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MARS: The Magnet II Real-Time Scheduling Algorithm

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A real-time scheduling algorithm, MARS, for Asynchronous Time Sharing (ATS) based ... Slotted channel: Cycle. C I II III Class. MC MI MI MIII Length of subcycle ... – PowerPoint PPT presentation

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Title: MARS: The Magnet II Real-Time Scheduling Algorithm


1
MARS The Magnet IIReal-Time Scheduling Algorithm
  • Hyman, Lazer, and Pacifici
  • 1991 ACM ?
  • Presented by
  • ???

2
Abstract
  • A real-time scheduling algorithm, MARS, for
    Asynchronous Time Sharing (ATS) based switching
    modes.
  • 3 classes of traffic sources
  • real-time video sources
  • guaranteed maximum end-to-end delay SI for all
    cells
  • real-time voice sources
  • guaranteed maximum end-to-end delay SII
  • ? cell loss rate
  • maximum average cell loss gap ?
  • data traffic
  • guaranteed minimum average throughput
  • guaranteed maximum average time delay

3
1. Introduction
  • Goal
  • To evaluate the performance of Asynchronous Time
    Sharing (ATS) based integrated networks
  • ATS based integrated network is novel
  • the concept of quality of service explicitly
    appears in the design specification at both the
    edge and the core of the network
  • the core of the network makes a distinction
    between traffic classes
  • it is necessary in order to efficiently provide
    QoS
  • it is not a requirement of ATM based integrated
    networks

4
  • The study of this paper is limited to
  • a switching nodes taken in isolation
  • This paper
  • presents the performance of the MAGNET II
    scheduling algorithm
  • compares it with the performance of other known
    algorithms, with
  • quantitative data
  • implementation complexity and knowledge

5
2. Review of Past Work
  • The early literature on real-time scheduling
    relates to an operation systems environment,in
    which arriving tasks need to be scheduled at an
    available processor, such that each task is
    completed by its deadline
  • Assume
  • a priori knowledge of all of the arrival times,
    processing times, and deadlines for the entire
    set of tasks to be scheduled

6
  • Various criteria for optimization have been used
  • Cheng and Stankovic 3
  • Liu and Laylan
  • Tobagi and Peha
  • Baker
  • Chipalkatti et al.
  • Ferarri and Verma
  • Sriram

7
3. The Architecture of the Switching Nodes
  • 3 elements
  • Input Buffers
  • Switching Fabric
  • Output Buffers
  • Fundamental Requirement
  • The transfer of information from its inputs to
    its output such that time delay and blocking
    sensitive performance criteria are met.

8
  • 4 traffic classes Class I, II, III, and C
  • 4 input buffers / access point.
  • Traffic arriving at an access point is stored,
    according to its class, in one of the four
    buffers.
  • ? Minimum Average User ThroughputT Maximum
    Average User Time Delay

9
3.1 The Multi-class Traffic Model
  • Contention Packet Loss
  • Clipping Network Congestion ? Delay gt Max
    Delay, SI or SII
  • Blocked Buffer Overflow ? Packet discarded
  • For Class II
  • ? Contention packet loss ? Upper bound
    on the average number consecutively lost packet

10
3.2 The Link Scheduling Model
  • ATS-based switching node
  • was first implemented on MAGNET II
  • and also adopted by TeraNet.

Buffer ManagerLink Scheduler
3 input links
3 output links
3 x (3 x 4)
J x (3 x 1)output buffers
J x (3 x 1)input buffers
Ring Switch Fabric
11
  • Model the output link of TeraNet
  • A queuing system with 4 buffers 1 server
  • At server
  • Fixed capacity - C bits/secFixed cell size - D
    bits/cell
  • Service rate ? C/D cells/sec
  • Slotted channel
  • MX dynamically adjusted
  • Fixed Cycle Length H ? M

12
3.3 Real-Time Traffic Source Models
  • Video
  • Fixed frame duration F 62.5 ms
  • Constant cell rate CI 1M bps
  • Active period ?active 10ms, 40ms
    (uniformly distributed)
  • E?(t,video) E?active CI / F 4M bps

13
  • Voice
  • On-off source
  • Fixed frame duration F 62.5 ms
  • Constant cell rate CI 1M bps
  • Active period ?onInactive period ?off
    (exponentially distributed)
  • E?on 352 msE?off 650 ms
  • E?(t,voice) E?on / (E?on E?off) 22.5K
    bps
  • Data
  • Poisson distributed
  • E?(t,data) 1M bps

14
4. MARS The MAGNET II Real-Time Scheduling
Algorithm
  • Assume 1 server 3 queues (for Class I, II, and
    III only)
  • Scheduler Operations
  • H is kept constant
  • 2 schedules (lists) number of Class I and II
    cells to be transmitted during the future cycle
  • the lists are updated at the end of each
    cycle(new cells arrived during the previous
    cycle)
  • only the min. amount of resources is allocated to
    each class
  • QoS requirements of Class I must always be met
  • exceeding Class II cells are clipped
  • the remaining bandwidth (if available) is
    allocated toClass III traffic.

