MicroBlend: An Automated Service-Blending Framework for Microservice-Based Cloud Applications

Myungjun Son, Shruti Mohanty, Jashwant Raj Gunasekaran, Mahmut Kandemir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the increased usage of public clouds for hosting applications, it becomes essential to choose the appropriate services from the public cloud offerings in order to achieve satisfactory performance while minimizing deployment expenses. Prior research has demonstrated that combining different services can be more cost-effective than solutions based on a single service type. However, automating the combination of resources for applications composed of large graphs of loosely-connected microservices has not yet been thoroughly explored, especially in the context of microservice-based cloud applications. Motivated by this, targeting microservice-based applications, we propose MicroBlend, an automated framework that mixes Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS) cloud services in a way that is both cost-effective and performance-efficient. MicroBlend focuses on: (i) providing an automated approach for blending resources that takes microservice dependencies into account, (ii) generating FaaS-ready code using a compiler-based approach, and (iii) suggesting an optimization plan for combining microservices with user annotation. We implement MicroBlend on Amazon Web Services (AWS) and evaluate its performance using real-world traces from three different applications. Our findings demonstrate that by employing automated microservice-to-cloud service assignment, MicroBlend can significantly reduce Service Level Objective (SLO) violations by 9%, compared to traditional VM-based resource procurement schemes. Additionally, MicroBlend can decrease costs by 11%.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 16th International Conference on Cloud Computing, CLOUD 2023
EditorsClaudio Ardagna, Nimanthi Atukorala, Pete Beckman, Carl K. Chang, Rong N. Chang, Constantinos Evangelinos, Jing Fan, Geoffrey C. Fox, Judy Fox, Christoph Hagleitner, Zhi Jin, Tevfik Kosar, Manish Parashar
PublisherIEEE Computer Society
Pages460-470
Number of pages11
ISBN (Electronic)9798350304817
DOIs
StatePublished - 2023
Event16th IEEE International Conference on Cloud Computing, CLOUD 2023 - Hybrid, Chicago, United States
Duration: Jul 2 2023Jul 8 2023

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2023-July
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Conference

Conference16th IEEE International Conference on Cloud Computing, CLOUD 2023
Country/TerritoryUnited States
CityHybrid, Chicago
Period7/2/237/8/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

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