Scalable IoT/M2M Platforms Based on Kubernetes-Enabled NFV MANO Architecture

Chen, Hung-Li; Lin, Fuchun Joseph

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00188

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Our test environment consisted of a Kubernetes cluster with six nodes, a single master node, and five worker nodes using Docker 20.10.22 and Kubernetes 1.26.0 on OpenStack with an Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10 GHz (36 cores) and 512 GB of RAM. Note that such an environment is comparable to the existing studies that explore the auto-scaling behaviors of the Kubernetes cluster [16,43,44]. Each node ran on a virtual machine using Ubuntu 20.04, allocating 8 CPU cores and 16 GB RAM, respectively.…”

Section: Evaluation and Implicationsmentioning

confidence: 99%

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

Hong,

Kim,

Nam

et al. 2024

Sensors

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A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a-Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on commercial public cloud offerings. However, the lack of standardized guidelines on K8s abstraction to fairly schedule and allocate resources on auto-scaling configuration options for such on-premise hosting environment in serverless computing poses challenges in meeting the service level objectives (SLOs) of diverse workloads. This study fills this gap by exploring the relationship between auto-scaling behavior and the performance of FaaS workloads depending on scaling-related configurations in K8s. Based on comprehensive measurement studies, we derived the logic as to which workload should be applied and with what type of scaling configurations, such as base metric, threshold to maximize the difference in latency SLO, and number of responses. Additionally, we propose a methodology to assess the scaling efficiency of the related K8s configurations regarding the quality of service (QoS) of FaaS workloads.

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Section: Evaluation and Implicationsmentioning

confidence: 99%

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

Hong,

Kim,

Nam

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…These all require orchestrating lots of machines at the edge with emphasis on low latency and reliability. Recent work has seen Kubernetes already become popular for this orchestration at the edge [21,22]. Kubernetes, with a more performant and available core, can fit the orchestration frameworks required for these use cases to offload work from the cloud, improve latency for requests and provide service-level adaptability.…”

Section: Related Workmentioning

confidence: 99%

Rearchitecting Kubernetes for the Edge

Jeffery

Howard

Mortier

2021

Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking

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Recent years have seen Kubernetes emerge as a primary choice for container orchestration. Kubernetes largely targets the cloud environment but new use cases require performant, available and scalable orchestration at the edge. Kubernetes stores all cluster state in etcd, a strongly consistent key-value store. We find that at larger etcd cluster sizes, offering higher availability, write request latency significantly increases and throughput decreases similarly. Coupled with approximately 30% of Kubernetes requests being writes, this directly impacts the request latency and availability of Kubernetes, reducing its suitability for the edge. We revisit the requirement of strong consistency and propose an eventually consistent approach instead. This enables higher performance, availability and scalability whilst still supporting the broad needs of Kubernetes. This aims to make Kubernetes much more suitable for performance-critical, dynamically-scaled edge solutions. CCS CONCEPTS• Computing methodologies → Distributed computing methodologies.

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“…These platforms have two main advantages: I) the platforms rely on the parent company cloud infrastructure (leading to increase scalability and reliability), and II) the customers can connect to third-party services (usually part of the parent company) to access several advanced features, such as persistent storage, data analytics, and data visualisation. Several authors have shown the positive impact of cloud-based solutions [ 45 , 46 ] as the basis of an IoT platform.…”

Section: State Of the Artmentioning

confidence: 99%

Building an IoT Platform Based on Service Containerisation

Antunes

Santiago

Manso

et al. 2021

Sensors

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IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

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Scalable IoT/M2M Platforms Based on Kubernetes-Enabled NFV MANO Architecture

Cited by 8 publications

References 7 publications

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

On the Analysis of Inter-Relationship between Auto-Scaling Policy and QoS of FaaS Workloads

Rearchitecting Kubernetes for the Edge

Building an IoT Platform Based on Service Containerisation

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