Learning Predictive Autoscaling Policies for Cloud-Hosted Microservices Using Trace-Driven Modeling

Abdullah, Muhammad; Iqbal, Waheed; Erradi, Abdelkarim; Bukhari, Faisal

doi:10.1109/cloudcom.2019.00028

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…As an example, the work proposed in [35] aims to model the response time of microservices based on stress testing and concurrently collecting performance traces for predefined intervals to learn a predictive auto-scaling model, such that the response time requirements are satisfied. Those performance traces are used to learn the resource provisioning policy model using a regression analysis approach.…”

Section: Analytical Performance Modeling Approachesmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan

Taheri

Al-Dulaimy

et al. 2023

IEEE Trans. Cloud Comput.

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Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.

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Section: Analytical Performance Modeling Approachesmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan

Taheri

Al-Dulaimy

et al. 2023

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…As an example, the work proposed in [34] aims to model the response time of microservices based on stress testing and concurrently collecting performance traces for predefined intervals to learn a predictive auto-scaling model, such that the response time requirements are satisfied. Those performance traces are used to learn the resource provisioning policy model using a regression analysis approach.…”

Section: Analytical Performance Modeling Approachesmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan,

Taheri,

Al-Dulaimy

et al. 2021

Preprint

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Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.

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“…The author uses a fuzzy-based system to predict the future resources which are needed to maintain the application performance. A recent work by Abdullah et al [36] proposed a predictive autoscaling method using a machine learning to identify the required number of resources for satisfying the response time requirements using a forecasted workload and adjust the resources accordingly.…”

Section: Related Workmentioning

confidence: 99%

“…The React scale-out the application resources whenever the response time of the application increase from the user-defined threshold and the React scale-in the application resources whenever the response time of the last three time interval of the application decreases from the half of the user-defined threshold. The Predict [36] autoscaling method is a recent work that uses a predictive resources provisioning model to identify the required number of resources to satisfy the response time requirements using a forecasted workload and adjust the resources accordingly.…”

Section: Baseline Autoscalingmentioning

confidence: 99%

See 1 more Smart Citation

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

Self Cite

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Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method. INDEX TERMS Autoscaling, predictive, SLO violations, response time, workload, burstiness, online burst detection.

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Learning Predictive Autoscaling Policies for Cloud-Hosted Microservices Using Trace-Driven Modeling

Cited by 14 publications

References 16 publications

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

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