Bandwidth Scheduling and Provisioning in Access and Wide Area Networks

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Bandwidth Scheduling and Provisioning in Access and Wide Area Networks

Bin Wang

Department of Computer Science and Engineering

Wright State University

Dayton, OH 45435

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Outline

Bandwidth scheduling in Ethernet Passive Optical Network (EPON)

Sliding scheduled traffic model

Bandwidth scheduling over a point-to-point WDM link

Bandwidth provisioning in WDM networks Look-ahead scheduling of a set of demands Dynamic scheduling of a demand

Summary

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Access Network - Passive Optical Networks

A single fiber is used to support multiple customers – 20km

No active equipment in the path highly reliable

Optical line terminal (OLT) in central office, which connected to the rest of the Internet

Optical network unit (ONU) on customer premises

Both upstream and downstream traffic on ONE fiber (1490nm down, 1310nm up)

EPON: Ethernet based PON draft designed by IEEE 802.3ah 1000 Mbps downstream and 1000

Mbps upstream

Rest of Internet

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PON Topologies

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Why PON?

Reduced OpEx: passive network High reliability Reduced power expenses Shorter installation times

Reduced CapEx: 16-128 customers per fiber 1 Fiber + N transceivers

Scalable CO equipment shared new customers can be added

easily as the network grows Bandwidth is shared existing customer bandwidth can be

changed on demand

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Downstream Traffic - Broadcast

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Upstream Traffic -Shared

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Bandwidth Scheduling - Upstream

TDMA – a frame consists of N time slots N ONUs Each ONU is assigned a dedicated time slot Traffic arriving to ONU is buffered till correct time slot for this

ONU arrives Traffic will be sent at full link speed upstream

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Pros and Cons

Pros simple Dedicated bandwidth

Cons Fixed frame (N time slots) Potential long delay No statistical sharing – low utilization Loss due to buffer overflow; using a larger buffer increases

delay

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Dynamic Polling-based Bandwidth Scheduling

Use OLT polling ONUs to deliver data encapsulated in Ethernet frames from ONUs to OLT

To avoid walk times associated with polling (due to large RTT), polling requests and data transmission need to be properly scheduled Interleaved polling with adaptive transmission cycle time

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OLT maintains polling table # of bytes in ONU’s buffer

• requested transmission window

RTT to each ONU OLT issue Grant message to

ONU Granted transmission window

ONU transmits up to the granted transmission window

At the end of transmission, ONU issues a Request to OLT # of bytes in ONU’s buffer

grantrequest

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OLT properly times the sending of next Grant message to ONU BEFORE receiving transmission from ONU, given RTT to ONUs Transmission window of

previous Grant Guard time needed

Such that The next transmission from

ONU is received by OLT right AFTER the receipt of the last bit of previous ONU transmission

grantgrant

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Upon receipt of transmission from ONU, OLT Updates RTT to ONU Updates # of bytes requested

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The next transmission from ONU is received by OLT is right AFTER the receipt of the last bit of previous ONU transmission + some guard time

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Maximum Transmission Window Size (Wimax)

Fixed: based on SLA for each ONU Dynamic: based network conditions Wi

max determines guaranteed bandwidth available to ONU-i max polling cycle

• Large cycle increased delay for all packets

• Small cycle more bandwidth wasted by guard time polling cycle is variable depending on requested window

sizes or network traffic condition

• excessive bandwidth distributed to highly loaded ONUs

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Maximum Transmission Window Size (Wimax)

Fixed service Ignore the requested window size and always grants the

max window TDMA Limited service

Grants the requested # of bytes, but no more than Wmax

Constant credit Add a constant credit to the requested window size Granted window size = requested window size + x Reduce average delay

Linear credit Granted window size = requested window size + credit Size of credit proportional to the requested window Longer burst in last cycle is likely to continue in the next

cycle

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Other Scheduling Algorithms for EPON

Differentiated services QoS for multiple classes of services (voice, data,

video, etc)

