Queueing model based resource optimization for multimedia cloud

Nan, Xiaoming; He, Yejun; Guan, Ling

doi:10.1016/j.jvcir.2014.02.008

Cited by 39 publications

(20 citation statements)

References 31 publications

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“…Nan X. M. et al [3] investigated the fundamental concerns with queueing theory and optimization methods for multimedia cloud. Nan G.F. et al [4] proposed a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Zhou

Dong

2015

J Wireless Com Network

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According to the loss network problems caused by limited resources and environment interference factors, we presented a survivability mechanism based on mobility prediction and based on the topological evolution of cloud computing wireless multimedia sensor network (WMSN). First, based on cooperative neural network and opportunity Markov chain model, the multimedia mobile sensor node state and state transition probability could be predicted. Then, the opportunity computational scheme was given based on mobility prediction of dynamic topology evolution mechanism. Finally, using the network topology reconfiguration and opportunities for cloud computing, an enhanced WMSN survivability and end-to-end quality of service guarantee mechanism was proposed. Experimental results show that compared with the static scheme for WMSNs, the proposed survivability mechanism has the obvious advantage in the node protection, data communication, network life cycle, and decodable frame rate and other aspects, and can effectively improve the invulnerability of WMSNs.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Zhou

Dong

2015

J Wireless Com Network

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“…In [19], a queue model for multimedia cloud is proposed. The model is composed of three concatenated queues: are the schedule queue, the computation queue, and the transmission.…”

Section: Related Workmentioning

confidence: 99%

Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data

Hammer

Yazidi

Bratterud

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes of the requests are varied to investigate the impact on the computer system. A challenge with such an approach is that we do not know if the artificial requests processes are realistic when the system are applied in a real setting. Motivated by this challenge, we develop a method to estimate the properties of the underlying request processes to the computer system when the system is used in a real setting. In particular we look at the problem of recovering the request patterns to a CPU processor. It turns out that this is a challenging statistical estimation problem since we do not observe the request process (rate and size of the requests) to the CPU directly, but only the average CPU usage in disjoint time intervals.In this paper we demonstrate that, quite astonishingly, we are able to recover the properties of the underlying request process (rate and sizes of the requests) by using specially constructed statistics of the observed CPU data and apply a recently developed statistical framework called Approximate Bayesian Computing.

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“…Nan et al . try to solve the response time minimization problem and resource cost minimization problem in single‐class service, multiple‐class service, and priority service cases. Kaur and Chana propose QoS‐aware resource elasticity framework, which allows service providers to make an assessment of the application behavior.…”

Section: Related Workmentioning

confidence: 99%

“…Internet has witnessed a dramatic increase in the popularity of cloud computing systems, which rent resources on demand and bill on a pay-as-you-go basis. There is an extensive literature on cloud-based resource scheduling for multiple services [14][15][16][17][18][19]. Lee [16] solves the cloud resource management problem from providers' perspective and users' viewpoint.…”

Section: Related Workmentioning

confidence: 99%

“…They design a queueing network-based model to analyze the users' viewing behaviors in a multichannel VoD application and propose a dynamic cloud resource provisioning algorithm that supports Video on Demand (VoD) streaming with low cloud utilization cost. Nan et al [17] try to solve the response time minimization problem and resource cost minimization problem in single-class service, multiple-class service, and priority service cases. Kaur and Chana [15] propose QoS-aware resource elasticity framework, which allows service providers to make an assessment of the application behavior.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

FRP: a fast resource placement algorithm in distributed cloud computing platform

Wei

Zhang

Liu

et al. 2015

Concurrency and Computation

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Summary We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.

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Queueing model based resource optimization for multimedia cloud

Cited by 39 publications

References 31 publications

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data

FRP: a fast resource placement algorithm in distributed cloud computing platform

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