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With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity. Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG). An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.
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EdgeRDV: A Framework for Edge Workload Management at Scale
Edge computing is a distributed computing paradigm that moves data-intensive applications and services (e.g., AI) closer to the data source. The rapid growth of edge endpoints connected to the Internet today poses several challenges in scalable application life cycle management. That is, managing data and workloads on several thousand, up to millions of edge endpoints, challenged by limited connectivity, resource constraints, network and edge endpoint failures. In this work, we present EdgeRDV, a new edge abstraction that builds on the idea of rendezvous nodes to manage edge workloads at scale. The EdgeRDV architecture is comprised of a central cloud management endpoint (or cloud hub), a central gateway for each edge site (or edge hub), redundant gateways (or rendezvous nodes), and edge endpoints. Beyond its scalable architecture, EdgeRDV presents new techniques and algorithms that address single points of failures and provide adjustable levels of resilience and cost-effectiveness in edge network deployments. We conducted preliminary experiments to evaluate EdgeRDV, through simulations, and our results show that EdgeRDV requires one to three orders of magnitude fewer intermediate nodes compared to relay structures, can gracefully adapt to failures, and requires a constant number of messages during failure recovery in edge sites with up to 667K+ edge endpoints.
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- PAR ID:
- 10419417
- Date Published:
- Journal Name:
- IEEE International Conference on Edge Computing and Communications
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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