This document discusses different approaches for queue management and congestion control in routers. It describes FIFO with Drop Tail, which uses a single queue and drops arriving packets when the queue is full. This can bias against bursty traffic and cause global synchronization issues. Random drop and Early Random Drop are also summarized, which drop packets randomly to avoid bursty traffic bias. The document then describes RED (Random Early Detection), which calculates average queue length and drops arriving packets randomly if the average is above a threshold, in order to signal congestion before queues fill up.
Qmagik is an automated customer flow management / queue management system. It is extremely important that the queue management system should be simple and easy to use and maintain. The Queue management system once implemented will be used mostly by the general public who visits the service organization to avail one or the other kind of service Applications of Queue Management Solution software system hospitals-clinic,banks-financial,telecom services,government office in INDIA,DUBAI,UAE,LONDON.
This slideshow presentation offers top practices and compares technologies that can help you better manage your queue. You’ll discover how companies across industries – from transportation to retail to amusement parks – are using intelligent queue management and learn how to build a business case for your own adoption of technology.
This is a one type of Management Service so customer
can Book Appointment.
Vendor can earn more revenue.
It will increase vendor’s profit .
User can get service without Waiting in Queue.
Onlinet Group - Queue Management, Kiosk, Digital Signage, Smartphone AppONLINET Group
Latest product catalogue for Queue Management System, Customer Flow Management, Information and Self-Service Kiosk, Digital Signage, Interactive Window Display and Smartphone app
Qmagik is an automated customer flow management / queue management system. It is extremely important that the queue management system should be simple and easy to use and maintain. The Queue management system once implemented will be used mostly by the general public who visits the service organization to avail one or the other kind of service Applications of Queue Management Solution software system hospitals-clinic,banks-financial,telecom services,government office in INDIA,DUBAI,UAE,LONDON.
This slideshow presentation offers top practices and compares technologies that can help you better manage your queue. You’ll discover how companies across industries – from transportation to retail to amusement parks – are using intelligent queue management and learn how to build a business case for your own adoption of technology.
This is a one type of Management Service so customer
can Book Appointment.
Vendor can earn more revenue.
It will increase vendor’s profit .
User can get service without Waiting in Queue.
Onlinet Group - Queue Management, Kiosk, Digital Signage, Smartphone AppONLINET Group
Latest product catalogue for Queue Management System, Customer Flow Management, Information and Self-Service Kiosk, Digital Signage, Interactive Window Display and Smartphone app
A Fuzzy Based Dynamic Queue Management Approach to Improve QOS in Wireless se...IJARIDEA Journal
Abstract— Wireless Sensor Networks (WSNs) are predicted to be the following iteration of networks which
will kind an indispensable a part of man’s lives and which furnish a bridge between the true bodily and
virtual worlds. WSNs will have to be able to aid more than a few functions over the same platform. Specific
applications would have unique QoS requirements helping the preliminary specifications for delivering
Quality of services (QoS), which is fundamental for numerous purposes, is directly concerning energy
consumption, delay, reliability, distortion, and community lifetime. There may be an inevitable correlation
between quality of accessible service levels in WSNs and power consumption in these networks, while
acquiring any of those bases acquires the influential interface on the other.
Keywords—Data integrity, Delay differentiated services, Dynamic routing, Potential field, Wireless sensor
networks.
Onlinet Queue Management System - Visual TourONLINET Group
The concept and functioning of a Queue Management System. Forget the queuing and control your customer flow, increase your efficiency and make your customers loyal.
Efficient Digital Signage With Queue Management SystemONLINET Group
How to make a Digital Signage application efficient with Queue Management System. Forget the queuing and ineffective advertising. Control your customer flow and reach your target audience with targeted content.