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Traffic Models

Following traffic models in open literature: static traffic model

• all demands are known in advance and do not change over time

dynamic random traffic model

• a demand is assumed to arrive at a random time and last for a random amount of time

admissible set model

• demands are from some prescribed traffic matrices incremental traffic model

• demands arrive sequentially. Once the demand is accommodated, the demand remains in the network indefinitely

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Motivation

Many US DOE large-scale science applications must deliver Gbps throughput at scheduled time durations

These applications require provisioning of scheduled dedicated channels or bandwidth pipes at a specific time with certain duration

Bandwidth leasing market Customers need bandwidth only for a limited period of time Limited-time leasing of bandwidth possible in the future

These scheduled capacity demands are dynamic demands only last during the specified intervals they are not entirely random

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New -- Sliding Scheduled Traffic Model

A demand (s, d, n, ℓ, r, )

s: source d: destination (or a destination set) n: capacity requirement : duration, or lasting time [ℓ, r]: time window during which demand of duration

must be satisfied Example: (s, d, 1, 10:00, 13:00, 60 minutes)

l r

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Bandwidth scheduling over a point-to-point WDM link under sliding scheduled traffic model

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Problem Settings A single fiber link with W wavelengths

Time is slotted with T time slots over a day: 0, 1, …, T-1

Demands require lightpaths periodically repeated every day denoted by (a, b, L) – a discrete version of sliding scheduled model

• starts in [a, b] and lasts L time slots (L<T)

• demand satisfied in [a, b+L]

• time flexibility defined as |[a,b]|-1

Lightpath service (w, s, L): wavelength w is used for a duration of L time slots start from s per day (L<T)

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Problem definition

Given a batch of lightpath demands, assign them lightpath services so that at any time there is at most one lightpath service per wavelength

W=2; T=8; Demands = (4,6,4) (3,3,2) (7,1,3) (1,3,4)

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Traffic Constraint for Schedulability Conditions

Virtual Packet (VP) model treat a demand (a, b, L) as a virtual packet that “arrives” at a

and has a “transmission duration” or (work) of L (σ, ρ): ρ is a measure of the average traffic (demand) rate,

and σ is a measure of the traffic (demand) burstiness ρ ≤ W A(t): work of virtual packets that arrive at time t (σ, ρ) constrained traffic, total work in [x, y]

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Theorem 1: Schedulability Condition when no lightpath wraps around at the end of [0, T-1]

Suppose the batch of lightpath requests are (σ, ρ) constrained, Lmax is the max lasting time, and let:

And A(t) = 0 for all

(i.e., there is no virtual packet arrival in the last

time slots)

Then there is an assignment for the lightpath requests if their time flexibility is at least f

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Theorem 2: General Schedulability Condition

Suppose the batch of lightpath requests are (σ, ρ) constrained, and

Let

Then there is an assignment for the lightpath request if their time flexibility is at least f

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Heuristic Scheduling Algorithms

First Come First Serve (FCFS) Lowest valued wavelengths are used first Demands with earlier arrival times are scheduled first, ties are

broken randomly

Earliest Deadline First (EDF) Lowest valued wavelengths are used first Demands with the earliest deadline, b+L, is scheduled first

• b+L is the last possible time slot for the end of the lightpath

Both schemes tend to assign lightpaths close to time 0 which creates peak bandwidth demand at time 0

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Lowest wavelength, maximum duration (LWMD) Wavelengths are filled with lightpath requests one wavelength

at a time starting from wavelength 0 When filling wavelength k, demands that have longer

durations are scheduled first, ties broken randomly (a, b, L): start times are considered in the start interval [a, b]

beginning with a

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Lowest wavelength, Fixed (LWFixed) Wavelengths are filled with lightpath requests one wavelength

at a time starting from wavelength 0 Wavelength k is filled starting from time = 0 Choose the longest unassigned request (a, b, L) that could

start at time t and assign it starting from t Continue to fill the wavelength from t+L If no such request, then continue filling the wavelength from

time t+1 May create peak bandwidth demand at time 0

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Lowest wavelength, Continuous (LWCont) Wavelengths are filled with lightpath requests one wavelength

at a time starting from wavelength 0 Wavelength k >0 is filled by starting at a time t that depends

on how wavelength k-1 was filled

• If wavelength k-1’s last request was assigned time slots [x,y], then wavelength k is filled starting from y+1

