Here, There, Anywhere: Profiling-Driven Services to Tame the Heterogeneity of Edge Applications

Pandey, Manish Kumar; Cruz, Breno Dantas; Le, Minh; Kwon, Youngwoo; Tilevich, Eli

doi:10.1109/smds53860.2021.00019

Cited by 3 publications

(1 citation statement)

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“…Consequently, some studies profile the requests to achieve better resource utilization. They profile each request's network, energy, CPU/GPU, RAM, and QoS requirements to select the edge nodes that better fulfill these profiles [49,101]. Also, these resources have to be monitored for status and health observation.…”

Section: Conclusion Validitymentioning

confidence: 99%

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Castro¹,

Rigo²

2023

Preprint

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Context: Microservices running and being powered by Edge Computing have been gaining much attention in the industry and academia. Since 2014, when Martin Fowler popularized the Microservice term, many studies have been published relating these subjects to explore how the Edge's low-latency feature could be combined with the high throughput of the distributed paradigm from Microservices. Objective: Identifying how Microservices work together with Edge Computing whereas they take advantage when running on Edge. Method: In order to better understand this relationship, we first identified its key concepts, which are: architecture approaches and features, microservice composition (orchestration/choreography), and offloading. Afterward, we conducted a Systematic Literature Review (SLR) as the survey method. Results: We reviewed 111 selected studies and built a taxonomy of Microservices on Edge Computing demonstrating their current architecture approaches and features, composition, and offloading modes. Moreover, we identify the research gaps and trends. Conclusion: This paper is a step forward to help researchers and professionals get a general overview of how Microservices and Edge have been related in the last years. It also discusses gaps and research trends. This SLR will also be a good introduction for new researchers in Edge and Microservices.

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Section: Conclusion Validitymentioning

confidence: 99%