  Data centers are on the rise and scientists are re-thinking and re-designing
networks for data centers. The concept of central control which was not
effective in the Internet era is now gaining popularity and is used in many
data centers due to lower scale of operation (compared to Internet), structured
topologies and as the entire network resources is under a single entity's
control. With new opportunities, data center networks also pose new problems.
Data centers require: high utilization, low median, tail latencies and
fairness. In the traditional systems, the bulk traffic generally stalls the
interactive flows thereby affecting their flow completion times adversely. In
this thesis, we deal with two problems relating to central controller assisted
prioritization of interactive flow in data center networks.
  Fastpass is a centralized "zero-queue" data center network. But the central
arbiter of Fastpass doesn't scale well for more than 256 nodes (or 8 cores). In
our test runs, it supports only about 1.5 Terabits's of network traffic. In
this work, we re-design their timeslot allocator of their central arbiter so
that it scales linearly till 12 cores and supports about 1024 nodes and 7.1
Terabits's of network traffic.
  In the second part of the thesis, we deal with the problem of congestion
control in a software defined network. We propose a framework, where the
controller with its global view of the network actively participates in the
congestion control decisions of the end TCP hosts, by setting the ECN bits of
IPV4 packets appropriately. Our framework can be deployed very easily without
any change to the end node TCPs or the SDN switches. We also show 30x
improvement over TCP cubic and 1.7x improvement over RED in flow completion
times of interactive traffic for one implementation of this framework.
