2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing 2014
DOI: 10.1109/ucc.2014.164
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Scalable Analytic Models for Performance Efficiency in the Cloud

Abstract: This paper presents a scalable model-driven approach to quantify the availability of resources and optimal distribution of tasks over these resources, such that the average response time of tasks is minimized. To reduce the complexity of analysis and solution time, we use an integrated stochastic based approach. To achieve this, first we use clustering algorithm to group the tasks into distinct classes with similar characteristics in terms of resource and performance requirements. Second, we quantify the resou… Show more

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Cited by 3 publications
(3 citation statements)
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“…It depicts the queuing performance model in the cloud for the service requests to be performed. Figure 2 A data owner is responsible for generating the encrypted files and uploading the files to the cloud server [27]. Trusted authority checks for the incoming requests and sends the required key to the data owner.…”
Section: Fig 1 Basic System Modelmentioning
confidence: 99%
“…It depicts the queuing performance model in the cloud for the service requests to be performed. Figure 2 A data owner is responsible for generating the encrypted files and uploading the files to the cloud server [27]. Trusted authority checks for the incoming requests and sends the required key to the data owner.…”
Section: Fig 1 Basic System Modelmentioning
confidence: 99%
“…Another approach to increasing capacity efficiency has been to dynamically add or remove servers as demand changes [2], [3], [47]- [53], or even turn off unused server capacity [54]. In practice this approach is difficult to implement for large online services because it requires existing systems to be ported to run on top of a new infrastructure, and the complexity of dynamic capacity makes the system difficult to debug.…”
Section: Related Workmentioning
confidence: 99%
“…Reliable state management and exible scheduling are essential in running modern distributed applications on clusters [9], [6], [7], [3], [4], [10], [11]. Academic and industrial researchers have developed several other cluster management frameworks for resource e ciency, such as Mesos [5], Omega [8], Borg [11] etc.…”
Section: Related Workmentioning
confidence: 99%