15
  • To determine the bandwidth allocation
  • At the end of each cycle
  • The scheduler generates 2-dimensional (hI gt hII)
    schedules, with hk ? Sk ? / H ? -
    1 (k1,2)
  • 2 logical partitions (bins) for Class I and II
  • Mki,j the number of Class k cells that at the
    beginning of cycle i are predicted to be
    scheduled in the next hk cycles, j lt hk.

16
  • The updating process for Class I traffic
  • MIi,j min(MIi-1,j1OIi,j1,H)
  • OIi,j max(0,MIi-1,j1OIi,j1-H)

17
  • The updating process for Class II traffic
  • MIIi,j min(MIIi-1,j1OIIi,j1,Rj,j)OII
    i,j max(0,MIi-1,j1OIIi,j1-RI,j)where
    RI,j H- MIi,j j 0,1,,hI H -
    (1/hI) ? MIi,j j hI,, hII-1

18
5. Experimental Results
  • Compare MARS with
  • SPS (Static Priority Scheduling)
  • MLT (Minimum Laxity Threshold)
  • Assume
  • Link capacity C 100M bps
  • Fixed cell size D 1024 bits/cell
  • Fixed cycle size H 39 cells
  • QOS SI,SII,?,?,T
  • SI,SII max. delay of Class I and II traffic (in
    seconds)
  • ? percentage of contention packet loss
  • ? max. gap between consecutive packet loss (in
    cells)
  • T max. average user time delay (in seconds)

19
  • Experiment 1
  • KI KII 0..20 calls, KIII 0..80 Poisson
    data callsH 39 cellsQOS 1ms, 1ms, 0.001,
    5.0, 1msFixed KI and KIII ? Find KII
  • For KI lt 10 same performance of the 3
    algorithms.For KI gt 10 MARS and MLT allow more
    KII users.

20
  • Let KIII 0 in Experiment 1
  • KI increases ? KII decreases
  • MARS/MLT gt SPS
  • When KI 16
  • MARS/MLT KII 500 voice calls.
  • SPS KII 0

21
  • Experiment 2 (for smaller delay)
  • H 9 cellsQOS 400?s, 800?s, 0.001, 5.0, 1ms
  • Same performance of the 3 algorithms.
  • Little effect for SPSDrastic decreases for MARS
    and SPS

22
  • Conclusions from EXP 1 and 2
  • (1) When Class I and II requires smaller delays,
    the SPS is very nearly optimal. (Same in
    EXP. 1 and 2)
  • (2) When Class I and II have larger traffic, a
    significant gain in utlilization can be
    achieved by usin one of the more complex
    algorothms, MARS or MLT. (KI gt 10)

23
  • Experiment 3 (for correlated and bursty traffic)
  • CI 10Mbps ? 50Mbps
  • ?active 10ms, 40ms ? 2ms, 8ms
  • E?active 25ms ? 5ms
  • F 62.5ms
  • E?(t,video) E?active CI / F 4Mbps
  • Only when both Class I and II loads are low is
    the performance of SPS, MARS, and MLT the same.

24
  • Let KIII 0 in Experiment 3
  • Worse performance when bursty traffic.
  • Improved by using MARS and MLT.

25
6. Conclusions (1)
  • When the network load is highly correlated (video
    or on-off voice), ? MARS and MLT improved the
    performance considerably.
  • When the cells are multiplexed together, ?
    their correlation decreases as the number of
    sources increases. ?The improvement due to
    MARS and MLT is considerably smaller.

26
6. Conclusions (2)
  • Resources allocation
  • SPS Class I traffic first
  • smaller delay for Class I traffic
  • MARS and MLT that is necessary to satisfy
    Class I QoS parameters
  • maximum delay for Class I traffic
  • Class II and III have more resources, and thus,
    the multiplexer has a greater network
    utilization factor.

27
6. Conclusions (3)
  • MARS has at most twice the computation time of
    SPS, while MLT runs much slower.
  • MARS and MLT are closed to the upper bound for
    network utilization.
  • Additional knowledge requirement for MLT leads to
    substantial increase in complexity without
    proportional improvement in network utilization.
  • MARS is recommended for real-time scheduling
    algorithms in ATS-based nodes.
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