TY - CHAP
T1 - Autonomic Decentralized Microservices - The Gru Approach and Its Evaluation
AU - Nitto, Elisabetta Di
AU - Florio, Luca
AU - Tamburri, Damian A.
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020
Y1 - 2020
N2 - Cloud applications are more and more featuring microservices as a design pattern, using related technologies (containerization, orchestration, continuous deployment, integration, and more) to speed up design, development, and operation. However, microservices are not bullet-proof: they increase design and management issues in the cloud adding to the mix all the intrinsic complexities of highly distributed systems. This addition can render ineffective all centralized management technologies like Docker or clustering systems like Swarm and Kubernetes. Conversely, autonomic and decentralized microservices management is still largely unexplored. We address this problem with Gru, an approach based on multiagent systems that adds an autonomic adaptation layer for microservice applications focusing on Docker, the de facto market leader in container technology. Gru is designed to support fully decentralized microservices management, and can be integrated with ease in dockerized applications, managing them with autonomic actions to satisfy application quality requirements. We evaluate Gru with a concrete case study showing autoscaling dockerized microservices matching variating and bursty workloads. Evaluation shows encouraging results for Gru autonomic management.
AB - Cloud applications are more and more featuring microservices as a design pattern, using related technologies (containerization, orchestration, continuous deployment, integration, and more) to speed up design, development, and operation. However, microservices are not bullet-proof: they increase design and management issues in the cloud adding to the mix all the intrinsic complexities of highly distributed systems. This addition can render ineffective all centralized management technologies like Docker or clustering systems like Swarm and Kubernetes. Conversely, autonomic and decentralized microservices management is still largely unexplored. We address this problem with Gru, an approach based on multiagent systems that adds an autonomic adaptation layer for microservice applications focusing on Docker, the de facto market leader in container technology. Gru is designed to support fully decentralized microservices management, and can be integrated with ease in dockerized applications, managing them with autonomic actions to satisfy application quality requirements. We evaluate Gru with a concrete case study showing autoscaling dockerized microservices matching variating and bursty workloads. Evaluation shows encouraging results for Gru autonomic management.
UR - http://www.scopus.com/inward/record.url?scp=85092693855&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-31646-4_9
DO - 10.1007/978-3-030-31646-4_9
M3 - Chapter
SN - 9783030316457
SP - 209
EP - 248
BT - Microservices
ER -