FairCloud: sharing the network in cloud computing

By L. Popa, G. Kumar, Mosharaf Chowdhury, Arvind Krishnamurthy, Sylvia Ratnasamy, Ion Stoica. CCRV, 2012

436 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/4c85be0340a1409787109cf77ced5bc379b00c50

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Performance Isolation and Fairness for Multi-Tenant Cloud Storage (2012, neutral): “While more throughput efficient, Gatekeeper’s ingress and egress scheduling [27], Seawall’s congestion-controlled VM tunneling [28], and FairCloud’s per-endpoint sharing [26], respectively provide fairness on a per-VM, VM-pair, or communicating-VMgroup basis, rather than on a per-tenant basis.”
Programmable Packet Scheduling at Line Rate (2016, methodology): “, minimizing flow completion times [12] using Shortest Remaining Processing Time [35], allocating bandwidth flexibly across flows or tenants [26, 33] using Weighted Fair Queueing [20], minimizing tail packet delays using Least Slack Time First [29], etc.”
End-to-end Performance Isolation Through Virtual Datacenters (2014, background): “These services are often implemented using appliances, which include in-network middleboxes like load balancers and end-devices like networked storage servers.”
Resource Management in Clouds: Survey and Research Challenges (2015, background): “[174] sharing network resources is challenging because ‘‘the network allocation for a VM X depends not only on the VMs running on the machine with X, but also on the other VMs that X communicates with, as well as the cross traffic on each link used by X.”
HULA: Scalable Load Balancing Using Programmable Data Planes (2016, background): “Load balancing also provides the abstraction of a single large output-queued switch for the entire network [1–3], which in turn simpliﬁes bandwidth allocation across tenants [4,5], ﬂows [6], or groups of ﬂows [7].”
Making Sense of Performance in Data Analytics Frameworks (2015, background): “Researchers have used the argument that data-intensive application performance is closely tied to datacenter network performance to justify a wide variety of network optimizations [6,14,17–21,27,28,41,42,48,53].”