MicroTE: fine grained traffic engineering for data centers

By Theophilus A. Benson, Ashok Anand, Aditya Akella, Ming Zhang. Conference on Emerging Network Experiment and Technology, 2011

697 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/988653d9cc361ce41dbe0930eaf2ecea27c85053

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

Citations in the corpus

HULA: Scalable Load Balancing Using Programmable Data Planes (2016, background): “However, ECMP suffers from degraded performance [8–12] if two long-running ﬂows are assigned to the same path.”
Planck (2014, methodology): “Our traffic engineering application draws inspiration from Hedera [2] and MicroTE [4] and takes a similar approach of measuring the network and rerouting flows based on measurements.”
CONGA: distributed congestion-aware load balancing for datacenters (2015, background): “The source leaf makes load balancing decisions based on per uplink congestion metrics, derived by taking the maximum of the local congestion at the uplink and the remote congestion for the path (or paths) to the destination leaf that originate at the uplink.”
Heavy-Hitter Detection Entirely in the Data Plane (2016, methodology): “Many network management applications can bene t from nding the set of ows contributing signi cant amounts of tra c to a link: for example, to relieve link congestion [5], to plan network capacity [18], to detect network anomalies and attacks [23], or to cache forwarding table entries [36].”
Presto: Edge-based Load Balancing for Fast Datacenter Networks (2015, background): “Hed-era [3], MicroTE [12] and Planck [54] use centralized trafﬁc engineering to reroute trafﬁc based on network conditions.”
A roadmap for traffic engineering in SDN-OpenFlow networks (2014, methodology): “MicroTE [37] is a fine-grained traffic engineering scheme that works atop a variety of underlying data center network topologies.”
Efficient Coflow Scheduling Without Prior Knowledge (2015, background): “While traffic managers like Hedera [8] and MicroTE [16] cannot directly be used to optimize coflows, they can be extended to perform coflow-aware throughput maximization and load balancing.”
Dynamic scheduling of network updates (2014, background): “Categories and Subject Descriptors: C.2.1 [ComputerCommunication Networks]: Network Architecture and Design— Network communications; C.2.3 [Computer-Communication Networks]: Network Operations—Network management Keywords: Software-defined networking; network update”
Software Defined Networking Architecture, Security and Energy Efficiency: A Survey (2017, background): “MicroTE has been proposed in [156] as a traffic monitoring scheme in the network.”
Efficient coflow scheduling with Varys (2014, neutral): “Datacenter Trafﬁc Management Hedera [7] manages ﬂows us-ing a centralized scheduler to increase network throughput, and MicroTE [12] adapts to trafﬁc variations by leveraging their short-term predictability.”
Inside the Social Network's (Datacenter) Network (2015, background): “[14] claim that while traffic is unpredictable at timescales of 150 seconds and longer, it can be relatively stable on the timescale of a few seconds, and discuss traffic engineering mechanisms that might work for such traffic.”
Participatory networking: an API for application control of SDNs (2013, methodology): “Hints about trafﬁc predictability can be used by optimizers such MicroTE [5].”