Optimizing Service Selection and Allocation in Situational Computing Applications

Abstract

Abstract-This paper describes a novel model for the service selection problem of workflow-based applications in the context of self-managing situated computing. In such systems, the execution environment includes different types of devices, from remote servers to personal notebooks, smartphones, and wireless sensors, which build an infrastructure that can dynamically change both its physical and logical architecture at run-time. We assume that worflows are defined abstractly; i.e., they invoke abstract services whose concrete counterparts can be selected dynamically. We also assume that concrete service implementations may possibly migrate on the nodes of the infrastructure. The selection problem we address is framed as an optimization problem of the quality of service, which evaluates at run-time the optimal binding to concrete services as well as the trade-off between the remote execution of software fragments and their dynamic deployment on local nodes of the computational environment. The final deployment takes into account quality of service constraints, the capabilities of the physical devices involved, including their performance and energy consumption, and the characteristics of the networking links connecting them.

QoS-aware web services composition using GRASP with Path Relinking

“…Finally, regarding the application contexts of the 12 QoS-aware web service composition, it has recently ap-13 plied to: optimize network latency in Cloud environ-14 ments (Klein et al, 2012); improve the robustness and 15 flexibility of systems using data from dynamic sensor 16 networks (Geyik et al, 2013) (Efstathiou et al, 2013);17 and to optimize the allocation of resources in situational 18 computing applications (Sandionigi et al, 2013). …”

Towards Green Service Composition Approach in the Cloud

“…In addition to the settings used for GA+, we extend it with our proposed energy consumption model and network resource consumption model. The fitness function is the sum of (11) and (12). The parameters settings for GA+ and GA are same.…”

“…Although energy efficiency has become an active focus of research [8,10,11], the energy consumption of composite services is yet to be considered. Those services are typically distributed across various physical servers with varying energy constraints depending on the types of switches utilized, the types of computing servers, etc.…”

Service selection in mobile environments: considering multiple users and context-awareness

“…Ai & Tang, 2008; Deng, Huang, Hu, Zhao, & Wu, 2016; Sandionigi, Ardagna, Cugola, & Ghezzi, 2013; Vanrompay, Pinheiro, & Berbers, 2009; Yu & Reiff-Marganiec, 2009; Zhou, Zheng, Song, Du, & Chen, 2008). Few of them also consider context-based dependencies that could exist within a certain part or the entire service composition of a user (e.g.…”