TY - GEN
T1 - An uncertain assessment compatible incentive mechanism for eliciting continual and truthful assessments of cloud services
AU - Qu, Lie
AU - Wang, Yan
AU - Orgun, Mehmet
PY - 2016
Y1 - 2016
N2 - The evaluation of dynamic performance of cloud services relies on continual assessments from cloud users, e.g., ordinary consumers and testing parties. In order to elicit continual and truthful assessments, an effective incentive mechanism in cloud environments should allow users to provide uncertain assessments when they are not sure about the real performance of cloud services, e.g., when users do not access cloud services on time, rather than providing untruthful or arbitrary assessments. Different from all prior works, we propose a novel uncertain assessment compatible incentive mechanism. Under this mechanism, a user not only has sufficient incentives to continually provide truthful assessments, but also would prefer providing uncertain assessments over untruthful or arbitrary assessments since uncertain assessments can bring more benefits than untruthful or arbitrary assessments. We theoretically analyze the proposed incentive mechanism and evaluate it through simulations under different circumstances. The theoretical analysis demonstrates the effectiveness of our approach. Moreover, the experimental results based on simulations strongly support the results from the theoretical analysis.
AB - The evaluation of dynamic performance of cloud services relies on continual assessments from cloud users, e.g., ordinary consumers and testing parties. In order to elicit continual and truthful assessments, an effective incentive mechanism in cloud environments should allow users to provide uncertain assessments when they are not sure about the real performance of cloud services, e.g., when users do not access cloud services on time, rather than providing untruthful or arbitrary assessments. Different from all prior works, we propose a novel uncertain assessment compatible incentive mechanism. Under this mechanism, a user not only has sufficient incentives to continually provide truthful assessments, but also would prefer providing uncertain assessments over untruthful or arbitrary assessments since uncertain assessments can bring more benefits than untruthful or arbitrary assessments. We theoretically analyze the proposed incentive mechanism and evaluate it through simulations under different circumstances. The theoretical analysis demonstrates the effectiveness of our approach. Moreover, the experimental results based on simulations strongly support the results from the theoretical analysis.
UR - http://www.scopus.com/inward/record.url?scp=84989347344&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46295-0_21
DO - 10.1007/978-3-319-46295-0_21
M3 - Conference proceeding contribution
AN - SCOPUS:84989347344
SN - 9783319462943
VL - 9936 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 335
EP - 351
BT - Service-Oriented Computing - 14th International Conference, ICSOC 2016, Proceedings
A2 - Sheng, Quan Z.
A2 - Stroulia, Eleni
A2 - Tata, Samir
A2 - Bhiri, Sami
PB - Springer, Springer Nature
CY - Switzerland
T2 - 14th International Conference on Service-Oriented Computing, ICSOC 2016
Y2 - 10 October 2016 through 13 October 2016
ER -