On the marginal utility of network topology measurements
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
VL2: a scalable and flexible data center network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Hedera: dynamic flow scheduling for data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
ICTCP: Incast Congestion Control for TCP in data center networks
Proceedings of the 6th International COnference
Mesos: a platform for fine-grained resource sharing in the data center
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Managing data transfers in computer clusters with orchestra
Proceedings of the ACM SIGCOMM 2011 conference
DevoFlow: scaling flow management for high-performance networks
Proceedings of the ACM SIGCOMM 2011 conference
Improving datacenter performance and robustness with multipath TCP
Proceedings of the ACM SIGCOMM 2011 conference
Purlieus: locality-aware resource allocation for MapReduce in a cloud
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
GRIN: utilizing the empty half of full bisection networks
HotCloud'12 Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing
Choreo: network-aware task placement for cloud applications
Proceedings of the 2013 conference on Internet measurement conference
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Optimizing distributed applications in the cloud requires network topology information, yet this information is kept confidential by the cloud providers. Today, applications can infer network properties and optimize accordingly but this is costly to get right. The cloud can optimize the network via load balancing, but the scope is limited to moving traffic between the available paths. In this paper we challenge this status quo. We show that network topology information is not confidential in the first place by conducting a study of Amazon's EC2 topology, and we show how such information can have a big impact in optimizing a web search application. We propose that applications and the network should break the silence and communicate to allow better optimizations that will benefit both parties. To this end we design CloudTalk, a very simple language that allows apps to express traffic patterns that are then ranked by the network. The ranking helps application pick the right way to implement its tasks. We provide a proof-of-concept implementation of CloudTalk showing that it is expressive enough to capture many traffic patterns and that it is feasible to use in practice.