Date: Fri, 29 Mar 2024 06:21:37 +0000 (UTC)
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Overview
This document describes performance measurement and verification scenari=
os that have been identified by SIG-PMV, with corresponding challenges and =
the (broad) solution space for each.
The document is a "living" text that is under periodic review and update=
s as the work of SIG-PMV progresses. It is one of the outputs ("KPIs"=
) of the SIG.
We accepted a wide range of scenarios in our first meeting at Zurich, an=
d discussed them further in Amsterdam, narrowing the scope a little, and fl=
eshing out a number of the scenarios.
If there are any scenarios that should be added, please get in touch wit=
h the SIG-PMV group.
Current Scenarios
The following existing scenarios have been identified by SIG-PMV.
Data Intensive Scienc=
e Transfers
Description:
- Researchers from a growing number of disciplines are movin=
g increasingly large volumes of data between systems, locally, nationally a=
nd internationally.
- Likely to see Science DMZ model deployed
Challenges:
- Identifying poor performance and troubleshooting the cause=
s, which may lie in end systems or on the network path
Solution space:
- perfSONAR (widely used by WLCG / GridPP)
- In-application monitoring (e.g. FTS reports)
- GTS FIONA DTNs; open soon for testing
Multi domain monitor=
ing - toolkits
Description:
- Monitoring network performance between multiple administra=
tive domains
- Understanding in which domains issues lie
- Focus is on the networking aspect.
Challenges:
- Likely to need multiple measurement systems deployed =
- Coordination between the administrative domains
- Understand how it can be automated (alongside provisioning=
)
Solution space:
- perfSONAR
- GEANT T4 work heading towards solutions
- (Should this merge with data intensive science scenario?)<=
/li>
Wireless Network Monitori=
ng
Description:
- Measuring the utilisation and performance of a site=E2=80=
=99s local WiFi infrastructure
- Probably providing eduroam if at an academic site
- (At the moment not including 5G, IoT tech, but might do=E2=
=80=A6)
Challenges:
- Difficult to run tests from an end user=E2=80=99s system w=
hen that is likely to be a BYOD device
- High variability in performance depending on exact locatio=
n
- Multiple frequency channels and standards, emerging 802.11=
ac
- RF interference
Solution space:
- Crowd-sourced measurements data (WiFiMon)
Layer 2 Monitoring
Description:
- Measurement of L2 performance, below IP layer
- Includes Ethernet, MPLS, Carrier Ethernet
Challenges:
- Variety of L2 media
- Visualisation
Solution space:
- Work reported in GEANT JRA1/2 in 2013 (Cyan, Juniper, Cien=
a, Accedian equipment)
- Embedded probes (e.g. CFM/Y.1731)
Measuremen=
ts on virtual network environments
Description:
- Measurement of performance on VM infrastructure
- May include measurements to/from cloud services; AWS, Azur=
e, Google Cloud Platform
- Increasingly important as university / research services d=
eployed to cloud
Challenges:
- Abstraction of systems, impact of hypervisor, etc
- Variability of cloud performance depending on instance; e.=
g. AWS performance will vary depending on specific platform/size
- Tunnelling to cloud; MS Expressroute, etc. Extending addre=
ss space to cloud
Solution space:
- ??
- JRA2 Task1 connection services might be applicable
IPv6 Networks
Description:
- Measure IPv6 traffic levels
- Desire to measure growth of IPv6 deployment and usage, and=
relative performance to IPv4
Challenges:
- Not possible to differentiate IPv4 and IPv6 in all devices=
given state of MIB support?
- Operation in an IPv6-only environment
Solution space:
- IETF moving towards YANG
- (In theory, everything we do in SIG-PMV should be IP versi=
on agnostic, i.e., feature equivalent)
Overlay Network M=
onitoring solutions
Description:
- (Not sure of original intent here - need to clarify)
- Measurement of performance of overlay networks
- Do we mean the overlay, or the infrastructure over which i=
t runs? (e.g. under a L2VPN) =E2=80=93 both! Understanding which laye=
r has issues
- MD-VPN (used in ~20 NRENs)
- GTS
Challenges:
- Separation of overlay and underlying infrastructure
- Difficult for a network like GEANT to =E2=80=9Cpeer into=
=E2=80=9D tunnels
- User has no way to understand where the problem is
Solution space:
IP Multicast Mon=
itoring
Description:
- Monitor the successful performance and delivery of multica=
st traffic
- May be within a site, or inter-domain
Challenges:
- Apparently minimal use of multicast in the NRENs?
- Superceded to some point by multi-point VPNs
Solution space:
Emerging Scenarios
The following scenarios are emerging areas where SIG-PMV believes that s=
olutions will be required.
100G and beyond
Description:
- Performance measurement at 100Gbps +
Challenges:
- How to monitor/sniff/measure at such line rates
- Knowing vendor-specific tricks; tuning, performance of end=
systems; do 10G recipes work at 100G? They may not
- Building a generic model; so we become service oriented ra=
ther than technology oriented
- Transport tech may move at a different pace to CPU tech; o=
ther e2e elements such as firewalls
- Mixed speeds =E2=80=93 10G <-> 100G
Solution space:
- Existing systems, e.g. perfSONAR, with appropriate tuning =
/ configuration?
SDN controlled Monitoring=
h2>
Description:
Challenges:
- Service differences?
- What=E2=80=99s different to a standard IP service
- Tools like traceroute in an OpenFlow network
- Monitoring traffic may follow different paths to applicati=
on traffic
Solution space:
- ??
- Some related work in GEANT project; JRA2, maybe JRA1
Monitoring autonomic ne=
tworks
Description:
- Measuring performance in self-configuring networks
Challenges:
- Solution needs to also be self-configuring
- Network operating systems that move flows very dynamically=
; flow may not have a static path
Solution space:
Monitoring as a =
Service / NMS as a Service
Description:
- Includes OSS, BSS with monitoring and performance verifica=
tion.
Challenges:
- Provision, and automatically monitor
Solution space:
- ..?
- JRA2 T2 is doing something in this area
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