Fastrack for taming burstiness and saving power in multi-tiered systems

Caniff, Andrew; Lü, Lei; Mi, Ningfang; Cherkasova, Ludmila; Smirni, Evgenia

doi:10.1109/itc.2010.5608741

Cited by 17 publications

(14 citation statements)

References 14 publications

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“…In this paper, we consider to exploit this algorithm for identifying changes in cloud user demands. However, we also find that the algorithm in [10] cannot accurately detect the start and the end of some bursts. Especially, the end of a burst is easily missed because of the deficiency of the algorithm, which results in the unnecessary delay in the detection of changes from peak to idle.…”

Section: B Online Versionmentioning

confidence: 84%

“…In [10], an algorithm has been proposed to use I coupled with information about the current and previous arrival rates to detect changes in arrival intensities. In this paper, we consider to exploit this algorithm for identifying changes in cloud user demands.…”

Section: B Online Versionmentioning

confidence: 99%

“…Especially, the end of a burst is easily missed because of the deficiency of the algorithm, which results in the unnecessary delay in the detection of changes from peak to idle. In addition, the monitoring window size used in [10] is too large, which although is beneficial to capture the state transition, further extends the delay of detections in the ending of bursts.…”

Section: B Online Versionmentioning

confidence: 99%

“…In order to improve the prediction accuracy, we refine the algorithm by dynamically adjusting the monitoring window size m instead of a fixed value in [10] to trade off the contradiction of monitoring window size and detection delay. To shorten the detection delay, a small window size is preferred which however may miss the detections of state changes, especially the end of bursts.…”

Section: B Online Versionmentioning

confidence: 99%

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ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

Tai

Zhang

et al. 2011

30th IEEE International Performance Computing and Communications Conference

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Abstract-Cloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. However, burstiness in user demands often dramatically degrades the application performance. In order to satisfy peak user demands and meet Service Level Agreement (SLA), efficient resource allocation schemes are highly demanded in the cloud. However, we find that conventional load balancers unfortunately neglect cases of bursty arrivals and thus experience significant performance degradation. Motivated by this problem, we propose new burstiness-aware algorithms to balance bursty workloads across all computing sites, and thus to improve overall system performance. We present a smart load balancer, which leverages the knowledge of burstiness to predict the changes in user demands and on-the-fly shifts between the schemes that are "greedy" (i.e., always select the best site) and "random" (i.e., randomly select one) based on the predicted information. Both simulation and real experimental results show that this new load balancer can adapt quickly to the changes in user demands and thus improve performance by making a smart site selection for cloud users under both bursty and non-bursty workloads.

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Section: B Online Versionmentioning

confidence: 84%

Section: B Online Versionmentioning

confidence: 99%

Section: B Online Versionmentioning

confidence: 99%

Section: B Online Versionmentioning

confidence: 99%

See 2 more Smart Citations

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

Tai

Zhang

et al. 2011

30th IEEE International Performance Computing and Communications Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…Workloads with bursty session arrivals will have very large values for I a in the order of hundreds or thousands to indicate a clear deviation from the Poisson process. Caniff et al presented evidence of burstiness in real-life workloads [17]. In particular, the authors showed that the classic FIFA World Cup trace of web request arrivals has an index of dispersion value of 8400.…”

Section: Workload Scenario Parametersmentioning

confidence: 99%

Burstiness-aware service level planning for enterprise application clouds

Youssef

Krishnamurthy

2017

J Cloud Comp

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Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber's applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers' SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads.

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Context‐aware resource management and alternative pricing model to improve enterprise cloud adoption

Balaji

Kumar

2020

Concurrency and Computation

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Existing Cloud resource management approaches favor Cloud service providers because of their data-center origin. The onus of efficiently managing the resources is on the end-user. Present provisioning/deprovisioning methods and the pricing model could be modified to ensure that the end-user reaps the benefits. The current study proposes a context-aware Cloud resource management approach based on a workload-type provisioning and a just-in-time deprovisioning design. Furthermore, it also recommends an alternative pricing model. The design intends to provision/deprovision individual resources like CPU, memory, and so on, according to the workload-type rather than instance-type as done in existing Cloud resource management approaches. The proposed approach was commissioned on a private lab infrastructure and several experiments were conducted. It was observed that the proposed approach was able to detect sample workload types with an average sensitivity of 82% and specificity of 76%. The average cost of the proposed approach was found to be 20% less than the present approach. K E Y W O R D S alternative cloud pricing, cloud resource management, cloud scaling, just-in-time deprovisioning 1 INTRODUCTION Cloud computing is an evolving paradigm and end-users could benefit more from this model if the supporting technologies like resource management and pricing provide more options. Existing resource management approaches are either rule-based and reactive (subsequently referred to as the traditional approach) or predictive. 1-4 The authors, in their earlier study, observed that provisioning was better using the predictive approach than the traditional approach. 5,6 Enterprise workloads are complex and nonstationary due to large, concurrent, and dependent applications that get executed in parallel or sequence. Simultaneous requests from such applications in a short time-period result in burst/spike, leading to resource management challenges. 7-9 An efficient Cloud resource management framework needs to classify the user request into workload type (WT) to offset this uncertainty. The WT of a user-request prescribes the resources required for fulfilling the demand. For example, a compute-intensive on-off WT would rather require additional CPU than storage, while a read-write-intensive exponential WT would require more disk IOPS than CPU. Earlier studies report a measure of self-similarity as a proficient approach for identifying anomalies that could be leveraged for WT classification. 10-12 It must be noted that existing Cloud resource management approaches do not include WT while provisioning resources. Currently, Cloud resources such as CPU, memory, disk, and network are grouped into logical units called instance-types (virtual machines) for provisioning/deprovisioning. Although the demand for individual resources like CPU, memory, and so on, is a frequent occurrence in enterprise Cloud applications, the only choice for the end-user is to make use of instance-types. An instance-type-based approach would make it is easier f...

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Fastrack for taming burstiness and saving power in multi-tiered systems

Abstract: Burstiness (i.e., sudden surges)

Cited by 17 publications

References 14 publications

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads

Burstiness-aware service level planning for enterprise application clouds

Context‐aware resource management and alternative pricing model to improve enterprise cloud adoption

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