An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

Faragardi, Hamid Reza; Dehnavi, Saeid; Nolte, Thomas; Kargahi, Mehdi; Fahringer, Thomas

doi:10.1002/spe.2592

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…A number of authors also focused on the problem of optimizing VM placement in a cloud infrastructure to better host real-time workloads and mitigate interferences of colocated instances. For example, in [51] authors propose to tackle an energy-aware VM placement optimization problem via simulated annealing. In [52], authors propose to place VMs by recurring to machine learning models that have been trained on historical data about resource usage and availability within the infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

Cucinotta

Abeni

Marinoni

et al. 2023

IEEE Trans. Cloud Comput.

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In this paper, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

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Section: Related Workmentioning

confidence: 99%

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

Cucinotta

Abeni

Marinoni

et al. 2023

IEEE Trans. Cloud Comput.

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“…Faragardi and collaborators present an energy-aware resource provisioning mechanism to optimise the energy consumption in the data-centres supporting the cloud [32]. In particular, this approach serves real-time periodic tasks following the SaaS model.…”

Section: Energy-aware Cloud Systemsmentioning

confidence: 99%

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Cañizares

Núñez

Lara

et al. 2020

Journal of Systems and Software

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Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging.To solve these issues we present MT-EA4Cloud, a formal approach to check the correctnessfrom an energy-aware point of view -of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators.

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“…The first article of this special issue, entitled “An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center”, was presented by Faragardi et al and addressed the resource provisioning for real‐time applications in cloud data centers with the goal of minimizing energy consumption through the reduction of used servers. The authors formalized the tackled problem as an integer linear optimization problem.…”

Section: Summary Of the Contributionsmentioning

confidence: 99%

“…From the providers' perspective, the first part of this special issue is particularly concerned with the management of cloud resources by means of optimization methods. The first two articles propose models and methods to support energy‐aware resource provisioning and virtual machine (VM) consolidation in cloud data centers. Novel power and cooling strategies using metaheuristics and a numerical algorithm for optimal load distribution to optimize the performance and energy consumption in cloud data centers are discussed in the third and fourth article, respectively.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics in cloud computing

et al. 2018

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The cloud computing paradigm is increasingly becoming mainstream and a growing number of companies and research organizations seek to gain value from its unique characteristics, service models, and deployment forms. According to recent estimates of Gartner 1 and IDC, 2 the public cloud services market is projected to grow at a compound annual growth rate of 16.5% and 21.5%, respectively, from 2015-2020. Gartner expects the highest growth rates in spendings on public cloud infrastructure services. This implies not only a high relevance of approaches supporting the management of cloud infrastructures, but also the need for approaches facilitating and improving the use of cloud infrastructures from the consumer's point of view. The realization of the computing utility vision, commoditized and delivered in a manner similar to traditional utilities like water and electricity, 3 however, currently fails because of the huge complexity in cloud environments where many complex decisions have to be made, as recently reported in one of the largest global IT leadership surveys. 4 In addition, due to the environmental impact of cloud computing and the relation between energy consumption and costs, a sustainable use of resources is of considerable importance and requires the development of new policies and techniques. As a result, the success of cloud computing adoption and provision is highly dependent on efficient and intelligent decision making.This gives rise to many complex combinatorial optimization problems both from the consumers' and providers' perspective. 5 The implications of decisions need to be quantifiable in terms of different dimensions (eg, economics, sustainability, performance) to allow a comparison of alternatives and to decide on the right course of action. Cloud service providers aim to reduce operational expenses and improve economies of scale to maximize profits and provide competitive prices in a highly competitive market. Therefore, cloud service providers are particularly concerned with a cost-effective, energy-efficient, fault-tolerant utilization and management of physical and virtual computing resources, whereby the quality-of-service (QoS) requirements of consumers have to be satisfied. From the consumers' point of view, particularly an efficient selection and utilization of cloud providers and cloud services, as well as the management thereof, is of interest. Several requirements in terms of costs, service quality, and legal constraints need to be taken into account when placing applications into the cloud (for extensive overviews, the interested reader is referred to Heilig et al 6,7 ). This also involves specific characteristics of cloud computing, such as the options of scalability, load balancing, and replication. Moreover, cloud-related application areas and research directions have been expanded to novel paradigms in recent years including fog computing, serverless computing, and internet of things (IoT), leading to new open issues and optimization challenges regarding resource management...

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An energy‐aware resource provisioning scheme for real‐time applications in a cloud data center

Cited by 10 publications

References 47 publications

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Metaheuristics in cloud computing

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