Time-Sensitive Provisioning of Bare Metal Compute as a Cloud Service

Venkateswaran, Sreekrishnan; Sarkar, Santonu

doi:10.1109/cloud.2019.00077

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…A abordagem dos autores realiza benchmarks periodicamente dentro de um cliente em uma máquina virtual para identificar desvios na performance como uma técnica de classificac ¸ão. Venkateswaran e Sarkar [Venkateswaran and Sarkar 2019] propõe um SLA de posicionamento de VM bare-metal para fornecer garantias de performance. Por outra perspectiva, Truyen et.…”

Section: Trabalhos Relacionadosunclassified

Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina

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et al. 2022

Anais Estendidos Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC Estendido 2022)

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O artigo propõe um modelo de aprendizado de máquina para detectar ambientes Kubernetes com overbook de recursos em um contêiner do Docker. As métricas do aplicativo e do sistema foram coletadas continuamente, as quais foram usadas como entrada para o modelo para identificar interferência causada por multi-tenancy. Os experimentos foram executados em um cluster Kubernetes, com um aplicativo de Big Data baseado em contêiner, o que mostrou que o modelo pode detectar overbooking de recursos com precisões de até 98% em um cenário que pode causar degradação no desempenho do aplicativo, com taxas de overbooking de até 1,2.

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Section: Trabalhos Relacionadosunclassified

Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina

Horchulhack

Viegas

Santin

et al. 2022

Anais Estendidos Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC Estendido 2022)

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“…As part of our earlier work, 9 we had built an initial version of a time-sensitive bare metal supply chain model that we expanded in Section 5 of this article. Some existing methods have attempted to provide value-added bare metal services from a set of cloud service providers, but unlike our work, do not address the issue of unpredictability of provisioning latency.…”

Section: Related Workmentioning

confidence: 99%

Architecture of a time‐sensitive provisioning system for cloud‐native software

2021

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Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud-native business models that leverage containerized multi-tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time-sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of the Reserved Instance Allocation Problem (RIAP), which we first prove to be NP-hard. We then design a heuristic-based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic-based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit-to-investment ratio for a broker who seeks to operate a commerce of virtual machines with time-sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost-sensitive fulfillment of bare metal provisioning requests in a time-sensitive manner.

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A survey of resource provisioning problem in cloud brokers

Pan

Liu

2022

Journal of Network and Computer Applications

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Time-Sensitive Provisioning of Bare Metal Compute as a Cloud Service

Cited by 4 publications

References 3 publications

Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina

Detecção de Overbooking em Aplicações Baseadas em Docker Através de Aprendizagem de Máquina

Architecture of a time‐sensitive provisioning system for cloud‐native software

A survey of resource provisioning problem in cloud brokers

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