A Microservice Store for Efficient Edge Offloading

Gedeon, Julien; Wagner, Martin; Heuschkel, Jens; Wang, Lin; Mühlhäuser, Max

doi:10.1109/globecom38437.2019.9014114

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…Discovery mechanisms that rely on central repositories (e.g., as in [50]) clearly do not scale. The same is true for the control and management of the infrastructure (e.g., as in [110]). Unified and scalable discovery and management become even more challenging if we consider edge sites owned by different stakeholders.…”

Section: Multi-access Edge Computingmentioning

confidence: 97%

“…Golkarifard et al [109] present a framework that supports both cloud and edge offloading for wearable computing applications. Orthogonal to those approaches, Gedeon et al [110] have suggested the concept of a repository where the services to be offloaded are stored and, thus, do not need to be transferred from the client to the surrogate.…”

Section: ) Offloading Mechanismsmentioning

confidence: 99%

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What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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Section: Multi-access Edge Computingmentioning

confidence: 97%

Section: ) Offloading Mechanismsmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

Self Cite

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“…Microservice offloading may be demanded due to several reasons: (1) demand change, (2) host node (or container) resources running low, and (3) security and privacy. Furthermore, depending on the application, the microservice does not offload as a whole but only as a task, a thread, or a workload [55,56]. Figure 4 shows an example where an orchestrator offloads a Microservice (µService A running in Node A) to another host (Node B).…”

Section: Architecting Microservicesmentioning

confidence: 99%

“…Consequently, a microservice must be capable of working with a plethora of different technologies, a concept called polyglot microservice [43]. Thus, several database systems can be used in a microservice architecture, like Cassandra [57], InfluxDB [136,147], MySQL [55,78], MongoDB [18,29]. Moreover, the microservices are developed using different programming languages and frameworks such as, Python [55,78,79,146,136,81,139,54,140,142,121,28], Java OSGi [110,125], SpringBoot [71,146,115], Java [41,29,79,128,53,94], Golang [18,146,84], Node.JS [125,137], C++ [78,79,140], R [17], and even pure C [126].…”

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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“…As a numeric metric, we can for example quantify the error in the computation, i.e., the deviation from a numeric optimum or the accuracy of the result. To realize our concept, we implement the required mechanisms for adaptable microservices into our previously developed Edge Computing framework [70]. Figure 2 depicts an overview of this design.…”

Section: The Concept Of Adaptable Microservicesmentioning

confidence: 99%

Chameleons on Cloudlets: Elastic Edge Computing Through Microservice Variants

Gedeon,

Wagner,

Skaisgiris

et al. 2021

Preprint

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Common deployment models for Edge Computing are based on (composable) microservices that are offloaded to cloudlets. Runtime adaptations-in response to varying load, QoS fulfillment, mobility, etc.-are typically based on coarsegrained and costly management operations such as resource reallocation or migration. The services themselves, however, remain non-adaptive, worsening the already limited elasticity of Edge Computing compared to Cloud Computing. Edge Computing applications often have stringent requirements on the execution time but are flexible regarding the quality of a computation. The potential benefits of exploiting this trade-off remain untapped.This paper introduces the concept of adaptable microservices that provide alternative variants of specific functionalities. We define service variants that differ w.r.t. the internal functioning of the service, manifested in different algorithms, parameters, and auxiliary data they use. Such variants allow fine-grained trade-offs between the QoS (e.g., a maximum tolerable execution time) and the quality of the computation. We integrate adaptable microservices into an Edge Computing framework, show the practical impact of service variants, and present a strategy for switching variants at runtime.

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A Microservice Store for Efficient Edge Offloading

Cited by 6 publications

References 13 publications

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

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

Chameleons on Cloudlets: Elastic Edge Computing Through Microservice Variants

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