Towards Recovering the Software Architecture of Microservice-Based Systems

Granchelli, Giona; Cardarelli, Mario; Francesco, Paolo Di; Malavolta, Ivano; Iovino, Ludovico; Salle, Amleto Di

doi:10.1109/icsaw.2017.48

Cited by 76 publications

(55 citation statements)

References 17 publications

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“…The MxNet deep learning framework [14] shows the feasibility of using serverless computing AWS Lambda framework [15]. There are several efforts [16]- [20] to deploy and orchestrate VMs or containers dynamically with all software dependencies. Barista's focus is orthogonal to these efforts; instead, it is to trigger the deployment process ahead of time so that the system can handle the workload surges by utilizing the afore-mentioned efforts.…”

Section: B Serverless Computingmentioning

confidence: 99%

“…GetForecast(t, t setup ) , i * , n_reqi * ← ResourceEstimation(y , params) ← GetExpireVMCount(t + t setup ) 12 δ ← (α − prevStepV M Count) − expireV M Count 13if δ > 0 then14 for i = 1 to δ do15 IP ←DeployVM(i * )16 AddContDwnldRegistry(t + tvm, IP, model)17 AddMdlLoadRegistry(t + tvm + t cd , IP, model)18 AddVMExpireRegistry(t + τvm, IP, model) 19 end 20 HorizontalScaleUp(sizeof (scaledV M s), scaledV M s) 21 else ← δ + sizeof (scaledV M s) 23 if δ ≥ 0 then 24 HorizontalScaleUp(δ , scaledV M s) ← CheckContDwnldRegistry(t)…”

mentioning

confidence: 99%

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BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Bhattacharjee

Chhokra

Kang

et al. 2019

2019 IEEE International Conference on Cloud Engineering (IC2E)

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Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have various deployment and management costs. To address these challenges, we present a distributed and scalable deep-learning prediction serving system called Barista and make the following contributions. First, we present a fast and effective methodology for forecasting workloads by identifying various trends. Second, we formulate an optimization problem to minimize the total cost incurred while ensuring bounded prediction latency with reasonable accuracy. Third, we propose an efficient heuristic to identify suitable compute resource configurations. Fourth, we propose an intelligent agent to allocate and manage the compute resources by horizontal and vertical scaling to maintain the required prediction latency. Finally, using representative real-world workloads for an urban transportation service, we demonstrate and validate the capabilities of Barista.

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Section: B Serverless Computingmentioning

confidence: 99%

mentioning

confidence: 99%

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Bhattacharjee

Chhokra

Kang

et al. 2019

2019 IEEE International Conference on Cloud Engineering (IC2E)

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“…If software architecture recovery is taken into account, it becomes clear that the prevailing techniques in this field are quite limited because all these techniques are based upon reverse engineering [5].…”

Section: E Need Of Systematic Mappingmentioning

confidence: 99%

“…It helps the developer to develop the large and complex application to distribute the application in chunks or unit by composing this application [1]. It can be written in language by using APIs for microservices [3]- [5]. Mostly papers are discussing about its independent services that can be upgrade or new addition or services any time.…”

Section: ) Motivation For Microservice Architecture (Rq3)mentioning

confidence: 99%

“…These services are known as a style of an architecture that develops an application as a set of small services independently [7]. Now, microservices are becoming very popular with cloud platform which is an emerging style in the context of application development due to its independency, scalability, flexibility, performance, and manageability [3], [5]. There is a lot of research in this area that needs to be address.…”

Section: E Need Of Systematic Mappingmentioning

confidence: 99%

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Matrix Clustering based Migration of System Application to Microservices Architecture

Ghayyur¹,

Razzaq²,

Ullah³

et al. 2018

ijacsa

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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.

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A systematic mapping study: The new age of software architecture from monolithic to microservice architecture—awareness and challenges

Razzaq

Ghayyur

2022

Comp Applic In Engineering

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Microservice architecture (MSA) is an emerging architectural style as an innovative approach that is growing in quality over time. Microservices are endorsed by various researchers to shatter the issues and limitations encountered by the use of aging approaches for monolithic legacy architecture styles. Nonetheless, there exists no state of the research exhaustive study on migration to MSA from monolithic architecture. MSA has made the need to design the development methods of software and styles of architectures that satisfy these demands. The design of software architecture is the ongoing rise of MSA to address the independent deployment, scalability, and maintenance requirements. In this article, a systematic mapping study (SMS) was performed, including the literature on the migration approaches published between 2010 and 2021 to evaluate the contemporary state of the art and practice of software architecting. This study outlines the awareness and importance of MSA. Seventy‐three studies were finally selected in this SMS. The following perspectives of this study are publication trends, main venues, the focus of research, migration approaches, migration challenges, successful factors after migration, and the potential for industrial adoption. We synthesize the data and give a summary of the state of the art. This study also provides future research directions and applications for adopting microservices.

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Towards Recovering the Software Architecture of Microservice-Based Systems

Cited by 76 publications

References 17 publications

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services

Matrix Clustering based Migration of System Application to Microservices Architecture

A systematic mapping study: The new age of software architecture from monolithic to microservice architecture—awareness and challenges

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