Autonomic SLA-Aware Service Virtualization for Distributed Systems

Kertész, Attila; Kecskeméti, Gábor; Brandić, Ivona

doi:10.1109/pdp.2011.17

Cited by 24 publications

(7 citation statements)

References 19 publications

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“…Resource planning supported SLA has been conduct by following papers, automatic SLA-aware asset arranging algorithmic program to decrease SLA encroachment and violation rate that works in cloud virtual condition to execute benefit while not infringement of SLA. This outline joins three parts: benefit agent, understanding arrange and request preparing to execute benefit while not SLA infringement (Kertesz et al, 2011). QoS based resource-arranging system to estimate the execution underneath disparate asset assignment by resize of VM.…”

Section: Sla-based Resource Managementmentioning

confidence: 99%

Cloud Resource Optimization System Based on Time and Cost

Dewangan¹,

Agarwal²,

Tanupriya³

et al. 2020

Int J Math, Eng, Manag Sci

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Resource management in cloud could be a time and cost-effective activity if it is managed property. These resources are accessible and computable which is totally dependent upon the management techniques applied in cloud. In a cloud setting, heterogeneous, vulnerability, and scattering of resources creates many issues of distribution among the workloads which need to be compute. Specialists still face inconveniences to pick the prudent, material and expend less time to execution of resource portion to the cloud. This investigation delineates an expansive composed writing examination of asset administration inside the space of cloud typically and cloud asset administration based on SLA with multi-objective functions like cost and time. In this paper, an autonomic cloud resource-management technique is proposed to resolve identified issues by adopting the self-characteristics mechanism and improved Antlion optimization algorithm and tested in cloudsim toolkit and Aws Ec2 environment. The implementation results of proposed work are the evidence that it is better performing as compared with the existing frameworks, however, the performance evaluation method depends upon the different cloud environment and it may vary.

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Section: Sla-based Resource Managementmentioning

confidence: 99%

Cloud Resource Optimization System Based on Time and Cost

Dewangan¹,

Agarwal²,

Tanupriya³

et al. 2020

Int J Math, Eng, Manag Sci

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“…However, we have already discussed the application of the optimization facility on the biochemical application of TINKER in our previous paper [39].…”

Section: Conmentioning

confidence: 99%

Virtual Appliance Size Optimization with Active Fault Injection

Kecskeméti

Terstyánszky

Kacsuk

2012

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Virtual appliances store the required information to instantiate a functional virtual machine on Infrastructure as a Service (IaaS) cloud systems. Large appliance size obstructs IaaS systems to deliver dynamic and scalable infrastructures according to their promise. To overcome this issue, this article offers a novel technique for virtual appliance developers to publish appliances for the dynamic environments of IaaS systems. Our solution achieves faster virtual machine instantiation by reducing the appliance size while maintaining its key functionality. The new virtual appliance optimization algorithm identifies the removable parts of the appliance. Then, it applies active fault injection to remove the identified parts. Afterwards, our solution assesses the functionality of the reduced virtual appliance by applying the -appliance developer provided -validation algorithms. We also introduce a technique to parallelize the fault injection and validation phases of the algorithm. Finally, the prototype implementation of the algorithm is discussed to demonstrate the efficiency of the proposed algorithm through the optimization of two well-known virtual appliances. Results show that the algorithm significantly decreased virtual machine instantiation time and increased dynamism in IaaS systems.

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“…In our integrated framework we can obtain low-level data/events from the process or from the context of execution using Dynamo [2], or from the infrastructure using Laysi [8]. We can then manipulate the data to obtain higher-level information using the event correlation capabilities provided by EcoWare [1].…”

Section: Monitoring and Correlationmentioning

confidence: 99%

“…Building upon our past experiences we have integrated process and infrastructure level monitoring [2,8] with a correlation technique that makes use of complex event processing [1]. The correlated data, combined with machine-learning techniques, allow us to pinpoint where the problems lie in the multi-layered system, and where it would be more convenient to adapt [7,12].…”

Section: Introductionmentioning

confidence: 99%

Multi-layered Monitoring and Adaptation

Guinea

Kecskeméti

Marconi

et al. 2011

Service-Oriented Computing

Self Cite

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Abstract. Service-based applications have become more and more multilayered in nature, as we tend to build software as a service on top of infrastructure as a service. Most existing SOA monitoring and adaptation techniques address layer-specific issues. These techniques, if used in isolation, cannot deal with real-world domains, where changes in one layer often affect other layers, and information from multiple layers is essential in truly understanding problems and in developing comprehensive solutions. In this paper we propose a framework that integrates layer specific monitoring and adaptation techniques, and enables multi-layered control loops in service-based systems. The proposed approach is evaluated on a medical imaging procedure for Computed Tomography (CT) Scans, an eHealth scenario characterized by strong dependencies between the software layer and infrastructural resources.

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Autonomic SLA-Aware Service Virtualization for Distributed Systems

Cited by 24 publications

References 19 publications

Cloud Resource Optimization System Based on Time and Cost

Cloud Resource Optimization System Based on Time and Cost

Virtual Appliance Size Optimization with Active Fault Injection

Multi-layered Monitoring and Adaptation

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