Quality of Service in Telecommunication Networks
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1 Quality of Service in Telecommunication Networks Åke Arvidsson, Ph.D. Ericsson Core Network Development, Sweden
2 Main Message Traffic theory: QoS is (partly) about congestion. Congestion is partly caused by temporary overload. Temporary overloads can be handled by statistics. QoS can be controlled by statistics. Typical issues for regulators: Is an operator serious about quality of service? What is the fair price to carry additional traffic? Typical issues for operators: How should a network be engineered? Can I just go for over-provisioning! Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 2
3 Overview Background and motivation. The significance of variations. Big variations means expenses. Relationship to shared networks. Small variations means savings. Relationship to shared networks. Hot topic: Relevance to IP and integrated services. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 3
4 Background (1) Voice and data traffic exhibit significant variations. Variable demand but fixed resources. Traffic may be lost. Traffic may be delayed. Traffic may be subject to other impairments. Mathematically tractable by traffic theory. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 4
5 Background (2) Can be used to control quality of service. Essential part of competition. Competitive advantage. Add value to services. Essential part of regulation/competition. Fair treatment of new players by former monopolies. Fair criteria for customers to choose service provider. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 5
6 Background (3) Significance: Society: Reliable services. Operators: Stable systems, fair competition. Users: Value for money. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 6
7 Background (4) Requirements: Prerequisite: The busy hour concept. Delays: dialing tone, through connection, speech,... ITU-T, ETSI and others. Blocking: lost traffic,... Typically operator dependent. The above apply to voice services. Data services specified but less used. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 7
8 Variations The problem lies in variations: Ideal: Fixed inter-arrival times and fixed service times: Real: Variable inter-arrival times (and variable service times): Time Time Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 8
9 Traffic Theory Statistical analysis of service systems: Waiting times. Example: Dialing tone delay in a switch. Congestion probabilities. Example: Traffic rejected from a trunk. Utilisations. Example: Load on a processor or link. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 9
10 Quality of service vs. supply Probability of sufficiency 1,0 0,8 0,6 0,4 0,2 Only 0.53 Better QoS takes more capacity No oversupply 0,0 0,6 0,7 0,8 0,9 1,0 1,1 1,2 1,3 1,4 Capacity/Demand Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 10
11 Conclusions (1) Dimension for average: Variations unaccounted for. Related impairments ignored. No control of QoS! Traffic theory necessary for QoS. Correct provisioning aims at targeted QoS. Over-provisioning is more than that, not just more than the mean. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 11
12 Randomness (1) More randomness means more problems. More delay, higher blocking, etc. Examples: Circuit oriented traffic. Erlang: Number of parallel connections. Packet oriented traffic. Load: A metric between zero and unit. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 12
13 Blocking and Variations Blocking probability erlang 10 erlang 1 erlang Traffic variation (peakedness) Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 13
14 Waiting Time and Variations 200 Mean waiting time Load 0.8 Load 0.5 Load Service time variation Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 14
15 Conclusions (2) More randomness means more problems. Split costs depend on traffic variations. Simple dimensioning based on factors: Correct answers for (at most) one working point only. Over-dimensioning is not as simple as it may sound. How much is too much? Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 15
16 Randomness (2) Less randomness means less problems. Less delay, lower blocking, etc. Examples: Circuit oriented traffic. Erlang: Number of parallel connections. Packet oriented traffic. Load: A metric between zero and unit. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 16
17 Circuits per Erlang Circuit oriented traffic. How many circuits are needed for one Erlang? Depends on performance and the scale of the system! Circuits per erlang High grade of service (0.5%) Low grade of service (5%) Traffic Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 17
18 Packet Delay Packet oriented traffic. What is the delay for a packet/signal? Depends on the load and the scale of the system! Delay (s) Load 30 parallel 64 kbps links; random/optimal sharing (20 octet packets) One kbps link (20 octet packets) Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 18
19 Conclusions (3) Less randomness means less problems. Sharing networks reduces marginal costs. The bigger the better. One big system is better than many small systems. Law of large numbers. The more samples, the less variation. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 19
