by Cisco, extends cloud capabilities to near-user edge devices, particularly suited for IoT applications. Cloudlets, developed by Carnegie Mellon University, offer a three-tier architecture (device-cloudlet-cloud) for low-latency services. Lastly, edge caching aims to alleviate backhaul congestion by storing popular content closer to users.

Key Technologies in Computing and Caching

Computing

Computational offloading is a core aspect of MEC, allowing resource-constrained devices to offload tasks to more powerful edge servers. Research has explored various optimization schemes for energy-efficient offloading, delay minimization, and multi-user scenarios. Notably, works like Zhang et al.'s energy-efficient computation offloading in 5G HetNets illustrate the significant benefits in terms of reduced energy consumption and improved computational efficiency.

Caching

Caching at the edge leverages content popularity to improve cache hit probability and reduce redundant data transmission. While traditional caching algorithms like LFU and LRU are adapted for mobile networks, newer strategies such as learning-based and cooperative caching have shown promise. For instance, user preference-based caching (UPP) and learning-based policies provide more dynamic and context-aware caching solutions, ultimately enhancing user experience by reducing latency and bandwidth usage.

Advances in Communication Techniques

The integration of 5G technologies with MENs enhances the efficiency and capabilities of mobile networks. The use of mmWave communication addresses the high data rate requirements of future networks, while D2D communication facilitates direct interactions among devices, optimizing resource usage. Additionally, interference management techniques like interference alignment (IA) and multicast-aware transmission schemes are critical for maintaining performance in dense network environments.

Applications and Use Cases

MENs support a wide range of applications, including dynamic content delivery, AR/VR, intensive computation assistance, video streaming and analysis, IoT, connected vehicles, cognitive assistance, and wireless big data analysis. These applications benefit from the low latency, high bandwidth, and localized processing capabilities provided by edge computing and caching. For instance, AR applications leverage MEC's context-awareness for real-time user interaction enhancements, while smart city applications utilize fog computing for efficient data management and event response.

Key Enablers and Open Challenges

Enabling technologies such as cloud computing, SDN, NFV, and smarter mobile devices are instrumental in realizing MENs. These technologies provide the necessary scalability, flexibility, and resource management capabilities for effective network operations. However, several challenges remain, including handling network heterogeneity, ensuring real-time analytics, managing user mobility, implementing robust security and privacy measures, and developing dynamic pricing policies. Addressing these challenges will require continued research and innovation in network architecture and resource management strategies.

Conclusion

The convergence of computing, caching, and communications in mobile edge networks presents a transformative approach to addressing the limitations of traditional centralized architectures. By deploying resources at the network edge, MENs offer significant improvements in latency, bandwidth efficiency, and user experience. The ongoing research and development in this field highlight the potential for MENs to support a wide array of next-generation applications, driving the future of mobile networking.