TY - GEN
T1 - Enterprise data center globality measurement
AU - De Frein, Ruairí
AU - Pfaff, Joel
AU - Paré, Thomas
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - Measurement of the globality of On-Line Transaction Processing (OLTP) workloads in Enterprise Data Centers is considered. Providing OLTP workload isolation (application, services and databases) for performance-sensitive enterprise workloads, so that activity in one workload cannot interfere with another, remains a challenge. We demonstrate that traditional aggregate OLTP Workload globality measurement frameworks can generate mis-leading globality measures. We propose a higher-order globality measurement framework which addresses this problem. We derive two high dimensional structured measurement matrices, namely a template and measurand matrix, with special spectral properties, which account for globality measurement 1) boundedness; 2) programmability; 3) multiplicity; 4) relativity; 5) spatial correlation and 6) the appropriate sensitivity of the measure to changes in the distribution of the workload. We demonstrate that these properties are exhibited by the new measure by ordering OLTP workloads by their globality measure. We evaluate the measure using a stochastic layered block model for data center topology and OLTP workload generation and demonstrate that it is consistent.
AB - Measurement of the globality of On-Line Transaction Processing (OLTP) workloads in Enterprise Data Centers is considered. Providing OLTP workload isolation (application, services and databases) for performance-sensitive enterprise workloads, so that activity in one workload cannot interfere with another, remains a challenge. We demonstrate that traditional aggregate OLTP Workload globality measurement frameworks can generate mis-leading globality measures. We propose a higher-order globality measurement framework which addresses this problem. We derive two high dimensional structured measurement matrices, namely a template and measurand matrix, with special spectral properties, which account for globality measurement 1) boundedness; 2) programmability; 3) multiplicity; 4) relativity; 5) spatial correlation and 6) the appropriate sensitivity of the measure to changes in the distribution of the workload. We demonstrate that these properties are exhibited by the new measure by ordering OLTP workloads by their globality measure. We evaluate the measure using a stochastic layered block model for data center topology and OLTP workload generation and demonstrate that it is consistent.
UR - https://www.scopus.com/pages/publications/84964284862
U2 - 10.1109/CIT/IUCC/DASC/PICOM.2015.277
DO - 10.1109/CIT/IUCC/DASC/PICOM.2015.277
M3 - Conference contribution
AN - SCOPUS:84964284862
T3 - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
SP - 1861
EP - 1869
BT - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
A2 - Atzori, Luigi
A2 - Jin, Xiaolong
A2 - Jarvis, Stephen
A2 - Liu, Lei
A2 - Calvo, Ramon Aguero
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Wu, Yulei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
Y2 - 26 October 2015 through 28 October 2015
ER -