HyScale: Hybrid and Network Scaling of Dockerized Microservices in Cloud Data Centres

Kwan, Anthony; Wong, Jonathon; Jacobsen, Hans‐Arno; Muthusamy, Vinod

doi:10.1109/icdcs.2019.00017

Cited by 45 publications

(21 citation statements)

References 17 publications

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“…Scalability is the property of a system to handle a growing amount of work by adding resources to the system [55]. The manner in which additional resources are added defines which of two scaling approaches is taken [8], [14], [56], [57] vertical scaling or horizontal scaling. The former, also known as scaling up, refers to adding more resources (CPU, memory, and storage) to an existing machine.…”

Section: Vertical and Horizontal Scalingmentioning

confidence: 99%

“…We narrowed the performance assessment to measuring the throughput. Although throughput is the predominant performance metric, other metrics, such as response time (latency) and CPU utilization, have also been used in prior studies [4], [14], [17], [18], [42], [70]. Our motivation was that throughput had also been commonly used as the scalability measure [18], while in this study, we were also interested in comparing the scalability of both architectural styles.…”

Section: A Construct Validitymentioning

confidence: 99%

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Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

2022

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Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C# .NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to .NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C# .NET) does not have a noticeable impact on the scalability performance.

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Section: Vertical and Horizontal Scalingmentioning

confidence: 99%

Section: A Construct Validitymentioning

confidence: 99%

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

2022

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“…• Using vertical and horizontal scaling in the same method: Hybrid scaling techniques reap the benefits of both the fine-grained resource control of vertical scaling and the high availability of horizontal scaling. This makes hybrid scaling a promising solution for effective autoscaling [Kwan et al 2019]. When proposing a generic method where there is no previous knowledge about the application, it is important to analyze which of the types of autoscaling would be most suitable for resource optimization.…”

Section: Types Of Microservice Autoscalingmentioning

confidence: 99%

State of the Art on Microservices Autoscaling: An Overview

Nunes¹,

Bianchi²,

Iwasaki³

et al. 2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

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The adoption of microservices architecture has taken on great pro-portions due to its benefits and popularization of containers driven tools, such as Kubernetes and Docker. Besides, the development of microservice-based applications is a complex task, specially because they can be composed of multiple heterogeneous parts. In particular, one of the main challenges is how to conduct the microservices autoscaling (i.e., adding or removing resources on demand), while still avoiding resource waste, such as CPU and memory. This paper presents the state of the art of approaches to solve the problem of micro services autoscaling, the main characteristics to be considered as well as the important future directions that need to be still investigated.

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“…Throughput is maximum output at a certain time for a specific hardware type. It is performance measure of basic resources such as RAM, Hard drive, and CPU in a shared resources pool [28]. The throughput calculated as T t = I t /t According to the statistics shown below diagram, T t presents the maximum output of a certain resource at a specific time.…”

Section: A Throughputmentioning

confidence: 99%

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Hussain¹,

He²,

Zhu³

et al. 2021

IJACSA

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Multi-cloud is a vendor-based heterogeneous cloud paradigm in recent era of computing with dynamic infrastructural deployment. Multi-cloud provides all the essential and on-demand requirements of a virtual environment from various domains under a single service level agreement (SLA). Consumers from multitier domains can access all the available resources placed in a shared pool on service provider's side, as per their requirement. The shared pool of resources creates complexity in assigning the best and suitable resource to a particular virtual instance under the same services provider end. The complexity of resources in terms of accessibility from the various domains, dynamic allocation, security, and quality of services (QoS) raises concerns in the multi-cloud infrastructure. This complexity raise concern relates to optimal provisioning and cost management. In the proposed work a hybrid technique with a shuffled leapfrog algorithm and ubiquitous binary search (SLFA-UBS) to resolve these issues with optimal provisioning, dynamic allocation and better resource selection. The proposed work will help to create a need-based and demand-based resource pool with the appropriate selection of each resource. The proposed model also supports resource optimization with dynamic provisioning, cost-effective solution to achieve QoS in multi-cloud deployment on service provider end.

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HyScale: Hybrid and Network Scaling of Dockerized Microservices in Cloud Data Centres

Cited by 45 publications

References 17 publications

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

State of the Art on Microservices Autoscaling: An Overview

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

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