Optimizing the Robustness of X-by-Wire using Word CombinatoricsNicolas Navet
Optimizing the Robustness of X-by-Wire using Word Combinatorics", Ecole Polytechnique Fédérale de Lausanne (EPFL), Network Calculus Group Seminar, 3rd March 2004,
A Fuzzy Based Dynamic Queue Management Approach to Improve QOS in Wireless se...IJARIDEA Journal
Abstract— Wireless Sensor Networks (WSNs) are predicted to be the following iteration of networks which
will kind an indispensable a part of man’s lives and which furnish a bridge between the true bodily and
virtual worlds. WSNs will have to be able to aid more than a few functions over the same platform. Specific
applications would have unique QoS requirements helping the preliminary specifications for delivering
Quality of services (QoS), which is fundamental for numerous purposes, is directly concerning energy
consumption, delay, reliability, distortion, and community lifetime. There may be an inevitable correlation
between quality of accessible service levels in WSNs and power consumption in these networks, while
acquiring any of those bases acquires the influential interface on the other.
Keywords—Data integrity, Delay differentiated services, Dynamic routing, Potential field, Wireless sensor
networks.
Onlinet Queue Management System - Visual TourONLINET Group
The concept and functioning of a Queue Management System. Forget the queuing and control your customer flow, increase your efficiency and make your customers loyal.
Efficient Digital Signage With Queue Management SystemONLINET Group
How to make a Digital Signage application efficient with Queue Management System. Forget the queuing and ineffective advertising. Control your customer flow and reach your target audience with targeted content.
Optimizing the Robustness of X-by-Wire using Word CombinatoricsNicolas Navet
Optimizing the Robustness of X-by-Wire using Word Combinatorics", Ecole Polytechnique Fédérale de Lausanne (EPFL), Network Calculus Group Seminar, 3rd March 2004,
Computer networks have experienced an explosive growth over the past few years and with
that growth have come severe congestion problems. For example, it is now common to see
internet gateways drop 10% of the incoming packets because of local buffer overflows.
Our investigation of some of these problems has shown that much of the cause lies in
transport protocol implementations (
not
in the protocols themselves): The ‘obvious’ ways
to implement a window-based transport protocol can result in exactly the wrong behavior
in response to network congestion. We give examples of ‘wrong’ behavior and describe
some simple algorithms that can be used to make right things happen. The algorithms are
rooted in the idea of achieving network stability by forcing the transport connection to obey
a ‘packet conservation’ principle. We show how the algorithms derive from this principle
and what effect they have on traffic over congested networks.
In October of ’86, the Internet had the first of what became a series of ‘congestion col-
lapses’. During this period, the data throughput from LBL to UC Berkeley (sites separated
by 400 yards and two IMP hops) dropped from 32 Kbps to 40 bps. We were fascinated by
this sudden factor-of-thousand drop in bandwidth and embarked on an investigation of why
things had gotten so bad. In particular, we wondered if the 4.3
BSD
(Berkeley U
NIX
)
TCP
was mis-behaving or if it could be tuned to work better under abysmal network conditions.
The answer to both of these questions was “yes”.
A Packet Drop Guesser Module for Congestion Control Protocols for High speed ...ijcseit
Different high speed Transport layer protocols have been designed and proposed in the literature to
improve the performance of standard TCP on high BDP links. They are mainly different in their increase
and decrease formulas of their respective congestion control algorithm. Most of these high speed protocols
consider every packet drop in the network as an indication of congestion and they immediately reduce their
congestion window size. Such an approach will usually result in under utilization of available bandwidth in
case of noisy channel conditions. We take CUBIC as a test case and have compared its performance in
case of normal and noisy channel conditions. The throughput of CUBIC was drastically degraded from
50Mbps to 0.5Mbps when we introduced a random packet drops with 0.001 probability. When the
probability of the packet drops increases then the throughput gets decreases. Indeed, we need to
complement existing congestion control algorithms with some intelligent mechanisms that can differentiate
whether a certain packet drop is because of congestion or channel error thus avoid unnecessary window
reduction. In order to distinguish between packets drops, we have developed a k-NN based module to guess
whether the packet drops are due to the congestion or any other reasons. After integrating this module with
CUBIC algorithm, we have observed significant performance improvement.
Available network bandwidth schema to improve performance in tcp protocolsIJCNCJournal
The TCP congestion control mechanism in standard implementations presents several problems, for
example, large queue lengths in network routers and packet losses, a misleading reduce of the transmission
rate when there are link failures, among others. This paper proposes a schema to congestion control in
TCP protocols, called NGWA, witch is based on the network bandwidth. The NGWA provides information
considering the available bandwidth of the network infrastructure to the endpoints of the TCP connection.
