Using Quantile Regression for Reclaiming Unused Cloud Resources While Achieving SLA

Dartois, Jean-Emile; Knefati, Anas; Boukhobza, Jalil; Barais, Olivier

doi:10.1109/cloudcom2018.2018.00030

Cited by 15 publications

(28 citation statements)

References 28 publications

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“…Datrois et al [18] proposed a method that uses machine learning algorithms that predict the 24-h availability of resources at the host level. Their predicting method is based on the employment of quantile regression to ensure an elastic composition between the potential amount of resources to reclaim and to define unused resources.…”

Section: Machine Leaning Based Approachesmentioning

confidence: 99%

Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

Buriboev

Kang

et al. 2019

Applied Sciences

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The monitoring utilization and workloads of computer hardware components, such as CPU, RAM, bus, and storage, are an ideal way to evaluate the effectiveness of these components. In this paper, we surveyed the basic concepts, characteristics, and parameters of computer systems that determine system performance, and the types of models that provide adequate modeling of these systems. We investigated and developed the applied aspects of the theory of fuzzy sets’ principles and the Matlab environment tools for monitoring and evaluating the state of computing systems. The idea of the paper is to identify the state of the computer infrastructure by using the models of Mamdani and Sugeno FIS (fuzzy inference system) to evaluate the impact of RAM and storage on CPU performance. With this approach, we observed the behavior of computer infrastructure. The results are useful for understanding performance issues with regard to specific bottlenecks and determining the correlation of performance counters. Moreover, the model presents linguistic results. Hereafter, performance counter correlations will support the development of algorithms that can detect whether the performance of a given computer will be affected by a reasonable priority. The performance assertions derived from these approaches allow resource management policies to prevent performance degradation, and as a result, the infrastructure will be able to serve safely as expected. These methods can be applied across the entire spectrum of computer systems, from personal computers to large mainframes and supercomputers, including both centralized and distributed systems. We look forward to their continued use, as well as their improvement when it is necessary to evaluate future systems.

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Section: Machine Leaning Based Approachesmentioning

confidence: 99%

Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

Buriboev

Kang

et al. 2019

Applied Sciences

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“…To do so, the decision engine first verifies whether the fingerprint recognition model for the requested application is available. If not, it requests the Fingerprint Builder to generate one for this new application (2). Then, the Decision Engine chooses a suitable farmer that will be in charge of executing the customer application (3) [11].…”

Section: Methodsmentioning

confidence: 99%

“…A promising alternative for optimizing the cost of processing applications on Cloud infrastructures is to opportunistically exploit their allocated but momentarily unused computing resources [2]. Many platforms (e.g., BOINC [3], Condor [4]) enable the leveraging of these unused resources for a variety of purposes (e.g., scientific computing, big data) and business models (e.g., free, reward).…”

Section: Introductionmentioning

confidence: 99%

Tracking Application Fingerprint in a Trustless Cloud Environment for Sabotage Detection

Dartois¹,

Boukhobza

Barais³

2019

2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

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Companies are more and more inclined to use collaborative cloud resources when their maximum internal capacities are reached in order to minimize their TCO. The downside of using such a collaborative cloud, made of private clouds' unused resources, is that malicious resource providers may sabotage the correct execution of third-partyowned applications due to its uncontrolled nature. In this paper, we propose an approach that allows sabotage detection in a trustless environment. To do so, we designed a mechanism that (1) builds an application fingerprint considering a large set of resources usage (such as CPU, I/O, memory) in a trusted environment using random forest algorithm, and (2) an online remote fingerprint recognizer that monitors application execution and that makes it possible to detect unexpected application behavior. Our approach has been tested by building the fingerprint of 5 applications on trusted machines. When running these applications on untrusted machines (with either homogeneous, heterogeneous or unspecified hardware from the one that was used to build the model), the fingerprint recognizer was able to ascertain whether the execution of the application is correct or not with a median accuracy of about 98% for heterogeneous hardware and about 40% for the unspecified one.

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“…State-of-the-art studies have discussed [8] how to select the appropriate learning algorithm(s) to forecast time series with learning algorithms such as Autoregressive (AR), Integrated Moving Average (ARIMA) and other more complex algorithms such as Recurrent Neural Network (RNN), Support Vector Machine (SVM), Long short-term memory (LSTM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF). In a previous work [4] we have shown that quantile regression is a relevant approach to reclaim unused resources with SLA requirements. This work has shown that quantile regression may increase the amount of savings by up to 20% compared to traditional approaches.…”

Section: B Forecasting Buildermentioning

confidence: 99%

“…This over-provisioning increases the Total Cost of Ownership (TCO) for Cloud providers and results in a low average resource utilization. In a previous study [4], authors shown that the average CPU usage lies between 20% to 50% on several data centers.…”

Section: Introductionmentioning

confidence: 97%

Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources

Dartois

Ribeiro

Boukhobza

et al. 2019

2019 IEEE 12th International Conference on Cloud Computing (CLOUD)

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Cloud infrastructures are generally overprovisioned for handling load peaks and node failures. However, the drawback of this approach is that a large portion of data center resources remains unused. In this paper, we propose a framework that leverages unused resources of data centers, which are ephemeral by nature, to run MapReduce jobs. Our approach allows: i) to run efficiently Hadoop jobs on top of heterogeneous Cloud resources, thanks to our data placement strategy, ii) to predict accurately the volatility of ephemeral resources, thanks to the quantile regression method, and iii) for avoiding the interference between MapReduce jobs and co-resident workloads, thanks to our reactive QoS controller. We have extended Hadoop implementation with our framework and evaluated it with three different data center workloads. The experimental results show that our approach divides Hadoop job execution time by up to 7 when compared to the standard Hadoop implementation.

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Using Quantile Regression for Reclaiming Unused Cloud Resources While Achieving SLA

Cited by 15 publications

References 28 publications

Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

Tracking Application Fingerprint in a Trustless Cloud Environment for Sabotage Detection

Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources

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