Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support...

Florida State University Zhenhai Duan 1

BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services

Zhenhai Duan

Karthik Parsha

Department of Computer Science

Florida State University

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Agenda

• Core stateless networks for per-flow guaranteed services– Introduction and motivation

• BCSQ: Bin-based Core Stateless Queueing

• Performance analyses and simulation studies

• Summary

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Core Stateless Networks for Per-Flow GS

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How Core Stateless Networks Work?• Many core stateless systems

– Core Jitter Virtual Clock (CJVC)– Virtual Time Reference System (VTRS)

• Core stateless virtual clock (CSVC)• Core stateless earliest deadline first (CS-EDF)

– Core Stateless Guaranteed Rate (CSGR)– Coordinated Network Scheduling (CNS)

• All work by emulating corresponding stateful scheduler– Scheduling packets based on virtual finish times

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Why Core Stateless?

• In stateful networks (GPS, WFQ, …), routers– Maintain per-flow state for scheduling/admission control– Perform per-flow packet classification– Perform per-flow queueing– Perform per-flow scheduling

• Core stateless networks– Eliminate needs for per-flow operations and state– Decouple control plane from data plane

• Routers focusing on data forwarding• Sophisticated admission control on bandwidth brokers

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Core stateless scheduling still expensive• Sort incoming packets based on virtual finish times

– – Where N is number of packets in scheduler

• How to overcome this problem?– Coarser grained packet sorting

– Using bins to queue packets with close virtual finish times

• Conceptually simple, however– Core stateless schedulers emulate stateful ones

• Can we still emulate them using bins?

– Goal is to provide per-flow GS• Can we still achieve this goal using bins?

– Management issues of bins• How many bins should we have to avoid overflow?

)(log2 NO

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BCSQ: Bin-based Core Stateless Queueing

• pkt put in a bin if virtual finish time falls in its range• bins scheduled according to ranges they represent• pkts in a bin served in FIFO order

• assuming infinite number of bins for time being

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BCSQ Network• (dynamic) packet state

– Reservation rate– Virtual time stamp– Virtual time adjustment term

• Edge routers– Maintain per-flow state– Perform per-flow operations– Initialize packet state

• Core routers– Schedule pkts based on pkt state– Update pkt state

• Admission control– For example, bandwidth brokers– For each router

• sum(reservation rate) <= link capacity

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Edge Routers• Maintaining per-flow state

– Flow reservation rate

• Inserting packet state– Reservation rate– Virtual time stamp (= departure time at edge) – Virtual time adjustment term

• Edge conditioner: traffic shaping– Traffic releasing rate <= flow’s reservation rate

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Core Routers• Upon receiving pkt, computing per-flow virtual delay

– Adjustment term: removing inter-pkt dependence, computed at edge

• Assigning virtual finish time– Virtual arrival time = virtual time stamp

• Packet scheduling– Pkt put in bin m if virtual finish time falls in its range

– Bins served according to their ranges

– Pkts in a bin served in FIFO order

• Assuming each scheduler has infinite bins for time being

termadjustratenreservatio

lengthpacketdelayvirtual _

_

__

Virtual finish time = virtual arrival time + virtual delay

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Core Routers (Cont’d)

• Upon departure, virtual time stamp updated appropriately– Reality check condition

– Virtual spacing property

– They are critical for bounded edge-to-edge delays

• Error term: bound on departure time of pkts

Virtual Time Stamp(k) >= real arrival time(k)

VTS(k+1) – VTS(k) >= pkt_length(k+1)/reveration_rate

Real departure time <= virtual finish time + error term

Virtual time stamp = virtual finish time + error term + prop daley

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Error Term & E2E Delay Bounds

• Error term of BCSQ

– Intuition: pkts served ahead of pkt p with larger virtual finish time

• Edge-to-edge delay bound for H hops

y)ation_delasum(propagterms)sum(error_n_ratereservatio

_lengthmax_packetHdelay

Error term = max pkt length of all flows / link capacity + length of bin

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Finite Number of Bins

• How many bins scheduler needs to avoid pkt overflow?– Assuming each bin has enough buffer

• Virtual time window of a scheduler– Time window that bins can collectively represent

• No packet overflow if the following condition holds

• BCSQ with (sufficiently large) finite number of bins– Rotating bins when VFT does not fit in current window

Virtual time window = number of bins * length of bin

Virtual time window >= 2 * worst case e2e delay

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Simulation Settings• Network topology

– S0, S1, S2, edge routers

• Link – capacity: 10Mbps– Propagation delay: 10ms

• Traffic – 6 CBR flows from S0 to R0 (1Mbps – 0.5Mbps)– 6 Exponential on/off flows from S1 to R1, from S2 to R2

• Target network utilization level 90%

– Pkt size: 210B

• Schedulers compared– BCSQ, FIFO, and CSVC– Traffic shaped at edge for all, reservation rate = average rate

• Simulated with other settings, similar observations• End-to-end delay of pkts:

– Delay between N1 and R0

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FIFO vs. CSVC

• Flow differentiation • all flows receive similar service for FIFO• flows with higher reservation rate get better service for CSVC

FIFO CSVC

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BCSQ• Controlling flow differentiation by changing bin length

– When bin length sufficient large, BCSQ -> FIFO– When bin length sufficient small, BCSQ -> CSVC

s01.0 s005.0 s0025.0

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Summary• Proposed and analyzed BCSQ

– A bin-based core stateless queueing mechanism– Provides per-flow guaranteed service– Flexibly control GS level by changing bin length

• Trade-off between complexity and GS level

– Derived the end-to-end delay bounds– Analyzed relationship between number of bins, bin length

and worst-case end-to-end delay to ensure no pkt overflow– Performed simulation studies

Thank you very much!