Layer-Aware Containerized Service Orchestration in Edge Networks

Dolati, Mahdi; Rastegar, Seyed Hamed; Khonsari, Ahmad; Ghaderi, Majid

doi:10.1109/tnsm.2022.3217134

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Dolati et al 25 models the traffic steering of the network function and the pulling and sharing of the container layers as an integer linear programming problem during the container scheduling of the virtualized network function, and provides a polynomial time optimization algorithm for this NP‐hard problem. Lou et al 26 jointly consider container placement and layer pulling sequences, and then determine the service placement through layer‐awareness and layer‐sharing, further optimize the image layer pulling sequences, thus reducing the startup latency.…”

Section: Related Workmentioning

confidence: 99%

An optimal container update method for edge‐cloud collaboration

et al. 2023

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Emerging computing paradigms provide field‐level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform‐independent, low‐resource‐consumption operating environment for edge service. The image‐pulling time of Docker is a crucial factor affecting the start‐up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node‐local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote‐cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency.

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Section: Related Workmentioning

confidence: 99%