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Simulation Call blocking rate

Ratio of # of calls blocked over # of calls

Traffic blocking rate Ratio of the work of blocked lightpath requests over the

work of all lightpath requests

Scenarios: request duration evenly distributed in [1,31] expected duration of lightpaths = 16 Earliest start time for a demand randomly distributed Blocking scenario

W=30,T=64, 114 requests Nonblocking scenario

W not limited, T=64, 128 requests

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Blocking scenario - call

FCFS, EDF high blocking rates

LWCont has about the lowest blocking rates over all flexibility times

Blocking rates decreases as time flexibility increases

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Blocking scenario - traffic

FCFS, EDF high blocking rates

LWCont has about the lowest blocking rates over all flexibility times

Blocking rates decreases as time flexibility increases

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Nonblocking scenario

Minimal # of wavelengths needed so that there is no blocking (Cmin= , a lower bound on the # of wavelengths needed,

M is work of the lightpath requests

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Result Summary

Functions of the time flexibility f FCFS and EDF have high blocking rates LWCont has about the lowest blocking rates over all

flexibility times Blocking rates decreases as time flexibility increases

except for LWFixed when the time flexibility is around 32

LWMD and LWCont require minimal number of wavelengths

LWCont performs the best

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Summary of Scheduling in P2P Link

Scheduling over a single WDM link under a flexible traffic model

Assigning periodic lightpath services which allow some time flexibility

Schedulability conditions for a set of demands Heuristic scheduling algorithms

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Bandwidth provisioning in WDM networks Look-ahead scheduling of a set of demands with

sub-wavelength capacity under sliding scheduled traffic model

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Space-Time Traffic Grooming Problem

Given a set of sliding scheduled traffic demands M, properly place demands within their time windows, route and groom (by finding a route and assigning a proper

wavelength to each demand in M) such that

non-blocking case

network has enough resources to accommodate all the demands in M to meet their specifications (i.e., capacity and schedule requirements)

goal is to minimize total network resources used

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Space-Time Traffic Grooming Problem

blocking case

network does not have enough resources to accommodate all the demands as specified

goal is

• to minimize the number of demands to be rearranged (i.e., to minimize the subset of demands that may have their starting time changed in order to have all the demands in the set M accommodated

• to minimize of total network resources used

demand priority can also be considered if necessary

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Time Conflict & Resource Conflicts

Temporal conflicts: Time conflicts of a set of scheduled demands M Demands may overlap in time Demands that are disjoint in time allow resource reuse

Spatial conflicts: Resource conflicts Routes of demands may overlap If not enough resources are available, conflicts result Some demands may not be schedulable because of lack of

resources

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Interval Graph Modeling of Time Conflict Reduction

85

323

0 10

3 28

2 116 9 12 18

Tight node: 2 x 8 > 10

Strong edge

Weak Edge

Loose node:2 x 5 < 25

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Lemma: no strong edge connects two loose nodes.

Theorem: let v be a loose node, A(v) be the set of nodes connected to v with strong edges, then all nodes in A(v) are tight nodes and are connected by strong edges pair wise.

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Time Conflict Reduction Algorithm

Use an interval graph to model time conflicts among scheduled demands

Identify time conflicts that can be avoided

Remove time conflicts in a greedy manner to obtain proper placement of demand intervals within their allowed time windows

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Time Conflict Reduction Algorithm

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Performance of Time Conflict Reduction Algorithm

Demand length 10-90% of time window size

Weak time correlation among demands

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Medium time correlation among demands

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Strong time correlation among demands

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Demand length 10-100%of time window size


Different time correlation among demands

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Time Window Based Routing and Grooming Algorithm

