Upload
robert-kennedy
View
215
Download
0
Tags:
Embed Size (px)
Citation preview
1
Traffic Engineering of High-Rate Large-sized Flows
Acknowledgment: UVA work is supported by DOE ASCR grants DE-SC002350 and DE-SC0007341, and NSF grants, OCI-1038058, OCI-1127340, and CNS-1116081, and ESnet work is supported by DOE grant DE-AC02-05CH11231
Tian Jin, Chris Tracy, Malathi Veeraraghavan, Zhenzhen Yan University of Virginia and ESnet
[email protected], [email protected] 8-11, 2013
Outline
• Problem statement & Motivation– Example of ESnet measured load– Adverse effects of “alpha flows”
• Hybrid Network Traffic Engineering System (HNTES)
• HNTES evaluation– NetFlow data collection– Effectiveness– Afflicted-flow packet percentage
2
Problem statement
• Flows generated by high-rate large-sized file transfers are called alpha flows– thresholds used in this paper: 1 GB in 1 min
• Previous work shows that alpha flows– are the cause of burstiness of IP traffic
• Experiment shows adverse effects of alpha flows on real-time A/V flows
• Problem: How can a provider identify such alpha flows within their network and direct them to separate QoS-controlled VCs?
3
Motivation: ESnet4 Core network for US Dept. of Energy Labs
StarLight
MAN LAN(32 A of A)
PNNL
FNL
ORNL
LLNL
GA
BNL
LANL
IP router
Lab
Optical node
SDN router Lab Link
MANNLR 10G
30/40/50G SDNIP
50
50
50
5040
3030
30
40
50
30
5050
40
40
4040
40
40
40
4040
40
Steve Cotter, Chin Guok, Joe Metzger, Bill Johnston
Brookhaven National Laboratory
Traffic surges on ESnet interface
5
Link rate: 10 Gbps
Outgoing traffic
Incomingtraffic
9 Gbps
Jan. 12, 2013
Motivation: Adverse effects of alpha flows
• Used DOE 100G testbed• Hosts: high-performance diskpts
6BNL
NEWY
ping flow(delay-sensitive)
TCP (alpha) flow
UDP flow(background)
buffer buildups
Impact of alpha flows on real-time flows
7
• Impact on ping flow delay – significant in 1-
queue configuration
– negligible in 2-queue configuration
• Need separate virtual queue for alpha flow packets
Pings: 1 per secDelay: 60 ms in 1-queue case
Delay: 2.1 ms in 2-queue case
UDP flow
TCP flow
3 Gbps
6 Gbps
Outline
• Problem statement & MotivationHybrid Network Traffic Engineering
System (HNTES)• HNTES evaluation
– NetFlow data collection– Effectiveness– Afflicted-flow packet percentage
8
Hybrid network traffic engineering system (HNTES)
- Intradomain identification/redirection of alpha flows
9
•Three steps– Analysis of NetFlow
reports from ingress routers to identify address prefixes of completed alpha flows
– IDC creates L3 circuits between ingress-egress router pairs and configures QoS
– IDC sets firewall filters to direct future alpha flows with matching address prefixes to L3 circuits
Aging parameter (A): age out rules corresponding to prefixes for which no alpha flows have been observed
Outline
• Problem statement & Motivation• Hybrid Network Traffic Engineering
System (HNTES)• HNTES evaluation
– NetFlow data collection– Effectiveness– Afflicted-flow packet percentage
10
Data collection for HNTES evaluation: NetFlow data from 4 routers were collected for 7 months (214
days)
11
router-1 & router-2: provider-edge (PE) routersrouter-3: core router (REN peering)router-4: core router (commercial peering)
OP: observation point
Effectiveness Analysis
• Two types of effectiveness– Cumulative effectiveness (Ci): percent of
alpha bytes (bytes reported in alpha NetFlow reports) that would have been redirected in period (1,i)
– Daily effectiveness (Ei): percent of alpha bytes that would have been redirected on day i
• Choose aging parameter for: – High effectiveness– Stability in firewall-filter size
12
Aging parameter: tradeoff effectiveness with size of firewall filter
• graphs for router 1 (similar for other routers)• 30 days is good compromise for aging parameter
13
Firewall filter size stable with aging parameter 30 Cumulative effectiveness > 90%
Cumulative effectiveness (/24)
14
Provider edge routers(single customers) Peering routers
(router-3: REN;router-4: commercial) Why is cumulative
effectivness lower for peeringrouters, esp. router-4?
Boxplots for 214 values each router-1 omitted as it is similar to router-2
Cum
ula
tive e
ffect
iveness
Effectiveness comparisons
15
• Obs. 1: higher effectiveness for /24 than for /32• Obs. 2: higher effectiveness for router-1 and router-2 than
for router-3 and router-4• Obs. 3: fewer alpha prefix IDs for router-3 and router-4
Explanations
16
• Obs. 1: data-transfer node clusters are typically located in the same /24 subnet; thus, repetition is greater with /24 than /32
• Obs. 2 and obs. 3: • Higher effectiveness for routers 1 & 2:
downloads from supercomputing facilities are repetitive (a scientist accesses the same data transfer nodes)
• Lower effectiveness for routers 3 & 4:• fewer uploads to DoE labs than
downloads from DOE labs• expect few, if any, scientific data
transfers from commerical peers (router-4)
Outline
• Problem statement & Motivation• Hybrid Network Traffic Engineering
System (HNTES)• HNTES evaluation
– NetFlow data collection– Effectiveness– Afflicted-flow packet percentage
17
Afflicted-flow packets
• B: set of non-alpha NetFlow reports for flows that share alpha prefix IDs
• Divide B into four subsets in sequence– C: non-alpha reports of alpha flows– D B-C: data-transfer reports (heuristic)– W B-C-D: well-known ports– L: leftover = B-C-D-W
• Afflicted flows: W+L
18
Afflicted-flow packets
• Tradeoff: /24 vs /32– /32 has lower effectiveness: large % of afflicted-flow packets
will be impacted when an alpha flow is not redirected– /24 has higher afflicted-flow packet percentage: small % of
afflicted-flow packets are adversely impacted
• Recommend /24 address prefixes for firewall filters
19
Percentage of afflicted-flow packets in samples of beta-flow (non-alpha flow) packets; across the 214-day period
Conclusions
• Hypothesis: Most high-speed data transfer nodes have static IP addresses, and alpha flows are created repeatedly between the same source-destination subnets– Validated for flows generated by dataset downloads as
observed at edge routers
• HNTES solution of determining src-dest address prefixes of completed alpha flows & using these prefixes to set firewall filters for future alpha-flow redirection is effective for downloads from DOE labs
• Less effective for uploads esp. from commercial peering links – But alpha-flow causing uploads are fewer
20