An orthogonal approach to reusable component discovery in cloud migration

Zhao, Junfeng; Zhou, Jian; Yang, Hongji; Liu, Shuai

doi:10.1109/cc.2015.7112036

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Then network capacity will surge. Strictly speaking, distributed antenna system is not a new idea, while combining with cloud computing at BBU pool and network virtualization technologies [68], C-RAN becomes a novel and impelling technology for future network architectures. C-RAN will be a necessary ingredient of future SGINs.…”

Section: Cloud Computing Based Applicationsmentioning

confidence: 99%

An overview of multi-antenna technologies for space-ground integrated networks

Gao¹,

Zhang²,

Liu

et al. 2016

Sci. China Inf. Sci.

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Physical layer security in multi-antenna cognitive heterogeneous cellular networks: a unified secrecy performance analysis SCIENCE CHINA Information Sciences 61, 022310 (2018); Design of multi-antenna relaying for OFDM in impulsive noise environment 数字通信和网络 5, 189 (2019); Development status and applied research on mimic technologies for space-ground integration information network SCIENTIA SINICA Informationis 49, 799 (2019); Deployment dynamics simulation and ground test of a large space hoop truss antenna reflector

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Section: Cloud Computing Based Applicationsmentioning

confidence: 99%

An overview of multi-antenna technologies for space-ground integrated networks

Gao¹,

Zhang²,

Liu

et al. 2016

Sci. China Inf. Sci.

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“…Current strategies for decomposing monolithic applications fall under static-or dynamic-analysis techniques, i.e., they typically compute module dependencies using static and/or dynamic analysis and apply clustering or evolutionary algorithms over these dependencies to create module partitions that have desired properties (e.g., high cohesion and low coupling). Static approaches [10,11,13,15,28,31,38,45,51] suffer imprecision in computing dependencies that is inherent to static analysis. In Java Enterprise Edition (JEE) applications, which are the focus of our work, these techniques face challenges in dealing with dynamic language features, such as reflection, dynamic class loading, context, and dependency injections.…”

Section: Introductionmentioning

confidence: 99%

Mono2Micro: A Practical and Effective Tool for Decomposing Monolithic Java Applications to Microservices

Kalia,

Xiao,

Krishna

et al. 2021

Preprint

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In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro [25] to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro [25] showed promising results.How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool's efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.

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“…The approaches presented in academic research broadly fall under static or dynamic techniques. Approaches based on static analysis (e.g., [2,3,5,10,11,14,16,17]) capture static call relations among class objects and, therefore, face challenges in dealing with dynamic features of Java Enterprise Edition (JEE) applications, such as reflection, dynamic class loading, and context and dependency injections. Dynamic techniques (e.g., [4,8,13]) capture runtime call relations and workload characteristics of an application; however the runtime traces do not capture alignment information about how classes and objects support different business functions which is a foremost concern in industry practices.…”

Section: Introductionmentioning

confidence: 99%

Mono2Micro: an AI-based toolchain for evolving monolithic enterprise applications to a microservice architecture

Kalia

Xiao

Lin

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Mono2Micro is an AI-based toolchain that provides recommendations for decomposing legacy web applications into microservice partitions. Mono2Micro consists of a set of tools that collect static and runtime information from a monolithic application and process the information using an AI-based technique to generate recommendations for partitioning the application classes. Each partition represents a candidate microservice or a grouping of classes with similar business functionalities. Mono2Micro takes a temporospatial clustering approach to compute meaningful and explainable partitions. It generates two types of partition recommendations. First, it computes business-logic-seams-based partitions that represent a desired encapsulation of business functionalities. However, such a recommendation may cut across data dependencies between classes, accommodating which could require significant application updates. To address this, Mono2Micro computes natural-seamsbased partitions, which respect data dependencies. We describe the set of tools that comprise Mono2Micro and illustrate them using a well-known open-source JEE application. CCS CONCEPTS • Software and its engineering → Software architectures; Software post-development issues.

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An orthogonal approach to reusable component discovery in cloud migration

Cited by 5 publications

References 30 publications

An overview of multi-antenna technologies for space-ground integrated networks

An overview of multi-antenna technologies for space-ground integrated networks

Mono2Micro: A Practical and Effective Tool for Decomposing Monolithic Java Applications to Microservices

Mono2Micro: an AI-based toolchain for evolving monolithic enterprise applications to a microservice architecture

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