Snooze: A Scalable and Autonomic Virtual Machine Management Framework for Private Clouds

Feller, Eugen; Rilling, Louis; Morin, Christine

doi:10.1109/ccgrid.2012.71

Cited by 101 publications

(79 citation statements)

References 13 publications

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“…Each server is running the Debian Squeeze operating system with Kernel-based Virtual Machine (KVM) enabled. In the virtualized cluster configuration, the Snooze [17] cloud stack is used to manage the parapluie servers. Snooze system management services are deployed on three dedicated Sun Fire X2270 servers of the parapide cluster.…”

Section: Platform Setupmentioning

confidence: 99%

Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study

Feller

Ramakrishnan

Morin

2015

Journal of Parallel and Distributed Computing

Self Cite

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The exponential growth of scientific and business data has resulted in the evolution of the cloud computing environments and the MapReduce parallel programming model. The focus of cloud computing is increased utilization and power savings through consolidation while MapReduce enables large scale data analysis.Hadoop, an open source implementation of MapReduce has gained popularity in the last few years. In this paper, we evaluate Hadoop performance in both the traditional model of collocated data and compute services as well as consider the impact of separating out the services. The separation of data and compute services provides more flexibility in environments where data locality might not have a considerable impact such as virtualized environments and clusters with advanced networks. In this paper, we also conduct an energy efficiency evaluation of Hadoop on physical and virtual clusters in different configurations. Our extensive evaluation shows that: (1) coexisting virtual machines on servers decrease the disk throughput; (2) performance on physical clusters is significantly better than on virtual clusters; (3) performance degradation due to separation of the services depends on the data to compute ratio; (4) application completion progress correlates with the power consumption and power consumption is heavily application specific. Finally, we present a discussion on the implications of using cloud environments for big data analyses.

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Section: Platform Setupmentioning

confidence: 99%

Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study

Feller

Ramakrishnan

Morin

2015

Journal of Parallel and Distributed Computing

Self Cite

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“…There are several main-stream open source cloud management frameworks, including OpenNebula [19], OpenStack [23], Eucalyptus [22], Nimbus [13], Snooze [9] and Origo [25] (used in the SciCloud). These frameworks greatly facilitate the cloud management: allocating computing resources according to user demands, failure recovery, load balancing, system monitoring and others.…”

Section: Related Workmentioning

confidence: 99%

SciCloud: A Scientific Cloud and Management Platform for Smart City Data

Liu¹,

Nielsen²,

Heller³

et al. 2017

2017 28th International Workshop on Database and Expert Systems Applications (DEXA)

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Abstract-The pervasive use of Internet of Things and smart meter technologies in smart cities increases the complexity of managing the data, due to their sizes, diversity, and privacy issues. This requires an innovate solution to process and manage the data effectively. This paper presents an elastic private scientific cloud, SciCloud, to tackle these grand challenges. SciCloud provides on-demand computing resource provisions, a scalable data management platform and an in-place data analytics environment to support the scientific research using smart city data.

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“…The Resource Manager interacts with a VM manager software, such as Nimbus [21], OpenNebula [22] or Snooze [23].…”

Section: Architecturementioning

confidence: 99%

SLA-Based Profit Optimization in Cloud Bursting PaaS

Dib

Parlavantzas²,

Morin³

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-PaaS (Platform as a service) is an increasingly popular cloud model, providing a complete development and hosting environment for cloud applications. As the use of PaaS becomes pervasive, defining and maintaining SLAs (Service Level Agreements) between PaaS customers and providers becomes essential. Useful SLAs should provide guarantees on application quality properties (e.g., response time) rather than on resource availability (e.g., number of virtual machines). Current PaaS offerings either provide no support for providing such guarantees or provide support targeting a restricted set of application types. In this paper, we propose an SLA-driven PaaS architecture, called Meryn, which supports cloud bursting and is designed to be easily extensible to host new application types. Meryn relies on a decentralized optimization policy that aims at maximizing the overall provider profit, taking into account the payment of penalties incurred when quality guarantees are unsatisfied. We implemented and evaluated a prototype of the Meryn system through a series of simulations on the Grid5000 testbed.The results show that our approach provides up to 14.77% more profit for the provider and uses up to 80.99% less public clouds resources compared with a basic approach.

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Snooze: A Scalable and Autonomic Virtual Machine Management Framework for Private Clouds

Cited by 101 publications

References 13 publications

Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study

Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study

SciCloud: A Scientific Cloud and Management Platform for Smart City Data

SLA-Based Profit Optimization in Cloud Bursting PaaS

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