CoCo: Compact and Optimized Consolidation of Modularized Service Function Chains in NFV Zili Meng Jun Bi Haiping Wang Chen Sun Hongxin Hu
NFV & Modularization Dedicated Dedicated Dedicated Dedicated NFV: Commodity Hardware Devices Service Chain VPN Monitor Firewall Load VM VM VM VM Balancer Alert Read Classifier Output Drop elements Modularized SFC (MSFC) 2
NFV & Modularization Dedicated Dedicated Dedicated Dedicated NFV: Commodity Hardware Devices Service Chain VPN Monitor Firewall Load VM VM VM VM Balancer Low Cost Alert Flexibility Read Classifier Output Scalability Drop elements …… Modularized SFC (MSFC) 3
However… • Two drawbacks: – High latency – poor resource efficiency 4
However… • Two drawbacks: • OpenBox [Sigcomm’16] – High latency – Element reuse • NFVnice [Sigcomm’17] – poor resource efficiency – NF consolidation: containers in one VM (core). Which elements to consolidate? 5
Key Observations placement affects MSFC performance by affecting inter‐VM transfers VM2 VM1 VM3 VM2 VM1 VM3 E 2 E 4 E 2 E 3 E 4 E 3 E 1 E 1 E 7 E 7 E 5 E 5 E 6 E 6 6
CoCo… identifies inter‐VM transfer between elements optimizes placement of elements on VMs optimizes dynamic scaling mechanism
Challenges • Optimized Placement – How to model the inter‐VM transfer? – How to find optimal solutions efficiently? • Optimized Dynamic Scaling – How to reduce inter‐VM transfers during scaling out? 8
Challenges • Optimized Placement – How to model the inter‐VM transfer? Optimized Placer – How to find optimal solutions efficiently? • Optimized Dynamic Scaling – How to reduce inter‐VM transfers Individual Scaler during scaling out? 9
Optimized Placer • Packet Transfer Cost: VM #1 VM #n Scheduler Scheduler – Four‐step transfer delay: � � … element element element element – Service chain throughput: Θ VM Memory VM Memory VM Memory VM Memory – Delayed Bytes: ① ④ �� � Θ ⋅ � � vNIC vNIC vNIC vNIC ② ③ • Resource Analysis: vSwitch vSwitch – Observation: The CPU utilization of an element is linear to processing speed 10
Optimized Placer • Packet Transfer Cost: VM #1 VM #n Scheduler Scheduler – Four‐step transfer delay: � � … element element element element – Service chain throughput: Θ VM Memory VM Memory VM Memory VM Memory – Delayed Bytes: ① ④ �� � Θ ⋅ � � vNIC vNIC vNIC vNIC ② ③ • Resource Analysis: vSwitch vSwitch – Observation: The CPU utilization of an element is linear to processing speed 11
Optimized Placer – 0‐1 Quadratic Programming • Intuition: Consolidate adjacent elements together – If we place two adjacent elements together to one VM, there will be no inter‐VM packet transfer. VM1 VM2 Stateful Header Logger Payload Alert Classifier Analyzer inter‐VM intra‐VM 12
Optimized Placer – 0‐1 Quadratic Programming • : indicating element is placed onto instance �,� • Challenge: How to express two elements are placed together? � 1 2 3 � 1 2 3 4 5 6 4 5 6 � �,� � �,� 0 1 0 0 0 0 0 0 1 0 0 0 � �,� � �,� 0 0 1 0 0 0 0 0 1 0 0 0 � �,� ⋅ � �,� � �,� ⋅ � �,� 0 0 0 0 0 0 0 0 1 0 0 0 indicator: (quadratic) �,� �,� � 13
Optimized Placer – 0‐1 Quadratic Programming • Objective – The total inter‐VM Delayed Bytes . • Constraints – The placement cannot lead to the overload of any instances. • For other mathematical details, please refer to our paper. 14
Optimized Individual Scaling VM1 VM2 MSFC before scaling Stateful Header Logger Payload Alert Classifier Analyzer state syn additional VM3 Stateful ~100ms according to packet Payload OpenNF [Sigcomm’14] Analyzer transfer VM1 VM2 Scaling with traditional method Stateful Header Logger Payload Alert Classifier Analyzer 15
