An agent-based service marketplace for dynamic and unreliable settings

TY - CHAP

T1 - An agent-based service marketplace for dynamic and unreliable settings

AU - Barakat, Lina

AU - Mahmoud, Samhar

AU - Miles, Simon

AU - Taweel, Adel

AU - Luck, Michael

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In order to address the unreliable nature of service providers, and the dynamic nature of services (their quality values could change frequently over time due to various factors), this paper proposes a probabilistic, multi-valued quality model for services, capable of capturing uncertainty in their quality values by assigning each quality attribute with multiple potential values (or ranges of values), along with a corresponding probability distribution over these values. The probability distribution indicates the most likely quality value for an attribute at the current time step, but also notifies discovery applications of the possibility of other, possibly worse outcomes, thus ultimately facilitating more reliable service selection and composition via avoiding services with high uncertainty. Such uncertainty-aware, multi-valued quality models of services are maintained via an agent-based service marketplace, where each service is associated with a software agent, capable of learning the time-varying probability distributions of its quality values through applying online learning techniques, based on the service’s past performance information. The experiments conducted demonstrate the effectiveness of the proposed approach.

AB - In order to address the unreliable nature of service providers, and the dynamic nature of services (their quality values could change frequently over time due to various factors), this paper proposes a probabilistic, multi-valued quality model for services, capable of capturing uncertainty in their quality values by assigning each quality attribute with multiple potential values (or ranges of values), along with a corresponding probability distribution over these values. The probability distribution indicates the most likely quality value for an attribute at the current time step, but also notifies discovery applications of the possibility of other, possibly worse outcomes, thus ultimately facilitating more reliable service selection and composition via avoiding services with high uncertainty. Such uncertainty-aware, multi-valued quality models of services are maintained via an agent-based service marketplace, where each service is associated with a software agent, capable of learning the time-varying probability distributions of its quality values through applying online learning techniques, based on the service’s past performance information. The experiments conducted demonstrate the effectiveness of the proposed approach.

KW - Adaptive learning

KW - Agent based marketplace

KW - Dynamic environment

KW - Probabilistic quality model

KW - Quality of service

UR - http://www.scopus.com/inward/record.url?scp=84910674379&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-45391-9_12

DO - 10.1007/978-3-662-45391-9_12

M3 - Conference paper

AN - SCOPUS:84910674379

SN - 9783662453902

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 169

EP - 183

BT - Service-Oriented Computing

A2 - Franch, Xavier

A2 - Ghose, Aditya K.

A2 - Lewis, Grace A.

A2 - Bhiri, Sami

PB - Springer Berlin Heidelberg

T2 - 12th International Conference on Service-Oriented Computing, ICSOC 2014

Y2 - 3 November 2014 through 6 November 2014

ER -