Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters

Shen, Siqi; Beek, Vincent van; Iosup, Alexandru

doi:10.1109/ccgrid.2015.60

Cited by 221 publications

(146 citation statements)

References 27 publications

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“…Due to the tight dependency between the nature of the applications and the techniques to be applied, in this paper we consider business-critical applications [32] and, in particular, virtualized banking applications executing batches of tasks. To characterize the power and performance of these applications, we make use of synthetic workloads which are representative of real banking applications according to our industry partners.…”

Section: Applications Descriptionmentioning

confidence: 99%

“…To characterize the power and performance of these applications, we make use of synthetic workloads which are representative of real banking applications according to our industry partners. As realistic CPU usage and memory footprint traces, we use the publicly available traces from Bitbrains, a service provider that provides service to banks such as ING [32]. Bitbrains traces provide data every 5 minutes.…”

Section: Applications Descriptionmentioning

confidence: 99%

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Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Pahlevan

Zapater

et al. 2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper we propose a heuristicand a machine learning (ML)-based VM allocation method and compare them in terms of energy, quality of service (QoS), network traffic, migrations, and scalability for various DC scenarios. Then, we present a novel hyper-heuristic algorithm that exploits the benefits of both methods by dynamically finding the best algorithm, according to a user-defined metric. For optimality assessment, we formulate an integer linear programming (ILP)-based VM allocation method to minimize energy consumption and data communication, which obtains optimal results, but is impractical at runtime. Our results demonstrate that the ML approach provides up to 24% server-to-server network traffic improvement and reduces execution time by up to 480x compared to conventional approaches, for large-scale scenarios. On the contrary, the heuristic outperforms the ML method in terms of energy and network traffic for reduced scenarios. We also show that the heuristic and ML approaches have up to 6% energy consumption overhead compared to ILP-based optimal solution. Our hyper-heuristic integrates the strengths of both the heuristic and the ML methods by selecting the best one during runtime.

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Section: Applications Descriptionmentioning

confidence: 99%

Section: Applications Descriptionmentioning

confidence: 99%

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Pahlevan

Zapater

et al. 2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…a normal distribution). For instance, Figure 7 shows how such monthly distribution functions changed for two service providers, Bitbrains [56] and Materna [34,35], during three consecutive months. Although both providers serve business-critical applications for enterprise customers, distributions of their CPU utilization are quite diferent.…”

Section: Determining Resource Distributionmentioning

confidence: 99%

“…These numerical analyses allow fast design space exploration and are complementary to the actual evaluation of the implemented prototype in Section 6. We use the Bitbrains [56] performance traces in this section. Results are attained under ixed resource price (p b = $0.05 and p d = $0.07 per VM-Hour [2]) throughout the simulation unless otherwise speciied.…”

Section: Numerical Evaluation Of Incentivesmentioning

confidence: 99%

Incentivizing self-capping to increase cloud utilization

Shahrad

Klein

Zheng

et al. 2017

Proceedings of the 2017 Symposium on Cloud Computing

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Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs. Higher utilization not only can enable higher proit for IaaS providers but also provides a mechanism to raise energy eiciency; therefore creating greener cloud services. Unfortunately, achieving high utilization is diicult mainly due to infrastructure providers needing to maintain spare capacity to service demand luctuations.Graceful degradation is a self-adaptation technique originally designed for constructing robust services that survive resource shortages. Previous work has shown that graceful degradation can also be used to improve resource utilization in the cloud by absorbing demand luctuations and reducing spare capacity. In this work, we build a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation. By using graceful degradation with an appropriate pricing model, the infrastructure provider can realize higher resource utilization while simultaneously, its tenants can increase their proit. Our proposed solution is based on a hybrid model which guarantees both reserved and peak on-demand capacities over lexible periods. It also includes a global dynamic price pair for capacity which remains uniform during each tenant's Service Level Agreement (SLA) term.We evaluate our scheme using simulations based on real-world traces and also implement a prototype using RUBiS on the Xen hypervisor as an end-to-end demonstration. Our analysis shows that the proposed scheme never hurts a tenant's net proit, but can improve it by as much as 93%. Simultaneously, it can also improve the efective utilization of contracts from 42% to as high as 99%.

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“…These VMs perform batch financial analysis, mainly based on matrix multiplication and manipulation, and both their CPU and memory utilization can be tuned. To consider representative values for memory usage, we use a dataset from Bitbrains [17] containing the performance traces of 1750 VMs. Based on this traces, we obtain statistics about memory utilization.…”

Section: Design Space Explorationmentioning

confidence: 99%

Towards Near-Threshold Server Processors

Pahlevan¹,

Picorel²,

Pourhabibi³

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

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Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters

Cited by 221 publications

References 27 publications

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Incentivizing self-capping to increase cloud utilization

Towards Near-Threshold Server Processors

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