TY - GEN
T1 - Autonomic cross-layer management of cloud systems
AU - Tunc, Cihan
AU - Fargo, Farah
AU - Al-Nashif, Youssif
AU - Hariri, Salim
N1 - Funding Information:
ACKNOWLEDGMENT This work is partly supported by National Science Foundation (NSF) research project NSF CNS-1624668, Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA95550-12-1-0241, and Thomson Reuters in the framework of the Partner University Fund (PUF) project (PUF is a program of the French Embassy in the United States and the FACE Foundation and is supported American donors and the French government).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/9
Y1 - 2017/10/9
N2 - The current management techniques of large scale cloud systems are inefficient, and lead to high operational costs. In this paper, we present a cross-layer management framework that overcomes the current IT management challenges of cloud systems and their applications. The proposed framework monitors and performs holistic analysis of the cloud system resource utilization from the physical layer, the hypervisor layer, and the virtual machine (VM) layer. For a given set of constraints (such as low power, cost minimization, etc.), policies are generated based on the Service Level Agreements (SLAs) that will be maintained at runtime by our cross-layer management system. We have evaluated our framework using a bidding application on a private cloud system. By autonomously scaling up/down the VM resources at runtime, the cross-layer management framework can effectively improve resource utilization and reduce the operational costs.
AB - The current management techniques of large scale cloud systems are inefficient, and lead to high operational costs. In this paper, we present a cross-layer management framework that overcomes the current IT management challenges of cloud systems and their applications. The proposed framework monitors and performs holistic analysis of the cloud system resource utilization from the physical layer, the hypervisor layer, and the virtual machine (VM) layer. For a given set of constraints (such as low power, cost minimization, etc.), policies are generated based on the Service Level Agreements (SLAs) that will be maintained at runtime by our cross-layer management system. We have evaluated our framework using a bidding application on a private cloud system. By autonomously scaling up/down the VM resources at runtime, the cross-layer management framework can effectively improve resource utilization and reduce the operational costs.
KW - autonomic resource management
KW - cloud computing
KW - self-configuring systems
UR - http://www.scopus.com/inward/record.url?scp=85035236540&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035236540&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2017.141
DO - 10.1109/FAS-W.2017.141
M3 - Conference contribution
AN - SCOPUS:85035236540
T3 - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
SP - 160
EP - 165
BT - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
Y2 - 18 September 2017 through 22 September 2017
ER -