Online Spike Detection in Cloud Workloads

Mehta, Amardeep; Dürango, Jonas; Tordsson, Johan; Elmroth, Erik

doi:10.1109/ic2e.2015.50

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…This means that our prediction model cannot easily predict sudden and sharp increases of the load (i.e., load bursts). This issue is out of scope of this paper, but it can be addressed by focusing on load burst detection techniques ( [29][30][31]). Thus, an interesting area of future work is combining load burst detection techniques with load prediction techniques to deal with a large variety of cloud load patterns.…”

Section: Resultsmentioning

confidence: 99%

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

2017

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Dynamic workloads in cloud computing can be managed through live migration of virtual machines from overloaded or underloaded hosts to other hosts to save energy and/or mitigate performance-related Service Level Agreement (SLA) violations. The challenging issue is how to detect when a host is overloaded to initiate live migration actions in time. In this paper, a new approach to make long-term predictions of resource demands of virtual machines for host overload detection is presented. To take into account the uncertainty of long-term predictions, a probability distribution model of the prediction error is built. Based on the probability distribution of the prediction error, a decision-theoretic approach is proposed to make live migration decision that take into account live migration overheads. Experimental results using the CloudSim simulator and PlanetLab workloads show that the proposed approach achieves better performance and higher stability compared to other approaches that do not take into account the uncertainty of long-term predictions and the live migration overhead.

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Section: Resultsmentioning

confidence: 99%

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

2017

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“…36,37 Hence, a combination of hurst exponent and sample entropy metric was used in PRMF to detect and classify the input WT into stationary and nonstationary. 6 This approach was observed to be better than the traditional approach for reducing user errors and handling random burst/spikes.…”

Section: Categorize User-request Based On Wtmentioning

confidence: 96%

“…Enterprise blueprint workloads for Cloud resource management were used to simulate input workloads. 6,26,[41][42][43][44][45][46] Different experiments were conducted using a sequence of user actions scripted in HP JMeter. About 180 experimental runs were conducted in total.…”

Section: Workload Simulation and Experimentsmentioning

confidence: 99%

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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“…Indeed, unanticipated changes in workload characteristics can potentially lead to service slowdown and might end in service‐failure because of insufficient resource allocation. In the study of Mehta et al, the authors investigate methods for detecting spikes in cloud workloads. In particular, they developed methods that make use of signal processing techniques.…”

Section: Related Workmentioning

confidence: 99%

Computational resource management for data‐driven applications with deadline constraints

Tolosana-Calasanz

Diaz-Montes

Rana

et al. 2016

Concurrency and Computation

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Summary Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user‐defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud‐based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints.

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Online Spike Detection in Cloud Workloads

Cited by 8 publications

References 16 publications

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

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

Computational resource management for data‐driven applications with deadline constraints

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