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 11 publications

(5 citation statements)

References 47 publications

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“…The distinctive topic Metaheuristics in Cloud Computing is a collection of several offerings that use advanced stochastic optimization methods including computational mathematics and metaheuristics in order to improve the state-of-the-art of decision-support systems in the context of cloud computing [66][67][68][69]. By reducing the number of unused servers, Faragardi et al [70] also addressed the scheduling of resources for real-time software in cloud data centers. Employing a method based on metaheuristics, the researchers formalized the analyzed challenge as an integer-linear optimization challenge.…”

Section: ) How Would Metaheuristic Algorithms Be Used To Optimize Cur...mentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

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Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

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Section: ) How Would Metaheuristic Algorithms Be Used To Optimize Cur...mentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

View full text Add to dashboard Cite

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

Cited by 11 publications

References 47 publications

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

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

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