20 Classic Traffic Theory Circuit switched services. Erlang-B. Pleased to meet you! Wide range of extensions. Packet switched services. Signal networks. Early arpanet community. How about present IP? Before: Best effort only. Now: QoS required. Agner Krarup Erlang ( ) KTAS, Copenhagen, Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 20
21 Quality of Service in IP Profitability: Charging on QoS to boost income. Integration: Service integration to cut expenses. Efficiency: Utilise expensive wireless resources. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 21
22 Recent Developments (1) Data (IP) traffic models: Packets are like calls in telephone traffic. Poisson process. Burstier processes. Ethernet (and other) measurements: High spread (heavy tails). Slow variations (long range dependence). Time scale independence (self similarity). Earlier models far too optimistic! Forget the old economy, the new economy is here! Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 22
23 Results (1) Queue 10,000 Heaviest tail Lightest tail No queue Load 50% Load 71% Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 23
24 Recent Developments (2) Objections: Most data traffic is subject to flow control. Queues cannot grow without bounds. TCP applies dynamic flow control to maximise throughput. Links may operate near (local) saturation. Earlier diagram based on measured traffic. Measurements are reality but the experiments are not real since flow control is frozen. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 24
25 Results (2) Earlier diagram Active flow control Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 25
26 Conclusions (4) New discoveries not so dramatic. However, traffic and systems interaction complicates: Measurements: What does link load mean to user performance? Modeling: Simple steady state Poisson not generally applicable. Dimensioning: Traffic and performance mutually dependent. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 26
27 Recent Developments (3) Research at Ericsson: Mathematical methods. Performance criterion: Downloading time (useful throughput). Examples: Bottleneck identification and removal. Peak factor calculations. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 27
28 Example: Bottlenecks (1) Access rate: Packet drop: Propagation: Window size: File size: Throughput: 256 kbps 2 % ms 4 packets 10 packets kbps + % Packet drop Access rate File size Window size Propagation Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 28
29 Example: Bottlenecks (2) Access rate: Packet drop: Propagation: Window size: File size: Throughput: 256 kbps 2 % 50 ms 4 packets packets kbps + % Packet Packet drop drop Access Access rate rate Window Window size size Propagation Propagation File File size size Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 29
30 Example: Bottlenecks (3) Access rate: Packet drop: Propagation: Window size: File size: Throughput: kbps 2 % 50 ms 4 packets 20 packets kbps + % Packet Packet drop drop Access Access rate rate Window Window size size Propagation Propagation File File size size Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 30
31 Example: Peak Factor (1) Network: Packet loss probability 1%. Round trip time 300 ms. Users: Access rate: 33% 64/128 kbps; 67% 64/384 kbps. Packet size: 25% 536 bytes; 75% 1540 bytes. Window size: kbyte. Traffic: 170 kbyte/busy hour. WAP, WWW, MMS, Mail. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 31
32 Example: Peak Factor (2) Dimension core network: A connection sees 5 links in tandem end-to-end. 5% additional RTT acceptable (15 ms means 3 ms per link). Different links have different load. One peak factor? Twice the average (peak factor 2, load 50%). Is this too much (over-dimensioning)? Or do we need more (e, π,...)? Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 32
33 Example: Peak Factor (3) Peak factor Sharing users Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 33
34 Conclusions (5) IP (TCP) fractal properties often overrated. Relationship to variation conceptually the same. This does not mean that dimensioning is simple: Traffic and network are mutually dependent. Traffic exhibits large variations over time. User perceived performance different from directly measurable. A magnitude of parameters means unclear bottlenecks. High burstiness may still enforce low utilisation. Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 34
35 Main Message Traffic theory: QoS is (partly) about congestion (loss and delay). Congestion is partly caused by temporary overload (variations). Temporary overloads can be handled by statistics (traffic model). QoS can be controlled by statistics (queuing theory). Typical issues: Is an operator serious about quality of service? Goals! What is the fair price to carry additional traffic? Depends! How should a network be engineered? Methods! Can I trust over-provisioning! No! Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 35
36 Ericsson AB 2005 ÄS/EAB/UKT/T Åke Arvidsson 36
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