832 research outputs found
Recommended from our members
A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
Dynamic weight parameter for the Random Early Detection (RED) in TCP networks
This paper presents the Weighted Random Early Detection (WTRED) strategy for congestion handling in TCP networks. WTRED provides an adjustable weight parameter to increase the sensitivity of the average queue size in RED gateways to the changes in the actual queue size. This modification, over the original RED proposal, helps gateways minimize the mismatch between average and actual queue sizes in router buffers. WTRED is compared with RED and FRED strategies using the NS-2 simulator. The results suggest that WTRED outperforms RED and FRED. Network performance has been measured using throughput, link utilization, packet loss and delay
A quantitative analysis and performance study of fast congestion notification (FN) mechanism
Congestion in computer network happens when the number of transmission requests exceeds the transmission capacity at a certain network point (called a bottle-neck resource) at a specific time. Congestion usually causes buffers overflow and packets loss. The purpose of congestion management is to maintain a balance between the transmission requests and the transmission capacity so that the bottle-neck resources operate on an optimal level, and the sources are offered service in a way that assures fairness. Fast Congestion
Notification (FN) is one of the proactive queue management
mechanisms that limits the queuing delay and achieves the
maximum link utilization possible with minimum packet drops.
In this paper we present a detailed performance comparison of the Linear FN algorithm to RED based on the results obtained through simulations. The paper shows how FN can be tuned for different window size (Ws) and periods of time constant (T) to achieve higher link utilization; reduce the queuing delay, and lower packet drop ratio
ABC: A Simple Explicit Congestion Controller for Wireless Networks
We propose Accel-Brake Control (ABC), a simple and deployable explicit
congestion control protocol for network paths with time-varying wireless links.
ABC routers mark each packet with an "accelerate" or "brake", which causes
senders to slightly increase or decrease their congestion windows. Routers use
this feedback to quickly guide senders towards a desired target rate. ABC
requires no changes to header formats or user devices, but achieves better
performance than XCP. ABC is also incrementally deployable; it operates
correctly when the bottleneck is a non-ABC router, and can coexist with non-ABC
traffic sharing the same bottleneck link. We evaluate ABC using a Wi-Fi
implementation and trace-driven emulation of cellular links. ABC achieves
30-40% higher throughput than Cubic+Codel for similar delays, and 2.2X lower
delays than BBR on a Wi-Fi path. On cellular network paths, ABC achieves 50%
higher throughput than Cubic+Codel
Controlling Network Latency in Mixed Hadoop Clusters: Do We Need Active Queue Management?
With the advent of big data, data center applications are processing vast amounts of unstructured and semi-structured data, in parallel on large clusters, across hundreds to thousands of nodes. The highest performance for these batch big data workloads is achieved using expensive network equipment with large buffers, which accommodate bursts in network traffic and allocate bandwidth fairly even when the network is congested. Throughput-sensitive big data applications are, however, often executed in the same data center as latency-sensitive workloads. For both workloads to be supported well, the network must provide both maximum throughput and low latency. Progress has been made in this direction, as modern network switches support Active Queue Management (AQM) and Explicit Congestion Notifications (ECN), both mechanisms to control the level of queue occupancy, reducing the total network latency. This paper is the first study of the effect of Active Queue Management on both throughput and latency, in the context of Hadoop and the MapReduce programming model. We give a quantitative comparison of four different approaches for controlling buffer occupancy and latency: RED and CoDel, both standalone and also combined with ECN and DCTCP network protocol, and identify the AQM configurations that maintain Hadoop execution time gains from larger buffers within 5%, while reducing network packet latency caused by bufferbloat by up to 85%. Finally, we provide recommendations to administrators of Hadoop clusters as to how to improve latency without degrading the throughput of batch big data workloads.The research leading to these results has received funding from the European Unions Seventh Framework Programme (FP7/2007–2013) under grant agreement number 610456 (Euroserver).
The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contracts TIN2012-34557 and TIN2015-65316-P, Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), HiPEAC-3 Network of Excellence (ICT- 287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft
- …