Computing Without Borders: The Way Towards Liquid Computing

Iorio, Marco; Risso, Fulvio; Palesandro, Alex; Camiciotti, Leonardo; Manzalini, Antonio

doi:10.1109/tcc.2022.3229163

Cited by 11 publications

(9 citation statements)

References 30 publications

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“…The four most prominent ones are [32]: Apache Mesos, 12 Docker Swarm, 13 Kubernetes, 14 and Rancher's Cattle. 15 We focus on Kubernetes, as it has recently become the de facto industry standard. All of the major cloud providers offer Kubernetes-based CaaS to their customers (see Table 1).…”

Section: Related Workmentioning

confidence: 99%

“…We show that the existing frameworks, while undoubtedly fine for the cloud, are not suitable for CaaS in the edge cloud. There are a few prior studies concerning these frameworks, such as Vir-tualCluster [4], Arktos [14], and Liqo [15], but this is the first paper to situate them, and EdgeNet, within the existing scientific literature on cloud multitenancy.…”

Section: Introductionmentioning

confidence: 99%

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Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

Şenel,

Mouchet,

Cappos

et al. 2023

IEEE Access

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In recent years, along with containers, the cloud community has rapidly taken up Kubernetes, the de facto industry standard container orchestration system. All major cloud providers currently offer Kubernetesbased Containers as a Service (CaaS). However, when CaaS is offered to multiple independent consumers, or tenants, a multi-instance approach is used, in which each tenant receives its own separate cluster, which imposes significant overhead due to employing virtual machines for isolation. If CaaS is to be offered not only in the cloud, but also in the edge cloud, where resources are limited, another solution is required. In this paper, drawing upon the scientific literature, we provide a novel classification of Kubernetes multitenancy into three approaches: multi-instance through multiple clusters, multi-instance through multiple control planes, and single-instance native. We propose a single-instance multitenancy framework, meaning tenants are served out of a shared control plane in a single cluster. Our empirical findings show that the singleinstance approach imposes a markedly decreased overhead than the other two. However, it entails a tradeoff in workload isolation owing to tenants sharing the compute nodes. There are still means to compensate for such weakened isolation, and we describe how our framework does it. The framework is publicly available as liberally-licensed, free, open-source software that extends Kubernetes. It is in production use within the EdgeNet testbed for researchers.INDEX TERMS Edge computing, cloud computing, Containers as a Service, multitenancy, Kubernetes

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

Şenel,

Mouchet,

Cappos

et al. 2023

IEEE Access

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“…Additionally, recent trends towards Edge, Fog, and Liquid [6] computing solutions favored, even more, the geodistribution of computing facilities [7], [8] in the attempt to guarantee the most appropriate hosting infrastructure for latency-sensitive applications. With the advent of Edge computing, telecommunication networks, IoT systems, and Smart City/Grid technologies have significantly enhanced their operational efficiency and resilience.…”

Section: Introductionmentioning

confidence: 99%

“…Such distributed computing facilities constitute what we refer to as a federation. Each constituent part of the federation (i.e., a cluster) offers (a subset of) its computing resources to the other members of the federation, allowing each individual and possibly autonomous entity to purchase resources when needed, creating a continuum of heterogeneous computing resources [6]. The primary objective of Phare is to optimize the execution of microservices by meeting their computing and communication needs while minimizing deployment costs.…”

Section: Introductionmentioning

confidence: 99%

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Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Castellano,

Galantino,

Risso

et al. 2024

IEEE Open J. Commun. Soc.

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The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for centralized data centers and are ineffective for locating microservices in geographically-distributed edge-like infrastructures. This paper presents Phare, a decentralized scheduling algorithm designed to optimize the placement of microservices by satisfying their computing and communication demands while minimizing deployment costs. Phare employs a heuristic-based approach to solve the NP-Hard scheduling problem, prioritizing the microservices with the more stringent requirements and placing them on the most convenient computing facilities, based on the concept of affinity, contributing to the field by providing a more holistic approach to resource scheduling in edge computing. We validate our approach against Firmament, the state-of-the-art workload scheduling algorithm for component-based applications, on simulated edge infrastructures with hundreds of clusters. Phare achieves up to a 10× reduction in terms of deployment costs compared to Firmament while providing a much lower scheduling latency.

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