A Qualitative Evaluation of Service Mesh-based Traffic Management for Mobile Edge Cloud

Duque, Aleksandra Obeso; Klein, Cristian; Feng, Jun; Cai, Xiao; Skubic, Björn; Elmroth, Erik

doi:10.1109/ccgrid54584.2022.00030

Cited by 5 publications

(2 citation statements)

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“…The service mesh is a management framework that controls the routing of requests towards different instances of a microservice to comply with a routing policy [7], [13], [14], [15]. The topology of the service mesh is a dependency graph G i of microservice instances (Fig.…”

Section: ) Microservice Applicationmentioning

confidence: 99%

“…Specifically, the random algorithm repeatedly selects a microservice at random from those not yet running in the edge and for which the edge has enough resources. The loop ends when no other microservice can be selected due to the exhaustion of edge resources 13. We also considered the PAMP algorithm for placement, but since in this case the results are the same as the optimal ones, we do not show them in the plots.VOLUME 11, 2023 …”

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Microservices From Cloud to Edge: An Analytical Discussion on Risks, Opportunities and Enablers

Detti

2023

IEEE Access

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Microservice applications are made of many interacting microservices that can leverage different cloud servers to distribute the computational load horizontally, so these applications are considered cloud-native. The emergence of edge computing has prompted the research of placement strategies that consider the possibility of instantiating microservices in cloud and edge data centers to improve specific performance metrics, such as the time the application spent serving a user request, also referred to as user delay or makespan. Microservice applications differ in many architectural properties, such as the number of microservices, the number of them involved per request, their dependency graph, and the presence of centralized databases. We found that these properties influence the possibility of a placement strategy to exploit edge resources to reduce user delay; therefore, they constitute an application-level optimization space that, however, has not yet been explored in the literature. Accordingly, this paper contributes to filling this knowledge gap by answering the following question: which are the architectural properties of a microservice application that enable it to be more edge-native, that is, to better reduce user delay by using edge computing? Our results are based on a new analytical modeling of the average user delay provided by a microservice application deployed on cloud/edge resources and a new heuristic placement strategy that take into account key aspects of the application, and the supporting cloud and network environment, not considered by previous literature works.INDEX TERMS Edge computing, microservices, placement problems, delay performance, analytical modeling.Algorithm 1 P i c for Locality Load Balancing Routing Policy 1: Initialize P i c with 0 values 2: for i = 1 to M do 3:▷ not for the user 5:j) 6: end if 7: if S (⟨i, edge⟩) = 1 then 8: if S (⟨j, edge⟩) = 1 then 9: P i c (⟨i, edge⟩, ⟨j, edge⟩) = P m c (i, j) 10: else 11: P i c (⟨i, edge⟩, ⟨j, cloud⟩) = P m c (i, j) 12: end if 13: end if 14:end for 15: end for We conclude the section by presenting, in Fig. 4, an example of the interactions that occur among microservices to Open Access funding provided by 'Università degli Studi di Roma "Tor Vergata"' within the CRUI CARE Agreement

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Section: ) Microservice Applicationmentioning

confidence: 99%

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confidence: 99%