Collaborative Cloud and Edge Computing for Latency Minimization

By Jinke Ren, Guanding Yu, Yinghui He, Geoffrey Y. Li. IEEE Transactions on Vehicular Technology, 2019

537 citations recorded by the source corpus.

By performing data processing at the network edge, mobile edge computing can effectively overcome the deficiencies of network congestion and long latency in cloud computing systems. To improve edge cloud efficiency with limited communication and computation capacities, we investigate the collaboration between cloud computing and edge computing, where the tasks of mobile devices can be partially processed at the edge node and at the cloud server. First, a joint communication and computation resource allocation problem is formulated to minimize the weighted-sum latency of all mobile devices. Then, the closed-form optimal task splitting strategy is derived as a function of the normalized backhaul communication capacity and the normalized cloud computation capacity. Some interesting and useful insights for the optimal task splitting strategy are also highlighted by analyzing four special scenarios. Based on this, we further transform the original joint communication and computation resource allocation problem into an equivalent convex optimization problem and obtain the closed-form computation resource allocation strategy by leveraging the convex optimization theory. Moreover, a necessary condition is also developed to judge whether a task should be processed at the corresponding edge node only, without offloading to the cloud server. Finally, simulation results confirm our theoretical analysis and demonstrate that the proposed collaborative cloud and edge computing scheme can evidently achieve a better delay performance than the conventional schemes.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/af2ad9cad35e99a9076b37176b92398a487e057d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Zhenjian Yu
2. Yanmin Gong
3. Shimin Gong
4. Madhusanka Liyanage
5. Yuanxiong Guo
6. Shen Wang
7. Patrick McEnroe

In Electrical and Electronic Engineering

1. Zhenjian Yu
2. Yanmin Gong
3. Shimin Gong
4. Madhusanka Liyanage
5. Yuanxiong Guo

Citations in the corpus

• Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “There is a rich literature on resource management in MEC that aims at optimizing system latency [11]–[14], energy consumption [15]–[17], and overall cost of system latency and/or energy consumption [18]–[21].”
• Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, unknown): “Traditionally, resource utilization is inefficient, and trading mechanisms are unfair in cloud-edge computing power trading [205] for AIGC services.”
• Computer Science Review (unknown): “The proliferation of demanding smart applications, such as AR/VR, autonomous driving, and industrial IoT [1–3], strains the capabilities of resource-constrained mobile devices [4,5] and challenges traditional cloud computing models due to high latency and network conges­ tion [6,7].”
• A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, background): “…on embedded deep learning as this has not already been discussed. a) Lower latency: As discussed in Section III-A3, UAVs employing traditional cloud AI can expect a significantly long transmission latency associated with sending and receiving data between the UAV and a central cloud server [43].”