Service Selection for On-Demand Provisioned Services

Abstract

Abstract-Service selection is an important concept in service oriented architectures that enables the dynamic binding of services based on functional and non-functional requirements. The introduction of the concept of on-demand provisioned services significantly changes the nature of services and as a consequence the traditional service selection process does not fit anymore. Existing approaches for service selection rely on the always on semantic of services, an assumption that is not valid for on-demand provisioned services. We tackle this problem by adapting the traditional service selection process and by defining an additional step covering the changes introduced by the concept of on-demand provisioning. Our solution comprises an extended architecture for on-demand provisioning, a metamodel for a service registry, and a detailed definition and discussion of the adapted and extended service selection process. The work presented in this paper allows keeping the advantages of dynamic service binding at runtime and combining them with the advantages of Cloud computing exploited through the concept of on-demand provisioning.

The Docker Ecosystem Needs Consolidation

“…Service discovery has always been an issue in SOA and has never been solved satisfactory in practice (Bachlechner et al, 2006). Recently, a new proposal was made for service discovery in a cloud context based on OpenTosca, an Enterprise Service Bus and Chef (Vukojevic-Haupt et al, 2014). Within the Docker ecosystem, the proposed tools often represent more of a service registry and leave it up to the application developer to use the provided lookup mechanism (r10).…”

Service Repository for Cloud Service Consumer Life Cycle Management

“…Service discovery is based on service information stored in service records that contain mandatory attributes such as service name, type of service, service endpoint, service interface, and service expiry date. VukojevicHaupt, et al [12] proposed a service selection method for on-demand provisioned services. Services are provided by a third party provider and service consumers have no knowledge about the implementation and the underlying infrastructure that supports the delivery of services.…”

Replicability of Dynamically Provisioned Scientific Experiments

“…In the following we will show how we realized the general concept of replicability for scientific experiments introduced in section II in our existing architecture for on-demand provisioning of workflow execution middleware and services [1] [4]. This architecture realizes the service binding strategy "dynamic binding with software stack provisioning" and in addition also supports "static binding" and "dynamic binding".…”

“…Experiments are assumed to be simple programs or scripts; there is no notion of SOC or workflows and consequently no runtime dynamicity like dynamic binding. Replicability is mainly related to the technical setup process, an aspect that is in our work encapsulated by service packages and the related provisioning engines together with a dynamic service package selection process at runtime [4].…”