Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support...
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Transcript of Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support...
![Page 1: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/1.jpg)
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
![Page 2: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/2.jpg)
Florida State University Zhenhai Duan 2
Agenda
• Core stateless networks for per-flow guaranteed services– Introduction and motivation
• BCSQ: Bin-based Core Stateless Queueing
• Performance analyses and simulation studies
• Summary
![Page 3: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/3.jpg)
Florida State University Zhenhai Duan 3
Core Stateless Networks for Per-Flow GS
![Page 4: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/4.jpg)
Florida State University Zhenhai Duan 4
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
![Page 5: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/5.jpg)
Florida State University Zhenhai Duan 5
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
![Page 6: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/6.jpg)
Florida State University Zhenhai Duan 6
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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Florida State University Zhenhai Duan 7
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
![Page 8: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/8.jpg)
Florida State University Zhenhai Duan 8
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
![Page 9: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/9.jpg)
Florida State University Zhenhai Duan 9
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
![Page 10: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/10.jpg)
Florida State University Zhenhai Duan 10
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
![Page 11: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/11.jpg)
Florida State University Zhenhai Duan 11
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
![Page 12: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/12.jpg)
Florida State University Zhenhai Duan 12
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
![Page 13: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/13.jpg)
Florida State University Zhenhai Duan 13
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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Florida State University Zhenhai Duan 14
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
![Page 15: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/15.jpg)
Florida State University Zhenhai Duan 15
FIFO vs. CSVC
• Flow differentiation • all flows receive similar service for FIFO• flows with higher reservation rate get better service for CSVC
FIFO CSVC
![Page 16: Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department.](https://reader036.fdocuments.in/reader036/viewer/2022071806/56649f3e5503460f94c5f402/html5/thumbnails/16.jpg)
Florida State University Zhenhai Duan 16
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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Florida State University Zhenhai Duan 17
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!