Autonomic Virtualized Environments

Menascé, Daniel A.; Bennani, Mohamed N.

doi:10.1109/icas.2006.13

Cited by 86 publications

(51 citation statements)

References 12 publications

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“…Existing solutions in this area [25]- [28] do not take into account contention on data, targeting mainly CPU-bound applications, even though they also consider the database tier. Moreover, they neglect the effect of data distribution/replication, which have a major impact on the performance of partially replicated data stores.…”

Section: Related Workmentioning

confidence: 99%

Performance Modelling of Partially Replicated In-Memory Transactional Stores

Didona

Romano

2014

2014 IEEE 22nd International Symposium on Modelling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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Abstract-This paper presents PROMPT, a PeRfOrmance Model for Partially replicated in-memory Transactional cloud stores. PROMPT combines white box Analytical Modelling and Machine Learning techniques, with the goal of achieving the best of the two methodologies: low training times, high extrapolation power, and portability across heterogeneous cloud infrastructures. We validate PROMPT via an extensive experimental study based on a popular open-source transactional in-memory data store (Red Hat's Infinispan), industry-standard benchmarks, and deployments on both public and private cloud infrastructures.

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Section: Related Workmentioning

confidence: 99%

Performance Modelling of Partially Replicated In-Memory Transactional Stores

Didona

Romano

2014

2014 IEEE 22nd International Symposium on Modelling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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“…It offers a free move of virtual resources (routers and links) from one physical equipment to another to simplify physical network-management tasks. Moreover, [12] proposes techniques for dynamic allocation of processing resources (CPU) to virtual machines. Furthermore, in [13], authors present an autonomic system called VIOLIN.…”

Section: Related Workmentioning

confidence: 99%

Towards an Autonomic Piloting Virtual Network Architecture

Fajjari

Ayari

Braham³

et al. 2011

2011 4th IFIP International Conference on New Technologies, Mobility and Security

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Abstract-TheInternet has known an impressive success in the three last decades. Nonetheless, due to its size and scope, this large network has become victim of its own success. Innovative approaches are required to overcome the shortfalls of current systems and to design Next Generation Internet. In this context, network virtualization presents a viable solution to deal with the current Internet impasse. It provides a promising way to deploy different network architectures and protocols over a shared physical infrastructure. However, in spite of its multiple advantages, network virtualization adds more complexity on network systems. A promising solution to address this huge complexity consists on developing systems which are capable of managing themselves, called autonomic computing systems or self-* systems. This paper proposes an agent-based autonomic framework which is able to self manage virtual resources. We provide a detail description of the proposed autonomic architecture and we focus on a real testbed implementation and testing of our framework. Experiment results show the ability of our system to self-configure its resources in order to maintain a required QoS level.

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“…The utility functions allow the on-the fly identification of best feasible state. In paper [10] the autonomic computing was used to allocate CPU resources dynamically for VMs by optimizing utility functions. The first utility function used to allocate resource is based on CPU share and priority based models explained in [10].…”

Section: Utility Functionsmentioning

confidence: 99%

“…In paper [10] the autonomic computing was used to allocate CPU resources dynamically for VMs by optimizing utility functions. The first utility function used to allocate resource is based on CPU share and priority based models explained in [10]. This paper addressed the technique used to dynamically allocate resources to various virtual machines with respect to varying workloads.…”

Section: Utility Functionsmentioning

confidence: 99%