Stochastic resource prediction and admission for interactive sessions on multimedia servers

Friedrich, Matthias G.; Hollfelder, Silvia; Aberer, Karl

doi:10.1145/354384.354446

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…Existing desktop workload studies [27], [11], [31], [9], [14], [15] have been mostly focused on describing properties of desktop usage so that the free computing cycles on workstations could be used to perform batch computation. However, this perspective led to different (than described in this paper) analysis since the explicit aggregation properties of multiple desktops were not considered.…”

Section: Related Literaturementioning

confidence: 99%

“…Exploring the extent of the potential gain as well as designing algorithms that can realize the potential efficiency gains while maintaining high quality of service requires good understanding of desktop workload properties. Even though this topic received some attention, researchers who looked into the statistical properties of desktop workload [27], [11], [31], [9], [14], [15] did so mainly for reasons different than desktop consolidation. The key interest was in running grid applications using free processing cycles of idle workstations.…”

Section: Introductionmentioning

confidence: 99%

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Desktop workload study with implications for desktop cloud resource optimization

Kochut

Beaty

Shaikh

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Desktop cloud is a new delivery model in which end users connect to virtual desktops running in remote data centers. This paradigm offers multiple benefits both in terms of manageability as well as efficiency improvements. However, realizing this potential requires better understanding of desktop workload and its implications for desktop consolidation.We analyze CPU and memory usage on a sample of 35 desktops using a fine-grained 10 second averaging interval. Results provide insights into achievable efficiency improvements from desktop consolidation as well as detailed autocorrelation and variability behavior as a function of number of aggregated desktops. We also propose an interactivity classification method leading to functional form suitable for estimating residual durations of interactivity states. This finding can be leveraged in on-line proactive management algorithms for desktop cloud optimization.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Desktop workload study with implications for desktop cloud resource optimization

Kochut

Beaty

Shaikh

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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“…The work being done in this area, e.g. [8][9][10], typically proposes the use of states to capture user interactions and the use of Markov chains to model probabilistic transitions. Additional problems presented by thin-client systems are: (i) the need to also measure user-perceived performance on the client for accurate latency measurements; and (ii) the need to consider the resource consumption of the remote display server in addition to that of the application.…”

Section: Application Performance Modelmentioning

confidence: 99%

Resource allocation for remote desktop sessions in Utility Grids

Talwar

Agarwalla

Basu

et al. 2005

Concurrency and Computation

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SUMMARYEmerging large-scale utility computing systems such as Grids promise computing and storage to be provided to end users as a utility. System management services deployed in the middleware are a key to enabling this vision. Utility Grids provide a challenge in terms of scale, dynamism and heterogeneity of resources and workloads. In this paper, we present a model-based architecture for resource allocation services for Utility Grids. The proposed service is built in the context of interactive remote desktop session workloads and takes application performance QoS models into consideration. The key design guidelines are hierarchical request structure, application performance models, remote desktop session performance models, site admission control, multi-variable resource assignment system and runtime session admission control. We have also built a simulation framework that can handle mixed batch and remote desktop session requests, and have implemented our proposed resource allocation service into the framework. We present some results from experiments using the framework. Our proposed architecture for resource allocation services addresses the needs of emerging utility computing systems and captures the key concepts and guidelines for building such services in these environments.

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“…The work being done in this area eg. [7,8,9] typically propose the use of states to capture user interactions, and the use of Markov chains to model probabilistic transitions. Additional problems presented by thin client systems is (i) the need to also measure user perceived performance on the client for accurate latency measurements, and (ii) the need to consider the resource consumption of the remote display server in addition to that of the application.…”

Section: Application Performance Modelmentioning

confidence: 99%

Architecture for resource allocation services supporting interactive remote desktop sessions in utility grids

Talwar

Agarwalla²,

Basu

et al. 2004

Proceedings of the 2nd Workshop on Middleware for Grid Computing -

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Emerging large scale utility computing systems like Grids promise computing and storage to be provided to end users as a utility. System management services deployed in the middleware are a key to enabling this vision. Utility Grids provide a challenge in terms of scale, dynamism, and heterogeneity of resources and workloads. In this paper, we present a model based architecture for resource allocation services for Utility Grids. The proposed service is built in the context of interactive remote desktop session workloads and takes application performance QoS models into consideration. The key design guidelines are hierarchical request structure, application performance models, remote desktop session performance models, site admission control, multi-variable resource assignment system, and runtime session admission control. We have also built a simulation toolkit that can handle mixed batch and remote desktop session requests, and have implemented our proposed resource allocation service into the toolkit. We present some results from experiments done using the toolkit. Our proposed architecture for resource allocation services addresses the needs of emerging utility computing systems and captures the key concepts and guidelines for building such services in these environments.

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Stochastic resource prediction and admission for interactive sessions on multimedia servers

Cited by 11 publications

References 24 publications

Desktop workload study with implications for desktop cloud resource optimization

Desktop workload study with implications for desktop cloud resource optimization

Resource allocation for remote desktop sessions in Utility Grids

Architecture for resource allocation services supporting interactive remote desktop sessions in utility grids

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