A scalable routing mechanism for stateful microservices

H., Nam; Do, Tien Van; Tran, Xuan Thi; Farkas, Lóránt; Rotter, Csaba

doi:10.1109/icin.2017.7899252

Cited by 6 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fine-grained approach to load balancing is to optimize service instance selection for each task at runtime, as discussed in this paper. Do et al [11] proposed a scalable routing mechanism for applications designed according to the microservices architecture. This approach is demonstrated through the design and implementation of an application according to Microservices Architecture (MSA), thus providing cloud resource reservation with some stateful services for private or enterprise users.…”

Section: Related Workmentioning

confidence: 99%

TCLBM: A task chain-based load balancing algorithm for microservices

Liang

Lan

2021

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture. Scalability is one of the most attractive features of microservices. Scaling in the microservices architecture requires the scaling of specified services only, rather than the entire application. Scaling services can be achieved by deploying the same service multiple times on different physical machines. However, problems with load balancing may arise. Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks. In the present paper, we propose TCLBM, a task chainbased load balancing algorithm for microservices. When an Application Programming Interface (API) request is received, TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance. TCLBM reduces the API response time by reducing data transmissions between physical machines. We use three heuristic algorithms, namely, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA), to implement TCLBM, and comparison results reveal that GA performs best. Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10% compared with existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

TCLBM: A task chain-based load balancing algorithm for microservices

Liang

Lan

2021

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Current work focuses on application-specific benchmarks. To name a few, there are benchmarks which evaluate database or storage systems (e.g., Bermbach et al, 2014 ; Cooper et al, 2010 ; Bermbach et al, 2017 ; Kuhlenkamp, Klems & Röss, 2014 ; Müller et al, 2014 ; Pallas et al, 2017 ; Pallas, Günther & Bermbach, 2017 ; Pelkonen et al, 2015 ; Difallah et al, 2013 ), benchmark microservices ( Villamizar et al, 2015 ; Grambow et al, 2020 ; Grambow, Wittern & Bermbach, 2020 ; Ueda, Nakaike & Ohara, 2016 ; Do et al, 2017 ), determine the quality of web APIs ( Bermbach & Wittern, 2016 , 2020 ), specifically tackle web sites ( Menascé, 2002 ), or evaluate other large-scale software systems (e.g., Jiang & Hassan, 2015 ; Hasenburg et al, 2020 ; Hasenburg & Bermbach, 2020 ). Our approach can use all of these application benchmarks as a baseline.…”

Section: Related Workmentioning

confidence: 99%

Using application benchmark call graphs to quantify and improve the practical relevance of microbenchmark suites

Grambow

Laaber

Leitner

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to existing build pipelines to ensure performance goals. In this paper, we show how the practical relevance of microbenchmark suites can be improved and verified based on the application flow during an application benchmark run. We propose an approach to determine the overlap of common function calls between application and microbenchmarks, describe a method which identifies redundant microbenchmarks, and present a recommendation algorithm which reveals relevant functions that are not covered by microbenchmarks yet. A microbenchmark suite optimized in this way can easily test all functions determined to be relevant by application benchmarks after every code change, thus, significantly reducing the risk of undetected performance problems. Our evaluation using two time series databases shows that, depending on the specific application scenario, application benchmarks cover different functions of the system under test. Their respective microbenchmark suites cover between 35.62% and 66.29% of the functions called during the application benchmark, offering substantial room for improvement. Through two use cases—removing redundancies in the microbenchmark suite and recommendation of yet uncovered functions—we decrease the total number of microbenchmarks and increase the practical relevance of both suites. Removing redundancies can significantly reduce the number of microbenchmarks (and thus the execution time as well) to ~10% and ~23% of the original microbenchmark suites, whereas recommendation identifies up to 26 and 14 newly, uncovered functions to benchmark to improve the relevance. By utilizing the differences and synergies of application benchmarks and microbenchmarks, our approach potentially enables effective software performance assurance with performance tests of multiple granularities.

show abstract

“…An enterprise application which is designed for a particular organization consists of different microservices which are responsible to communicate with one another with the help of a light-weight protocol and the API contract [2]. MSA design is generally preferred to the conventional Monolithic Architecture due to the fact that it can be continuously deployed and its scalability has no parallel while the conventional Monolithic Architecture lacks all these important features.…”

Section: A Microservicesmentioning

confidence: 99%

“…Nowadays, in any system, the scalability, service discovery and communication among services that are being supported by microservices architecture in development phase are two important sections [45]. Simultaneously, microservices architecture also handles a heavy concurrency during input load [2]. In fact, the purpose for using microservices is that it works on latest platform and is independently deployable [1].…”

Section: A Microservicesmentioning

confidence: 99%

See 1 more Smart Citation

Matrix Clustering based Migration of System Application to Microservices Architecture

Ghayyur¹,

Razzaq²,

Ullah³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Abstract-A microservice architecture (MSA) style is an emerging approach which is gaining strength with the passage of time. Micro services are recommended by a number of researchers to overcome the limitations and issues encountered by usage of aging method of monolithic architecture styles. Previously the monolithic applications cannot be decomposed into smaller and different services. Monolithic styles application was the one build application. The issue resolution has the focus on lightweight independent application services in the form of sizable services, self-contained units with primary focus on maintenance, performance, scalability, and online services eliminating dependency. All quality factors have been thoroughly discussed in literature, system application migration is becoming an emerging issue with different challenges. This study is addressing the tight coupling to reducing this issue. Moreover, this literature review indicates some complex problems about the migration or conversion of system application into microservice. In architecture, dependency is a big challenge and issue in recent technology. Microservices are recommended by a number of researchers to overcome the limitations issue about how to migrate the existing system application to microservice. The need for a systematic mapping is essential in order to recap the improvement and identify the gaps and requirements for future studies. This study shows open issues first, new findings of quality attributes of microservices and then this study helps to understand the difference between previous traditional systems and microservices based systems. This research study creates awareness about system migration to microservices.

show abstract

A scalable routing mechanism for stateful microservices

Cited by 6 publications

References 7 publications

TCLBM: A task chain-based load balancing algorithm for microservices

TCLBM: A task chain-based load balancing algorithm for microservices

Using application benchmark call graphs to quantify and improve the practical relevance of microbenchmark suites

Matrix Clustering based Migration of System Application to Microservices Architecture

Contact Info

Product

Resources

About