Event detection system
Event detection system in real time streaming data for traffic control in smart cities 300.00$
City School Network Automation
City School Network Automation 300.00$

Fine tunning ECN and RTT based Congestion Control in Data Centers Networks

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Project Domain / Category


Abstract / Introduction

Web applications are gradually shifting into cloud environments. Fine tunning system, these cloud services are hosted on huge scale computation and storage infrastructures called data centers (DC) (e.g. Google’s data center, Facebook’s data center etc.). A data center network (DCN) interconnects all the data center resources.

Current data center congestion control schemes may induce high latency in packet delivery due to path’s latency-oblivious congestion signal. Explicit congestion notification (ECN) is the predominantly used congestion signal in the data center networks; it signals whether any queue along the path is above a predefined threshold or not, but does not inform about the end-to-end delay of the path. Information of the end-to-end delay / round-trip-time (RTT) of a path can enable a sender to adjust its sending rate to keep the network latency below a threshold.

RTTs in data center networks are on the scale of up to few hundreds of microseconds and traditional data center operating systems lack such fine-grained microsecond-level timers; that is why delay-based congestion control schemes, that are widely deployed in the Internet, have not been used in the data center networks. But recent studies like [1] suggest that advances in NIC hardware has enabled accurate microsecond-level RTT measurement, thus, RTT can be incorporated in the data center congestion control schemes


Fine tunning system project aims to combine ECN and RTT signals in the data center congestion control scheme and Fine tunning it. The students will: i) study an existing widely-deployed DCN congestion control scheme and its implementation in ns-2, ii) design changes in the existing algorithm(s) to incorporate delay/RTT, iii) implement the design in the existing congestion control scheme in ns-2.

Working in ns-2 requires:
  1. understanding of basic commands of Linux operating systems (for ns-2 installation and running purposes)
  2. excellent programming skills in C++ (for simulating the DCN environment and implementing the proposed changes in the existing congestion control scheme)
  3. programming in TCL (for writing simulation scripts)
  4. understanding of AWK command (for trace text processing)
  5. understanding GNUPLOT command (for drawing graphs).


[1] Mittal, R., Lam, V. T., Dukkipati, N., Blem, E., Wassel, H., Ghobadi, M., … & Zats, D. (2015). TIMELY: RTT-based Congestion Control for the Datacenter. ACM SIGCOMM Computer Communication Review, 45(4), 537-550. Intended Outcome

  • Design of a path’s latency-aware DCN congestion control scheme
  • Implementation of a path’s latency-aware DCN congestion control scheme in ns-2



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