PTC: Pick-Test-Choose to Place Containerized Micro-Services in IoT

Nath, Shubha Brata; Chattopadhyay, Subhrendu; Karmakar, Raja; Addya, Sourav Kanti; Chakraborty, Sandip; Ghosh, Soumya K.

doi:10.1109/globecom38437.2019.9013163

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…On the other hand, there are multiple options of edge nodes in the collected papers, because we omit research results on binary offloading decisions. Usually the challenges addressed in these papers stem from the choice of edge nodes, the latency requirements, Online [52], [118]- [124] [59], [125]- [128] [129]- [140] [100], [141]- [146] [141], [143], [147]- [153] [109], [111], [154]- [160] [113] Online with migration [161]- [167] [168]- [170] Hybrid [171], [172] [173]- [179] [180]- [182] [183] and compute capabilities. 5) Cloud-edge-terminal: Finally, the last category might be considered as an extension of the previous one by adding remote clouds as options to the offloading decision.…”

Section: A Platform Componentsmentioning

confidence: 99%

“…2) Resource allocation in edge and fog computing systems: This section is devoted to the research works addressing services consisting of multiple components, such as VMs, containers or tasks, but there is no relation among the constituent elements taken into account. Following our Platform components dimension, the underlying infrastructure, where the service components are mapped to, can be multi-edge [80], [95], [129], [131]- [133], [135], [137], [140], [169], [180], [182], cloud-edge [59], [64], [68], [70], [75], [127], [161], [170], [174], [176], [178] or multi-cloud [51] but the core problem to be tackled, i.e., component placement, is similar.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…The online algorithm determines the best matching between application components and edge/core servers using the Hungarian method, and then applies a local search procedure to consider the communication requirements along with the users' movements and the previous placement result to improve their solution. Another related aspect is migration which is considered by various models [133], [161], [169], [174]. The authors in [161] formalize the placement problem for microservices constructed from smaller containerized components.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…Another related aspect is migration which is considered by various models [133], [161], [169], [174]. The authors in [161] formalize the placement problem for microservices constructed from smaller containerized components. Their main objective is to minimize the total response time, considering eventual migration times of the microservice containers.…”

Section: B Multiple Componentsmentioning

confidence: 99%

“…From the operators' perspective, optimizing for revenue, e.g., in [59], [68], [174], [180], or for utilization, e.g., in [127], [170], [176], [178], or for both revenue and utilization, e.g., in [129], [131], [140], is a straightforward decision. Some research works, such as [64], [132], [161], take the delay as the main subject of the optimization, which is also a reasonable choice as the quality of experience of the targeted edge applications are mainly determined by the perceived latency. Other researchers combine the delay with energy [80], with utilization [51], [70], [169] or with load balancing [135].…”

Section: B Multiple Componentsmentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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