Quality of Service‐aware matchmaking for adaptive microservice‐based applications

Abstract

Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi-tier applications along edge, fog, and cloud tiers by considering resource properties and non-functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud-To-Things infrastructure based on a subgraph pattern matching (P-Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud-native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above.

Location‐aware scalable service composition

“…Services must be executed as efficiently as possible, either individually or in combination with other services as described in the application's architecture. We assume that a previous matchmaking—in the line of those proposed by, for example, Wu and Wu, 51 Stefanic et al, 52 and Kritikos and Plexousakis 53 —has been carried out, so that each service in an application will have a list of possible service providers where they can be deployed to be executed. Each service has a list of available and fully compatible providers to be executed, which may be different from the lists of the other services.…”

Editorial: Science Gateways Special Issue 2020

“…MiCADO utilizes abstract application description templates based on the TOSCA specification that help separate the application (and associated resource) description from the orchestration solution, enabling one solution to be swapped out for another. Štefanič et al 2 tackle the challenge of efficient distribution of IoT application components (as microservices) across the continuum of resources available from the device, edge, fog, and so on, to the cloud to achieve low‐latency processing workflows. This challenge is transformed into and solved as a subgraph isomorphism problem through a novel representation of both the application constraints and the resource linkages and Quality of Service (QoS) metrics as labeled graphs.…”

Multilayer Resource-aware Partitioning for Fog Application Placement

“…Taneja et al [1] proposed a resource-aware application mapping approach to optimize resource utilization in Fog. Similarly, Stefanic et al [14] proposed a subgraph pattern matching approach for application placement that maps the multi-tier application graph onto the Fog infrastructure to improve resource utilization. Nevertheless, these three approaches only consider different Fog resources characteristics and ignore the network topology structure of Fog devices.…”