Practical Service Placement Approach for Microservices Architecture

Abstract

Abstract-Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of a "careful" placement of micro-clouds and services over the network.To achieve this, this paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is vital to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 211% in terms of bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the loss packet rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic.

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

“…To quantify the objectives, the authors proposed metrics and employed the metaheuristic technique for the optimization problem. Selimi et al proposed an approach to effectively place services in a network . The state of the network in which the services are to be deployed is also to be taken into account.…”

Guiding Architectural Decision Making on Service Mesh Based Microservice Architectures

“…For instance, Zheng et al [27] study the SLA-aware deployment of microservices. Selimi et al [21] study the service placement in a microservice architecture. Both studies are not specific for service meshes, but could be applied to them.…”

Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process

“…MEC is also more robust than traditional centralized cloud computing systems [10], because the edge servers (ES) are distributed and are thus less impacted by failures at a centralized location. The MEC concept is also known as cloudlet [10], edge cloud [11], fog computing [12], follow me cloud [13], micro cloud [14], [15], and small cell cloud [16].…”