Optimized Individual Scaling • Key novelty Migrate other elements consolidated together to release resources for the overloaded element. 16
Optimized Individual Scaling VM1 VM2 MSFC before scaling Stateful Header Logger Payload Alert Classifier Analyzer additional VM3 Stateful packet Payload Analyzer transfer state VM1 VM2 Scaling with traditional method syn Stateful Header Logger Payload Alert Classifier Analyzer VM1 VM2 Stateful CoCo Header Logger Payload Alert Classifier Analyzer 17
Optimized Individual Scaling • Consistency guarantee mechanism – Overload should be alleviated. – Migration will not lead to new hotspots. • Advantage of CoCo Individual Scaler – No new hardware resource consumed – Additional packet transfer avoided – State synchronization avoided • Application scenario of CoCo Individual Scaler – Imbalance between VMs (OFM [IWQoS’18]) 18
Optimized Individual Scaling • Consistency guarantee mechanism – Overload should be alleviated. – Migration will not lead to new hotspots. • Advantage of CoCo Individual Scaler – No new hardware resource consumed – Additional packet transfer avoided – State synchronization avoided • Application scenario of CoCo Individual Scaler – Imbalance between VMs (OFM [IWQoS’18]) 19
Implementation and Evaluation • Evaluation Setup – Docker for consolidation, DPDK version 16.11 – OpenNF [Sigcomm’14] and TFM [ICNP’16] for migration mechanisms. – MATLAB for solving 0‐1 Quadratic Programming – Intel(R) Xeon(R) E5‐2690 v2 CPUs, 256G RAM, 2 × 10G NICs • Evaluation Goal – demonstrate the assumption of linearity – demonstrate the effectiveness of CoCo placement – demonstrate the performance of CoCo scaling 20
1. Throughput‐CPU Utilization • For one core only • Sender – � � � 0.9997 • Classifier – 100 rules on IP header – � � � 0.9999997 21
2. Simulations on Placement • Evaluation Target – Random: select available VMs randomly – Greedy: place elements in sequence chain‐by‐chain • Traffic: Randomly pick flows from CAIDA traffic • Two topology: Chain 2 Chain 1 E6 E7 E1 E2 Chain 1 E5 E6 E1 E2 E3 E4 E5 Chain 3 Chain 2 E3 E4 E8 E9 22
2. Simulations on Placement • Performance • Resource Utilization 6 25% Placement Failure Rate CoCo CoCo 5 20% Sum of DB (MB) Greedy Greedy 4 18% Random 15% Random 3 10% 2 59% 5% 1 0 0% Topo1 Topo2 Topo1 Topo2 23
2. Simulations on Placement • Performance • Resource Utilization 6 25% Placement Failure Rate CoCo CoCo 5 20% Sum of DB (MB) Greedy Greedy 4 18% Random 15% Random 3 10% 2 59% 5% 1 0 0% Topo1 Topo2 Topo1 Topo2 24
3. Evaluation on Dynamic Scaling • Based on OpenNF [Sigcomm’14] • Per‐packet latency VM3 Stateful traffic increases Payload Analyzer 80 CoCo Latency (ms) VM1 VM2 60 Traditional Stateful Header 40 Logger Payload Alert Classifier Analyzer 20 by 45% 0 VM1 VM2 0 10 20 30 40 50 Stateful Header Packet # (kilo) Logger Payload Alert Classifier Analyzer 25
Conclusion • CoCo: Compact and Optimized Consolidation of MSFCs in NFV – Optimized Placer – Individual Scaler • Significant Performance Improvement – Up to 59% Delayed Bytes reduction in initial placement. – 45% latency reduction when dynamic scaling. • Future work – Multi‐core placement – Intra‐core cache analysis 26
Thank you! netarchlab.tsinghua.edu.cn mengzl15@mails.tsinghua.edu.cn
Recommend
More recommend
