Auto-scaling microservices on IaaS under SLA with cost-effective framework

Prachitmutita, Issaret; Aittinonmongkol, Wachirawit; Pojjanasuksakul, Nasoret; Supattatham, Montri; Padungweang, Praisan

doi:10.1109/icaci.2018.8377525

Cited by 36 publications

(26 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Prachitmutita et al (2018) proposed a new self-scaling framework based on the predicted workload, with an artificial neural network, a recurrent neural network, and a resource scaling optimization algorithm used to create an automated system to manage the entire application with Infrastructure-as-a-service (IaaS) ( Prachitmutita et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

“…Then, this service architecture is used to predict the future workload for making decisions about resource provisioning (Alipour & Liu, 2017). Prachitmutita et al (2018) proposed a new self-scaling framework based on the predicted workload, with an artificial neural network, a recurrent neural network, and a resource scaling optimization algorithm used to create an automated system to manage the entire application with Infrastructure-as-a-service (IaaS) (Prachitmutita et al, 2018). Ma et al (2018) proposed an approach, called scenario-based microservice retrieval (SMSR), to recommend appropriate microservices for users based on the Behavior-driven Development (BDD) test scenarios written by the user.…”

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

See 1 more Smart Citation

Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Background Microservices are an architectural approach of growing use, and the optimal granularity of a microservice directly affects the application’s quality attributes and usage of computational resources. Determining microservice granularity is an open research topic. Methodology We conducted a systematic literature review to analyze literature that addresses the definition of microservice granularity. We searched in IEEE Xplore, ACM Digital Library and Scopus. The research questions were: Which approaches have been proposed to define microservice granularity and determine the microservices’ size? Which metrics are used to evaluate microservice granularity? Which quality attributes are addressed when researching microservice granularity? Results We found 326 papers and selected 29 after applying inclusion and exclusion criteria. The quality attributes most often addressed are runtime properties (e.g., scalability and performance), not development properties (e.g., maintainability). Most proposed metrics were about the product, both static (coupling, cohesion, complexity, source code) and runtime (performance, and usage of computational resources), and a few were about the development team and process. The most used techniques for defining microservices granularity were machine learning (clustering), semantic similarity, genetic programming, and domain engineering. Most papers were concerned with migration from monoliths to microservices; and a few addressed green-field development, but none address improvement of granularity in existing microservice-based systems. Conclusions Methodologically speaking, microservice granularity research is at a Wild West stage: no standard definition, no clear development—operation trade-offs, and scarce conceptual reuse (e.g., few methods seem applicable or replicable in projects other than their initial proposal). These gaps in granularity research offer clear options to investigate on continuous improvement of the development and operation of microservice-based systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…This approach works for the applications with predictive and steady loads, but the disadvantage with this model is that it cannot meet the needs of a dynamic application loads. (4) The newer and advanced ways of auto scaling let the scaling process be controlled by parameters [8] [9]. This dynamic scaling technique helps to auto scale in response to fluctuating conditions, particularly when one cannot predict when the load fluctuations can occur.…”

Section: A Scaling Optionsmentioning

confidence: 99%

“…Using these values, the resource utilization at any point of time T can be calculated as per equation (7). To calculate the total resource utilization, we used equation (9) based on Reiman sum, by splitting the time intervals as 10,20,30,40,50 and 60 seconds. This gave us a total CPU utilization of 392% approximately.…”

Section: Figure 6 Cpu Utilization Trend For Large Functionmentioning

confidence: 99%

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

Rudrabhatla¹

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Traditional approaches (e.g., overprovisioning [32,80], re- current provisioning [49,62], and autoscaling [35,51,61,75,78,81,117]) reduce SLO violations by allocating more CPUs and memory to microservice instances by using performance models, handcrafted heuristics (i.e., static policies), or machinelearning algorithms.…”

Section: Introductionmentioning

confidence: 99%

FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices

Qiu¹,

Banerjee²,

Jha³

et al. 2020

Preprint

View full text Add to dashboard Cite

Modern user-facing, latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing compute-resources across microservices is still challenging in production because contention for shared resources can cause latency spikes that violate the service-level objectives (SLOs) of user requests. This paper presents FIRM, an intelligent fine-grained resource management framework for predictable sharing of resources across microservices to drive up overall utilization. FIRM leverages online telemetry data and machine-learning methods to adaptively (a) detect/localize microservices that cause SLO-violations, (b) identify low-level resources in contention, and (c) take actions to mitigate SLO-violations by dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up to 16.7× while reducing the overall requested CPU limit by up to 62.3%. Moreover, FIRM improves performance predictability by reducing tail latencies by up to 11.5×.

show abstract

Auto-scaling microservices on IaaS under SLA with cost-effective framework

Cited by 36 publications

References 10 publications

Defining and measuring microservice granularity—a literature overview

Defining and measuring microservice granularity—a literature overview

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

FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices

Contact Info

Product

Resources

About