Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field

Bauer, André; Herbst, Nikolas; Spinner, Simon; Ali-Eldin, Ahmed; Kounev, Samuel

doi:10.1109/tpds.2018.2870389

Cited by 47 publications

(47 citation statements)

References 35 publications

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“…We now present Chamulteon, a novel auto-scaling mechanism specifically designed to support the coordinated autoscaling of multi-service applications. Chamulteon is based on our original auto-scaler Chameleon [2], which is a hybrid proactive auto-scaler combining multiple different proactive methods coupled with a reactive fallback mechanism. The system consists of two independent cycles: (i) the reactive cycle that monitors the application and scales reactively in short intervals and (ii) the proactive cycle which predicts the demand at longer intervals for a set of future scaling intervals.…”

Section: The Chamulteon Approachmentioning

confidence: 99%

“…1) Forecasting Component [2]: To enable proactive decisions, the arrival rates for the next reconfiguration intervals are forecast. To minimize the forecasting overhead, this component is only called if an earlier forecast has no more predicted values for future arrival rates or a configurable drift between the forecast and the recent monitoring data is detected.…”

Section: A Redesign Of the Original Chameleonmentioning

confidence: 99%

“…Generally, auto-scalers can be distinguished based on whether they are capable of scaling applications composed of only one service or applications consisting of multiple services. Examples from the first category include Chameleon, which we proposed in our previous work [2], and the famous open-source auto-scalers React, Adapt, Hist, and Reg [3]- [6]. In contrast, popular auto-scalers from the second category, such as AutoMap, AGILE, and CloudScale [7]- [9], are closed-source.…”

Section: Introductionmentioning

confidence: 99%

“…Towards addressing the aforementioned questions, our contribution is four-fold: 1) We survey existing auto-scalers for applications consisting of multiple services and discuss challenges for such auto-scalers as well as their benchmarking (Section II). 2) We redesign our white-box auto-scaler Chameleon [2] to enable the coordinated scaling of applications with multiple services. That is, we introduce the components, the decision making logic and the conflict resolution introduced by proactive scaling coupled with a reactive fallback (Section III-A).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chamulteon: Coordinated Auto-Scaling of Micro-Services

Bauer

Lesch

Versluis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Self Cite

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Nowadays, in order to keep track of the fastchanging requirements of Internet applications, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources to the resource demand. The straightforward approach is to deploy a set of common and opensource single-service auto-scalers for each service independently. However, this deployment leads to problems such as bottleneckshifting and increased oscillations. Existing auto-scalers that scale applications consisting of multiple services are kept closed-source. To face these challenges, we first survey existing auto-scalers and highlight current challenges. Then, we introduce Chamulteon, a redesign of our previously introduced mechanism, which can scale applications consisting of multiple services in a coordinated manner. We evaluate Chamulteon against four different wellcited auto-scalers in four sets of measurement-based experiments where we use diverse environments (VM vs. Docker), real-world traces, and vary the scale of the demanded resources. Overall, Chamulteon achieves the best auto-scaling performance based on established user-oriented and endorsed elasticity metrics.

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Section: The Chamulteon Approachmentioning

confidence: 99%

Section: A Redesign Of the Original Chameleonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Chamulteon: Coordinated Auto-Scaling of Micro-Services

Bauer

Lesch

Versluis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Self Cite

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show abstract

“…It computes the response time per each tier. Author [10] proposed a hybrid auto-scaling technique. The queuing theory clubbed with the time series analysis technique.…”

Section: Queuingmentioning

confidence: 99%

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Singh

Gupta

Jyoti

et al. 2019

SCPE

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Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS). One of the key challenges for web application in cloud computing is auto-scaling. The auto-scaling in cloud computing is still in infancy and required detail investigation of taxonomy, approach and types of resources mapped to the current research. In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing. This survey supports the research community to find the requirements in auto-scaling techniques. We present a taxonomy of reviewed articles with parameters such as auto-scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric, etc. Based on the analysis, we proposed the new areas of research in this direction.

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Loads prediction and consolidation of virtual machines in cloud

Chen

Zhang

et al. 2023

Concurrency and Computation

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SummaryVirtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions.

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Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field

Cited by 47 publications

References 35 publications

Chamulteon: Coordinated Auto-Scaling of Micro-Services

Chamulteon: Coordinated Auto-Scaling of Micro-Services

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Loads prediction and consolidation of virtual machines in cloud

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