TY - GEN
T1 - ADVISE – A framework for evaluating cloud service elasticity behavior
AU - Copil, Georgiana
AU - Trihinas, Demetris
AU - Truong, Hong Linh
AU - Moldovan, Daniel
AU - Pallis, George
AU - Dustdar, Schahram
AU - Dikaiakos, Marios
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions.
AB - Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions.
UR - http://www.scopus.com/inward/record.url?scp=84910594524&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84910594524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 290
BT - Service-Oriented Computing - 12th International Conference, ICSOC 2014, Proceedings
A2 - Ghose, Aditya K.
A2 - Lewis, Grace A.
A2 - Bhiri, Sami
A2 - Franch, Xavier
PB - Springer Verlag
T2 - 12th International Conference on Service-Oriented Computing, ICSOC 2014
Y2 - 3 November 2014 through 6 November 2014
ER -