TY - GEN
T1 - SAL
T2 - 10th International Conference on Network and Service Management, CNSM 2014
AU - Shpiner, Alexander
AU - Keslassy, Isaac
AU - Arad, Carmi
AU - Mizrahi, Tal
AU - Revah, Yoram
N1 - Publisher Copyright:
© 2014 IFIP.
PY - 2014/1/16
Y1 - 2014/1/16
N2 - Multi-tenant data centers provide a cost-effective many-server infrastructure for hosting large-scale applications. These data centers can run multiple virtual machines (VMs) for each tenant, and potentially place any of these VMs on any of the servers. Therefore, for inter-VM communication, they also need to provide a VM resolution method that can quickly determine the server location of any VM. Unfortunately, existing methods suffer from a scalability bottleneck in the network load of the address resolution messages and/or in the size of the resolution tables. In this paper, we propose Smart Address Learning (SAL), a novel approach that expands the scalability of both the network load and the resolution table sizes, making it implementable on faster memory devices. The key property of the approach is to selectively learn the addresses in the resolution tables, by using the fact that the VMs of different tenants do not communicate. We further compare the various resolution methods and analyze the tradeoff between network load and table sizes. We also evaluate our results using real-life trace simulations. Our analysis shows that SAL can reduce both the network load and the resolution table sizes by several orders of magnitude.
AB - Multi-tenant data centers provide a cost-effective many-server infrastructure for hosting large-scale applications. These data centers can run multiple virtual machines (VMs) for each tenant, and potentially place any of these VMs on any of the servers. Therefore, for inter-VM communication, they also need to provide a VM resolution method that can quickly determine the server location of any VM. Unfortunately, existing methods suffer from a scalability bottleneck in the network load of the address resolution messages and/or in the size of the resolution tables. In this paper, we propose Smart Address Learning (SAL), a novel approach that expands the scalability of both the network load and the resolution table sizes, making it implementable on faster memory devices. The key property of the approach is to selectively learn the addresses in the resolution tables, by using the fact that the VMs of different tenants do not communicate. We further compare the various resolution methods and analyze the tradeoff between network load and table sizes. We also evaluate our results using real-life trace simulations. Our analysis shows that SAL can reduce both the network load and the resolution table sizes by several orders of magnitude.
UR - http://www.scopus.com/inward/record.url?scp=84922830122&partnerID=8YFLogxK
U2 - 10.1109/CNSM.2014.7014167
DO - 10.1109/CNSM.2014.7014167
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AN - SCOPUS:84922830122
T3 - Proceedings of the 10th International Conference on Network and Service Management, CNSM 2014
SP - 248
EP - 253
BT - Proceedings of the 10th International Conference on Network and Service Management, CNSM 2014
A2 - Raz, Danny
A2 - Nogueira, Michele
A2 - Madeira, Edmundo Roberto Mauro
A2 - Jennings, Brendan
A2 - Granville, Lisandro Zambenedetti
A2 - Gaspary, Luciano Paschoal
Y2 - 17 November 2014 through 21 November 2014
ER -