ENORM: A Framework For Edge NOde Resource Management (1709.04061v1)

Abstract: Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95\%. These results highlight the potential of fog computing for improving the quality of service and experience.

• The paper introduces the ENORM framework which addresses key challenges in edge resource management through novel provisioning, workload deployment, and auto-scaling mechanisms.
• Experimental results using an online game use-case demonstrate ENORM significantly reduces application latency by 20-80% and edge-to-cloud traffic by up to 95%.
• ENORM enables scalable and efficient resource management for latency-sensitive applications at the edge, supporting future decentralized computing landscapes like IoT and fog computing.

ENORM: A Framework for Edge Node Resource Management

The paper presents the ENORM framework, an innovative approach to managing resources on edge nodes in fog computing environments. As the proliferation of connected devices increases, traditional cloud-based architectures face significant challenges in handling the resultant data volumes and communication demands. Thus, leveraging edge nodes close to user devices, ENORM provides a comprehensive framework for provisioning and auto-scaling resources on these nodes, aiming to optimize quality of service (QoS) by minimizing latency and reducing cloud traffic.

Key Contributions

The authors address three principal challenges inherent in managing resources at the edge. Firstly, the need for efficient provisioning of edge nodes necessitates a mechanism capable of integrating edge nodes into existing cloud-server architectures. ENORM tackles this issue by implementing a provisioning mechanism composed of handshaking, deployment, and termination protocols tailored to resource-constrained environments.

Secondly, the framework introduces a deployment strategy that partitions workloads for deployment on edge nodes, favoring containerized environments over virtual machines due to their lightweight nature and rapid deployment capabilities. This separation of concerns allows local data relevant to users in proximity to an edge node to be processed locally, with updates sent periodically to a central cloud server, thereby reducing the frequency and volume of data transfer.

Thirdly, ENORM features an auto-scaling mechanism designed to dynamically adjust the resources allocated to edge applications. This ensures that the availability of resources on an edge node is optimized based on real-time demand and application priority, thus aligning resource allocation with QoS objectives.

Numerical Results and Implications

Through experimental validation using an online game use-case analogous to Pokémon Go, the paper demonstrates ENORM's efficacy in real-world scenarios. The framework significantly reduces application latency by 20%-80%, and diminishes data transfer and communication frequency between the edge node and the cloud by up to 95%. These results underscore the potential of fog computing in enhancing user experience by maintaining interactive latency-sensitive applications locally, thus offering a viable complement to centralized cloud computing paradigms.

The implications of ENORM extend beyond immediate performance enhancements, offering a scalable and efficient approach to resource management in edge computing environments. The capability to dynamically scale resources on-demand suggests future applicability across a wide range of IoT applications where data volumes and processing demands are expected to grow exponentially. Moreover, the paper positions ENORM as a precursor to more advanced distributed systems architectures that integrate seamlessly with heterogeneous computing resources, pointing toward a more decentralized computing landscape.

Speculation on Future Developments

Future developments in AI and computing could further enhance frameworks like ENORM by incorporating predictive analytics to anticipate and model resource demand patterns. This would enable preemptive resource allocation strategies, optimizing both energy efficiency and computational throughput at the edge. Additionally, advancements in federated learning may complement ENORM's architecture, allowing distributed models to be trained across edge nodes without the need for raw data centralization—boosting privacy while enhancing model accuracy.

The ENORM framework presents a tangible step forward in realizing the full potential of fog computing. By addressing the challenges of resource management at the edge, it lays the groundwork for more responsive and efficient computing systems that can adapt to the ever-increasing demands of a connected world. As edge computing continues to evolve, frameworks like ENORM will likely play a critical role in bridging the gap between centralized cloud services and end-user devices, ensuring that computing capabilities keep pace with technological advancements and societal needs.