TY - GEN
T1 - AEGLE's Cloud Infrastructure for Resource Monitoring and Containerized Accelerated Analytics
AU - Koliogeorgi, Konstantina
AU - Masouros, Dimosthenis
AU - Zervakis, Georgios
AU - Xydis, Sotirios
AU - Becker, Tobias
AU - Gaydadjiev, Georgi
AU - Soudris, Dimitrios
PY - 2017/7/20
Y1 - 2017/7/20
N2 - This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE's accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE's cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE's infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.
AB - This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE's accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE's cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE's infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.
KW - Accelerated Analytics
KW - Cloud Infrastructure
KW - Resource Monitoring
UR - https://www.scopus.com/pages/publications/85027244060
U2 - 10.1109/ISVLSI.2017.70
DO - 10.1109/ISVLSI.2017.70
M3 - Conference contribution
AN - SCOPUS:85027244060
T3 - Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
SP - 362
EP - 367
BT - Proceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
A2 - Reis, Ricardo
A2 - Stan, Mircea
A2 - Huebner, Michael
A2 - Voros, Nikolaos
PB - IEEE Computer Society
T2 - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
Y2 - 3 July 2017 through 5 July 2017
ER -