Divide a set of scheduled demands into subsets called time windows

Demands in a time window have time conflicts pair wise

Schedule demands in a time window according to demands’ decreasing resource requirements Using a modified Dijkstra’s algorithm on a wavelength

layered graph

If a demand is blocked due to unavailability of resources, rearrange the schedule of the demand Schedule the demand earlier or later in time when the

required resources are available

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Demand Set Division

Time

Time Window 3Time Window 2Time Window 1

r1

r2

r3

r4

r5

r6

r7

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Time Window Based Grooming Algorithm

DS : set of straddling demands

TWi : set of demands in a time window i

DR : set of demands need to be rearranged

Space Time RWA Algorithm (G, M)

run Time Window Division Algorithm (M);

run Greedy Time Window Grooming Algorithm (G, DS);

run Greedy Time Window Grooming Algorithm (G, TWi) for all TWi;

if (DR not empty), run Rearrange RWA Algorithm (DR);

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Performance Evaluation

Time correlation of a demand set after time conflict reduction characterize the extent of conflicts among demands in the

time domain weak, medium, strong

Demand sets contain 50-400 scheduled bidirectional demands

30 wavelengths per link

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11222

292

75

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209

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NSFNET

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Traffic Grooming: Results - I

Grooming factor g varies from 4 to 32 Given a grooming factor g, capacity units of a

demand drawn from [1, 2], [1, 4], …, [1, g] Metric: Total # of wavelength-links and max # of

wavelengths used on a link increase when # of demands increases when average demand capacity increases

Grooming is more effective when average demand capacity is smaller relative to grooming factor

Stronger time correlation negatively impacts effectiveness of grooming

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Traffic Grooming: Results - II

When grooming factor g increases, the amount of resources used decreases and levels off when g becomes large

The decrease in resources used is more significant when the demand time correlation is stronger

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Total number of wavelength-links vs number of demands, weak correlation

Grooming factor g = 16 Given a grooming factor

g, capacity units of a demand drawn from [1, 2], [1, 4], …, [1, g]

Total # of wavelength-links used increase when # of demands

increases when average

demand capacity increases

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Total number of wavelength-links vs number of demands, medium correlation






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Total number of wavelength-links vs number of demands, strong correlation






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Max number of wavelengths vs number of demands, weak correlation



Max # of wavelengths used on a link increase when # of demands



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Max number of wavelengths vs number of demands, medium correlation






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Max number of wavelengths vs number of demands, strong correlation






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Total number of wavelength-links vs Grooming factor

# of demands = 350 Grooming factor g= 1, 4,

8, ... 32 Total # of wavelength-

links used decrease when grooming

factor g increases levels off when g

becomes large The decrease in

resources used is more significant when the demand time correlation is stronger

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Max number of wavelengths vs Grooming factor

# of demands = 350 Grooming factor g= 1, 4,..

32 Max # of wavelengths

used on a link decrease when grooming

factor g increases levels off when g

becomes large The decrease in

resources used is more significant when the demand time correlation is stronger

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Dynamic scheduling of a demand under the sliding scheduled traffic model

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Problem

Given a demand (s, d, n, ℓ, r, ), find a route that has at least n units of bandwidth in a time interval of length at least in [ℓ, r]

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Dynamic Scheduling Algorithm

Divide time into time slots

Consider time intervals of length in [ℓ, r] starting from ℓ: [ℓ, ℓ+ ], [ℓ+1, ℓ+ +1], [ℓ+2, ℓ+ +2], …

Within a time interval, find a shortest path with at least n units of bandwidth given current network resource state info h-hop optimal routing algorithm:

• A modified Bellman-Ford algorithm

• Find the maximum available bandwidth path with at most h hops

If such a path is not found, try next time interval

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Summary

Bandwidth scheduling in EPON

Sliding scheduled traffic model

Bandwidth scheduling over a point-to-point WDM link

Bandwidth provisioning in WDM networks Look-ahead scheduling of a set of demands Dynamic scheduling of a demand

Documents

Bandwidth Scheduling and Provisioning in Access and Wide Area Networks