Goal-Driven Service Composition in Mobile and Pervasive Computing

Abstract

Mobile, pervasive computing environments respond to users' requirements by providing access to and composition of various services over networked devices. In such an environment, service composition needs to satisfy a request's goal, and be mobile-aware even throughout service discovery and service execution. A composite service also needs to be adaptable to cope with the environment's dynamic network topology. Existing composition solutions employ goal-oriented planning to provide flexible composition, and assign service providers at runtime, to avoid composition failure. However, these solutions have limited support for complex service flows and composite service adaptation. This paper proposes a self-organizing, goal-driven service model for task resolution and execution in mobile pervasive environments. In particular, it proposes a decentralized heuristic planning algorithm based on backward-chaining to support flexible service discovery. Further, we introduce an adaptation architecture that allows execution paths to dynamically adapt, which reduces failures, and lessens re-execution effort for failure recovery. Simulation results show the suitability of the proposed mechanism in pervasive computing environments where providers are mobile, and it is uncertain what services are available. Our evaluation additionally reveals the model's limits with regard to network dynamism and resource constraints.

Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile and Pervasive Computing

“…III. EVALUATION We used the NS-3 simulator to compare the performance of COPERNIC 1 against two state-of-the-art decentralized service composition models: GoCoMo, a goal-driven service model based on a decentralized heuristic backward-chaining planning algorithm [2]; and CoopC, a decentralized goal-driven cooperative composition model that does not support runtime composite service adaptation [3]. We modified service density (sparse (SD-S): 20, medium (SD-M): 40, dense (SD-D): 60); composition length (5 services (CL-5) or 10 services (CL-10)); and node mobility (slow (M-S): 0-2m/s, medium (M-M): 2-8m/s, and fast (M-F): 8-13m/s); and we measured 3 different metrics: composition time (CT in seconds), average memory used during the composition (MU in Kb), and a planning failure rate PFR (# of failed planning processes / # of all the issued requests).…”

“…Despite the existence of Mobile/Pervasive Computing (MPC) middleware for service composition [4], [10], [11], there are still some challenges that need to be tackled. Thus, we claim that service composition should: (1) consider requests from multiple users; (2) consider resource scarcity in smart devices;…”

Cognitively-Inspired Agent-Based Service Composition for Mobile and Pervasive Computing

“…Given the previous example we focus on five challenges, so service composition should: (1) consider preferences from multiple users; (2) coordinate the interaction between services hosted by different service providers; (3) consider resource scarcity in smart devices [21]; (4) perform dynamic adaptation to unpredictable changes occurring in the environment; (5) deal with both short-term and long-term user's goals. Performing service composition while taking into account a myriad of variable factors as described above (e.g., users, services, service providers, QoS values, context, etc.…”

Stigmergic Service Composition and Adaptation in Mobile Environments

“…Such applications are generally distributed among several devices since a single device is not capable of executing an entire application. For example, a smart route planner for users in a mall can be opportunistically provisioned by exploiting the ad-hoc interactions of a personal-shopper's phone, a nearby car's satellite navigator, and the mall's information kiosk to enable the requested functionality [8,17]. Apart from the functional requirements, this composition process should create solutions with the best possible QoS.…”

“…This approach relies on functionally-equivalent services, but with different QoS, to be optimally assigned to a predefined workflow of tasks. Such an exactly-defined request affects the composition' flexibility when the environment is dynamic [8]. The services can be combined in an inputoutput dependency graph, where each node corresponds to one service, and an edge is a matching between two connected services.…”