Florida State UniversityZhenhai Duan1 BCSQ: Bin-based Core Stateless Queueing for Scalable Support of Guaranteed Services Zhenhai Duan Karthik Parsha Department of Computer Science Florida State University

Zhenhai Duan2 Agenda Core stateless networks for per-flow guaranteed services –Introduction and motivation BCSQ: Bin-based Core Stateless Queueing Performance analyses and simulation studies Summary

Florida State UniversityZhenhai Duan3 Core Stateless Networks for Per-Flow GS

Florida State UniversityZhenhai Duan4 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

Florida State UniversityZhenhai Duan5 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

Florida State UniversityZhenhai Duan6 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?

Florida State UniversityZhenhai Duan7 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

Florida State UniversityZhenhai Duan8 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

Florida State UniversityZhenhai Duan9 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

Florida State UniversityZhenhai Duan10 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 Virtual finish time = virtual arrival time + virtual delay

Florida State UniversityZhenhai Duan11 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

Florida State UniversityZhenhai Duan12 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 Error term = max pkt length of all flows / link capacity + length of bin

Florida State UniversityZhenhai Duan13 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

Florida State UniversityZhenhai Duan14 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

Florida State UniversityZhenhai Duan15 FIFO vs. CSVC Flow differentiation all flows receive similar service for FIFO flows with higher reservation rate get better service for CSVC FIFOCSVC

Florida State UniversityZhenhai Duan16 BCSQ Controlling flow differentiation by changing bin length –When bin length sufficient large, BCSQ -> FIFO –When bin length sufficient small, BCSQ -> CSVC

Florida State UniversityZhenhai Duan17 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!