TY - GEN
T1 - On optimizing MMVEs in network-aware clouds
AU - Huang, Yu Siang
AU - Hsu, Cheng Hsin
AU - El Zarki, Magda
AU - Erbad, Aiman
AU - Venkatasubramanian, Nalini
PY - 2014
Y1 - 2014
N2 - Network operators will soon cooperate with traditional cloud providers to offer network-virtualization-based converged cloud services, which are referred to as network-aware clouds. Network-aware clouds allow network operators to share income with Over-The-Top (OTT) providers by providing them with end-to-end network QoS guarantees. For MMVE providers, leveraging the computation, storage, and communication resources offered by network-aware clouds for the best MMVE QoE levels is crucial to their success. In this paper, we point out a main research challenge: optimally placing various fine-grained MMVE tasks across heterogeneous clouds, which provide diverse computation and storage QoS guarantees (in data centers) and communication QoS guarantees (end-to-end). Via real experiments, we demonstrate the potential of network-aware clouds on improving the QoE of MMVEs. Achieving the optimal QoE level, however, is no easy task because of the dynamic nature of networks and virtual environments and the complex interplay between cloud QoS guarantees and MMVE QoE metrics, such as responsiveness, precision, and fairness. Throughly addressing the task placement problem is our current work. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
AB - Network operators will soon cooperate with traditional cloud providers to offer network-virtualization-based converged cloud services, which are referred to as network-aware clouds. Network-aware clouds allow network operators to share income with Over-The-Top (OTT) providers by providing them with end-to-end network QoS guarantees. For MMVE providers, leveraging the computation, storage, and communication resources offered by network-aware clouds for the best MMVE QoE levels is crucial to their success. In this paper, we point out a main research challenge: optimally placing various fine-grained MMVE tasks across heterogeneous clouds, which provide diverse computation and storage QoS guarantees (in data centers) and communication QoS guarantees (end-to-end). Via real experiments, we demonstrate the potential of network-aware clouds on improving the QoE of MMVEs. Achieving the optimal QoE level, however, is no easy task because of the dynamic nature of networks and virtual environments and the complex interplay between cloud QoS guarantees and MMVE QoE metrics, such as responsiveness, precision, and fairness. Throughly addressing the task placement problem is our current work. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
KW - Cloud
KW - Distributed systems
KW - Games
KW - IaaS
KW - NaaS
KW - Quality of experience
UR - https://www.scopus.com/pages/publications/84899673050
U2 - 10.1145/2577387.2577391
DO - 10.1145/2577387.2577391
M3 - Conference contribution
AN - SCOPUS:84899673050
SN - 9781450327084
T3 - Proceedings of the 6th ACM International Workshop on Massively Multiuser Virtual Environments, MMVE 2014
BT - Proceedings of the 6th ACM International Workshop on Massively Multiuser Virtual Environments, MMVE 2014
PB - Association for Computing Machinery
T2 - 6th ACM International Workshop on Massively Multiuser Virtual Environments, MMVE 2014
Y2 - 19 March 2014 through 21 March 2014
ER -