Evaluation of cloud autoscaling strategies under different incoming workload patterns

Calzarossa, Maria Carla; Massari, Luisa; Tessera, Daniele

doi:10.1002/cpe.5667

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Few other researchers have used techniques like neural networks and machine learning for predicting workloads on existing services [25] [29]. Few researchers also investigated the impact on performance using the reactive autoscaling policies and configurations [26] [27]. Some researchers also used fog computing techniques based on to develop a stochastic performance model for capacity planning using Markovian chain model [28].…”

Section: Related Workmentioning

confidence: 99%

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

Rudrabhatla¹

2020

IEEE Access

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Microservice architecture (MSA) has become a de facto standard for developing complex web applications lately. Horizontal scalability, domain isolation, agility and the provision to use heterogenous technologies are some of the key factors for the growing popularity of this architecture. To automatically cater to varying load patterns, quite a lot of advancements have been made in the field of cloud computing, containerization and orchestrating mechanisms which aid to perform the auto scaling of the microservices. However, setting up the scaling policies, optimal upper and lower thresholds is a daunting task for large applications. It generally involves some initial guess work followed by multiple rounds of tuning based on the real time load variations. This process causes situations where either the service becomes unavailable to the load when the thresholds are on the lower side, (or) underutilization of the compute resources when they are on higher side. This paper aims to find a quantitative way of determining the thresholds and step-up policies by deducing the mathematical formulas. To solve this formidable problem, we propose a model in which the total resource consumption of a container running in the peak load scenario can be calculated by-(1) first identifying the critical transactions and their maximum concurrency rates,(2) then calculating the resource consumption of such transactions in a controlled environment and (3) finally applying those values to the mathematical formulas based on Gaussian functions to calculate the total resource consumption for the peak load scenario. Using the total resource consumption value and considering the network and startup latencies, an optimal upper threshold value for step-up functions can be calculated. In this paper, we calculated the upper threshold values using the above-mentioned approach and verified using a research project that the calculated value is indeed the minimum number of containers to handle load.

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Section: Related Workmentioning

confidence: 99%

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

Rudrabhatla¹

2020

IEEE Access

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Cloud computing, IoT, and big data: Technologies and applications

Bakhouya

Zbakh

Essaaidi

et al. 2020

Concurrency and Computation

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Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

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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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Evaluation of cloud autoscaling strategies under different incoming workload patterns

Cited by 6 publications

References 32 publications

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

A Quantitative Approach for Estimating the Scaling Thresholds and Step Policies in a Distributed Microservice Architecture

Cloud computing, IoT, and big data: Technologies and applications

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

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