CloudScale

Shen, Zhiming; Subbiah, Sethuraman; Gu, Xiaohui; Wilkes, John

doi:10.1145/2038916.2038921

Cited by 443 publications

(40 citation statements)

References 25 publications

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“…However, this approach requires a long history of 10 to 20 minutes to generate a new model. Shen et al [2011] focus on methods for fast corrections against under-provisioning errors. They present an approach which combines online adaptive padding based on burst detection with additional allocation corrections using feedback from SLO violations and relative utilisation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Kalyvianaki

Charalambous

Hand

2014

ACM Trans. Auton. Adapt. Syst.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Resource management of virtualized servers in data-centres has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multi-tier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done towards adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high intensity workload changes. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark web-site deployed on a prototype Xen-virtualized cluster. Permanent repository link

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Section: Related Workmentioning

confidence: 99%

Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters

Kalyvianaki

Charalambous

Hand

2014

ACM Trans. Auton. Adapt. Syst.

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“…By exploiting performance feedback, AutoPro can realize a soft partitioning of unmanaged resources by adjusting the allocation of the bottleneck resource [Fedorova et al 2007;Sironi et al 2012]. Nonetheless, limiting the contention on unmanaged resources is still valuable, since this way of attacking degradation is inefficient and can complicate the billing schema (see Section 6).…”

Section: Analysis Of the Case Studymentioning

confidence: 99%

“…AutoPro requires each SLO-bound VM to make periodic performance reports available to its controller, in order to leverage its resource-performance models, as proposed in previous works [Zhang et al 2002;Padala et al 2009;Shen et al 2011;Sironi et al 2012;Bartolini et al 2013a;Hoffmann et al 2013;Sironi et al 2014]. Any performance metric meaningful to the user can be used for these reports and to express SLOs; for instance, a web server can use throughput (e.g., requests/s for a web server) or latency (i.e., response time).…”

Section: Performance Metrics and Measurementsmentioning

confidence: 99%

“…Other contributions [Nathuji et al 2010;Padala et al 2009;Shen et al 2011] describe systems that automate resource allocation based on SLOs but work at a coarse scale and granularity (see Section 5). In contrast, with AutoPro, we explore the possibility of enhancing IaaS clouds with automated, fast, and fine-grained resource allocation to meet users' SLOs, while allowing providers to safely share host nodes among VMs to maximize node-level utilization and reduce TCO.…”

Section: Introductionmentioning

confidence: 99%

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Automated Fine-Grained CPU Provisioning for Virtual Machines

Bartolini

Sironi

Sciuto

et al. 2014

ACM Trans. Archit. Code Optim.

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Ideally, the pay-as-you-go model of Infrastructure as a Service (IaaS) clouds should enable users to rent just enough resources (e.g., CPU or memory bandwidth) to fulfill their service level objectives (SLOs). Achieving this goal is hard on current IaaS offers, which require users to explicitly specify the amount of resources to reserve; this requirement is nontrivial for users, because estimating the amount of resources needed to attain application-level SLOs is often complex, especially when resources are virtualized and the service provider colocates virtual machines (VMs) on host nodes. For this reason, users who deploy VMs subject to SLOs are usually prone to overprovisioning resources, thus resulting in inflated business costs.This article tackles this issue with AutoPro: a runtime system that enhances IaaS clouds with automated and fine-grained resource provisioning based on performance SLOs. Our main contribution with AutoPro is filling the gap between application-level performance SLOs and allocation of a contended resource, without requiring explicit reservations from users. In this article, we focus on CPU bandwidth allocation to throughput-driven, compute-intensive multithreaded applications colocated on a multicore processor; we show that a theoretically sound, yet simple, control strategy can enable automated fine-grained allocation of this contended resource, without the need for offline profiling. Additionally, AutoPro helps service providers optimize infrastructure utilization by provisioning idle resources to best-effort workloads, so as to maximize node-level utilization.Our extensive experimental evaluation confirms that AutoPro is able to automatically determine and enforce allocations to meet performance SLOs while maximizing node-level utilization by supporting batch workloads on a best-effort basis.

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“…Models are constructed on demand to reflect the runtime conditions and then analyzed at runtime to prepare dynamic system reconfiguration. An example runtime optimization at this layer is the autoscaling feature of cloud service offerings [42].…”

Section: Middleware and Paas Layermentioning

confidence: 99%