Hence, it helps in choosing a better transmission rate for TCP improving its performance. Simulation
results show superior performance of the proposed scheme when compared to those obtained by TCP New
Reno and standard TCP. A physical implementation in the Linux kernel was performed to prove the correct
operation of the proposal.
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2. Not all flows are
congestion controlled
3 October 2008 NUS CS5229, Semester 1 2008/09 2
3. 1. Don’t need reliability, so
use UDP, which has no
congestion control
3 October 2008 NUS CS5229, Semester 1 2008/09 3
4. 2. No incentive to limit
own sending rate
3 October 2008 NUS CS5229, Semester 1 2008/09 4
5. Sally Floyd and Kevin Fall
“Promoting End-to-End
Congestion Control in
the Internet”
TON, 1999
3 October 2008 NUS CS5229, Semester 1 2008/09 5
6. What mechanisms can we
add to the router to provide
incentives for congestion
control?
3 October 2008 NUS CS5229, Semester 1 2008/09 6
7. Idea: Identify unresponsive flows,
then drop their packets or
regulate their rate.
3 October 2008 NUS CS5229, Semester 1 2008/09 7
8. Note: Not scalable to large
number of flows
(eg in core routers).
3 October 2008 NUS CS5229, Semester 1 2008/09 8
9. How to identify
unresponsive flows in a
router?
3 October 2008 NUS CS5229, Semester 1 2008/09 9
10. Approach 1:
Use TCP Throughput
Equation
3 October 2008 NUS CS5229, Semester 1 2008/09 10
11. The paper uses a rough approximation
3 October 2008 NUS CS5229, Semester 1 2008/09 11
12. MSS: Maximum packet size in bytes
over all outgoing links
p: Packet drop rates over all
outgoing links
RTT: Twice the 1-way propagation
delay of outgoing links
3 October 2008 NUS CS5229, Semester 1 2008/09 12
13. The expression will overestimate
the fair throughput for TCP.
Thus, not all unfriendly flows will
be identified.
3 October 2008 NUS CS5229, Semester 1 2008/09 13
15. Does the packet arrival rate of a
flow reduce appropriately when
packet drop rate increase?
3 October 2008 NUS CS5229, Semester 1 2008/09 15
16. If packet drop rate increases by
x%, then packet arrival rate
should decrease by sqrt(x)%
3 October 2008 NUS CS5229, Semester 1 2008/09 16
17. Does Not Work:
when packet drop rate is constant
3 October 2008 NUS CS5229, Semester 1 2008/09 17
18. Does Not Work:
packet might be dropped by
earlier router
3 October 2008 NUS CS5229, Semester 1 2008/09 18
19. Does Not Work:
A flow has an incentive to start
with high throughput
3 October 2008 NUS CS5229, Semester 1 2008/09 19
20. Approach 3:
Flows with
Disproportionate
Bandwidth
3 October 2008 NUS CS5229, Semester 1 2008/09 20
21. A flow should share 1/n of
total bandwidth
3 October 2008 NUS CS5229, Semester 1 2008/09 21
22. When congestion is low
(packet drop rate is low),
skewness is OK.
3 October 2008 NUS CS5229, Semester 1 2008/09 22
23. Condition 1: If a flow’s
bandwidth is more than ln(3n)/n
of the aggregate, then it is using
disproportionate share.
(ln(3n)/n : magic)
3 October 2008 NUS CS5229, Semester 1 2008/09 23
24. Condition 2: If a flow’s
bandwidth is more than
For MSS=512 and RTT=0.05s
3 October 2008 NUS CS5229, Semester 1 2008/09 24
25. If a flow’s bandwidth is more
than ln(3n)/n of the aggregate
flow bandwidth, then it is using
disproportionate share.
(ln(3n)/n : magic)
3 October 2008 NUS CS5229, Semester 1 2008/09 25
26. Does Not Work:
flows with short RTT will be
considered as disproportionate
3 October 2008 NUS CS5229, Semester 1 2008/09 26
27. Does Not Work:
the only flow with sustained
demand (long live) will be
considered as disproportionate.
