TY - GEN
T1 - Cross-layer SLA management for cloud-hosted big data analytics applications
AU - Zeng, Xuezhi
AU - Ranjan, Rajiv
AU - Strazdins, Peter
AU - Garg, Saurabh Kumar
AU - Wang, Lizhe
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/7
Y1 - 2015/7/7
N2 - As we come to terms with various big data challenges, one vital issue remains largely untouched. That is service level agreement (SLA) management to deliver strong Quality of Service (QoS) guarantees for big data analytics applications (BDAA) sharing the same underlying infrastructure, for example, a public cloud platform. Although SLA and QoS are not new concepts as they originated much before the cloud computing and big data era, its importance is amplified and complexity is aggravated by the emergence of time-sensitive BDAAs such as social network-based stock recommendation and environmental monitoring. These applications require strong QoS guarantees and dependability from the underlying cloud computing platform to accommodate real-time responses while handling ever-increasing complexities and uncertainties. Hence, the over-reaching goal of this PhD research is to develop novel simulation, modelling and benchmarking tools and techniques that can aid researchers and practitioners in studying the impact of uncertainties (contention, failures, anomalies, etc.) on the final SLA and QoS of a cloud-hosted BDAA.
AB - As we come to terms with various big data challenges, one vital issue remains largely untouched. That is service level agreement (SLA) management to deliver strong Quality of Service (QoS) guarantees for big data analytics applications (BDAA) sharing the same underlying infrastructure, for example, a public cloud platform. Although SLA and QoS are not new concepts as they originated much before the cloud computing and big data era, its importance is amplified and complexity is aggravated by the emergence of time-sensitive BDAAs such as social network-based stock recommendation and environmental monitoring. These applications require strong QoS guarantees and dependability from the underlying cloud computing platform to accommodate real-time responses while handling ever-increasing complexities and uncertainties. Hence, the over-reaching goal of this PhD research is to develop novel simulation, modelling and benchmarking tools and techniques that can aid researchers and practitioners in studying the impact of uncertainties (contention, failures, anomalies, etc.) on the final SLA and QoS of a cloud-hosted BDAA.
KW - Big data
KW - Cloud computing
KW - Service level agreement
UR - http://www.scopus.com/inward/record.url?scp=84941206362&partnerID=8YFLogxK
U2 - 10.1109/CCGrid.2015.175
DO - 10.1109/CCGrid.2015.175
M3 - Conference contribution
T3 - Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
SP - 765
EP - 768
BT - Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Y2 - 4 May 2015 through 7 May 2015
ER -