3 October 2008 NUS CS5229, Semester 1 2008/09 27
28. Sally Floyd and Van Jacobson
“Random Early
Detection Gateway for
Congestion Avoidance”
TON, 1993
3 October 2008 NUS CS5229, Semester 1 2008/09 28
38. One queue per flow
3 October 2008 NUS CS5229, Semester 1 2008/09 38
39. Drop what?
Drop arriving packets from
flow i when queue i is full
3 October 2008 NUS CS5229, Semester 1 2008/09 39
40. Send what?
Each flow takes turn --
send the packet at the head
of the queues in a round
robin manner.
3 October 2008 NUS CS5229, Semester 1 2008/09 40
41. Advantages of
FIFO and Drop Tail
3 October 2008 NUS CS5229, Semester 1 2008/09 41
43. Scale well
(no per-connection states)
3 October 2008 NUS CS5229, Semester 1 2008/09 43
44. Reduce delay for a bursty
connection
(e.g. VoIP)
3 October 2008 NUS CS5229, Semester 1 2008/09 44
45. Problems with
FIFO and Drop Tail
3 October 2008 NUS CS5229, Semester 1 2008/09 45
46. Problem 1
Bias against bursty traffic
burstiness increases chances that the
queue will overflow
3 October 2008 NUS CS5229, Semester 1 2008/09 46
47. Problem 2
Global synchronization
connections reduce their windows
simultaneously, lowering utilization.
3 October 2008 NUS CS5229, Semester 1 2008/09 47
48. Problem 3
Queue size
higher bandwidth needs longer
queue, increasing delay. TCP tries to
keep the queue full
3 October 2008 NUS CS5229, Semester 1 2008/09 48
49. Problem 4
No isolation against
unresponsive flows
3 October 2008 NUS CS5229, Semester 1 2008/09 49
59. Drop what?
Drop arriving packet
randomly if queue is longer
than a threshold
3 October 2008 NUS CS5229, Semester 1 2008/09 59
60. Early random drop avoids
congestion (full queue) by
dropping packets before queue
is full.
3 October 2008 NUS CS5229, Semester 1 2008/09 60
61. RED
Random Early Detection
3 October 2008 NUS CS5229, Semester 1 2008/09 61
62. Drop what?
Drop arriving packet randomly
if average queue length is
above a threshold
3 October 2008 NUS CS5229, Semester 1 2008/09 62
63. Differences 1: Use average
queue length instead of
instantaneous length to absorb
transient congestion.
3 October 2008 NUS CS5229, Semester 1 2008/09 63
64. Differences 2: Dropping
probability should change
dynamically depending on
queue length.
3 October 2008 NUS CS5229, Semester 1 2008/09 64
65. Dropping Probability
1
Average Queue Length
3 October 2008 NUS CS5229, Semester 1 2008/09 65
66. foreach incoming packet X
calc average queue length
if minth < average < maxth
calc p
drop X with probability p
else if average > maxth
drop X
3 October 2008 NUS CS5229, Semester 1 2008/09 66
67. (Instead of dropping packets, we
can also set the ECN bit to
indicate congestion)
3 October 2008 NUS CS5229, Semester 1 2008/09 67
68. How to calculate average queue
length?
How to calculate drop probability?
How to set thresholds?
3 October 2008 NUS CS5229, Semester 1 2008/09 68
69. We can use exponentially
weighted average. On every
packet arrival:
3 October 2008 NUS CS5229, Semester 1 2008/09 69
70. Large wq : A burst of packets will
cause avg to increase too fast, hit
the max threshold
Small wq : avg increases too slowly
and we are unable to detect initial
stage of congestions.
3 October 2008 NUS CS5229, Semester 1 2008/09 70
80. How to calculate average queue
length?
How to calculate drop probability
How to set thresholds?
3 October 2008 NUS CS5229, Semester 1 2008/09 80
81. maxth - minth should be sufficiently
large otherwise average queue
size can oscillate beyond maxth
“need more research” for
optimal value.
3 October 2008 NUS CS5229, Semester 1 2008/09 81
95. Conclusion:
RED increases throughput,
reduces delay, controls average
queue sizes, reduces global sync
and is fairer to bursty traffic. It is
deployed in routers today.
But careful tuning of parameters
is needed.
3 October 2008 NUS CS5229, Semester 1 2008/09 95