The Science DMZ

Eli Dart, Campus network engineering workshop
19/10/2016 The Science DMZ

The Science DMZ
Eli Dart
Network Engineer
ESnet Science Engagement
Lawrence Berkeley National Laboratory
JISC Campus Network Engineering for Data Intensive
Science Workshop
October 19, 2016

Overview
• Science DMZ Motivation and Introduction
• Science DMZ Architecture
• Network Monitoring For Performance
• Data Transfer Nodes & Applications
• Science Engagement
3 – ESnet Science Engagement (engage@es.net) - 11/1/2016
© 2016, Energy Sciences Network

• Networks are an essential part of data-intensive science
– Connect data sources to data analysis
– Connect collaborators to each other
– Enable machine-consumable interfaces to data and analysis resources (e.g. portals),
automation, scale
• Performance is critical
– Exponential data growth
– Constant human factors
– Data movement and data analysis must keep up
• Effective use of wide area (long-haul) networks by scientists has
historically been difficult
Motivation

The Central Role of the Network
• The very structure of modern science assumes science networks exist: high performance,
feature rich, global scope
• What is “The Network” anyway?
– “The Network” is the set of devices and applications involved in the use of a remote
resource
• This is not about supercomputer interconnects
• This is about data flow from experiment to analysis, between facilities, etc.
– User interfaces for “The Network” – portal, data transfer tool, workflow engine
– Therefore, servers and applications must also be considered
• What is important? Ordered list:
1. Correctness
2. Consistency
3. Performance

TCP – Ubiquitous and Fragile
• Networks provide connectivity between hosts – how do hosts see the network?
– From an application’s perspective, the interface to “the other end” is a socket
– Communication is between applications – mostly over TCP
• TCP – the fragile workhorse
– TCP is (for very good reasons) timid – packet loss is interpreted as congestion
– Packet loss in conjunction with latency is a performance killer
– Like it or not, TCP is used for the vast majority of data transfer applications (more
than 95% of ESnet traffic is TCP)

A small amount of packet loss makes a huge
difference in TCP performance
Metro Area
Local
(LAN)
Regional
Continental
International
Measured (TCP Reno) Measured (HTCP) Theoretical (TCP Reno) Measured (no loss)
With loss, high performance
beyond metro distances is
essentially impossible

Working With TCP In Practice
• Far easier to support TCP than to fix TCP
– People have been trying to fix TCP for years – limited success
– Like it or not we’re stuck with TCP in the general case
• Pragmatically speaking, we must accommodate TCP
– Sufficient bandwidth to avoid congestion
– Zero packet loss
– Verifiable infrastructure
• Networks are complex
• Must be able to locate problems quickly
• Small footprint is a huge win – small number of devices so that problem isolation is
tractable

Putting A Solution Together
• Effective support for TCP-based data transfer
– Design for correct, consistent, high-performance operation
– Design for ease of troubleshooting
• Easy adoption is critical
– Large laboratories and universities have extensive IT deployments
– Drastic change is prohibitively difficult
• Cybersecurity – defensible without compromising performance
• Borrow ideas from traditional network security
– Traditional DMZ
• Separate enclave at network perimeter (“Demilitarized Zone”)
• Specific location for external-facing services
• Clean separation from internal network
– Do the same thing for science – Science DMZ

Dedicated
Systems for Data
Transfer
Network
Architecture
Performance
Testing &
Measurement
Data Transfer Node
• High performance
• Configured specifically for
data transfer
• Proper tools
Science DMZ
• Dedicated network location
for high-speed data
resources
• Appropriate security
• Easy to deploy - no need to
redesign the whole network
perfSONAR
• Enables fault isolation
• Verify correct operation
• Widely deployed in ESnet
and other networks, as well
as sites and facilities
The Science DMZ Design Pattern

Abstract or Prototype Deployment
• Add-on to existing network infrastructure
– All that is required is a port on the border router
– Small footprint, pre-production commitment
• Easy to experiment with components and technologies
– DTN prototyping
– perfSONAR testing
• Limited scope makes security policy exceptions easy
– Only allow traffic from partners
– Add-on to production infrastructure – lower risk

Science DMZ Design Pattern (Abstract)
10GE
10GE
10GE
10GE
10G
BorderRouter
WAN
ScienceDMZ
Switch/Router
EnterpriseBorder
Router/Firewall
Site/Campus
LAN
Highperformance
DataTransferNode
withhigh-speedstorage
Per-service
securitypolicy
controlpoints
Clean,
High-bandwidth
WANpath
Site/Campus
accesstoScience
DMZresources
perfSONAR
perfSONAR
perfSONAR

Local And Wide Area Data Flows
10GE
10GE
10GE
10GE
10G
Border Router
WAN
Science DMZ
Switch/Router
Enterprise Border
Router/Firewall
Site / Campus
LAN
High performance
Data Transfer Node
with high-speed storage
Per-service
security policy
control points
Clean,
High-bandwidth
WAN path
Site / Campus
access to Science
DMZ resources
perfSONAR
perfSONAR
High Latency WAN Path
Low Latency LAN Path
perfSONAR

Support For Multiple Projects
• Science DMZ architecture allows multiple projects to put DTNs in place
– Modular architecture
– Centralized location for data servers
• This may or may not work well depending on institutional politics
– Issues such as physical security can make this a non-starter
– On the other hand, some shops already have service models in place
• On balance, this can provide a cost savings – it depends
– Central support for data servers vs. carrying data flows
– How far do the data flows have to go?

Multiple Projects
10GE
10GE
10GE
10G
Border Router
WAN
Science DMZ
Switch/Router
Enterprise Border
Router/Firewall
Site / Campus
LAN
Project A DTN
Per-project
security policy
control points
Clean,
High-bandwidth
WAN path
Site / Campus
access to Science
DMZ resources
perfSONAR
perfSONAR
Project B DTN
Project C DTN

Multiple Science DMZs – Dark Fiber
Dark
Fiber
Dark
Fiber
10GE
Dark
Fiber
10GE
10G
Border Router
WAN
Science DMZ
Switch/Routers
Enterprise Border
Router/Firewall
Site / Campus
LAN
Project A DTN
(building A)
Per-project
security
policy
perfSONAR
perfSONAR
Facility B DTN
(building B)
Cluster DTN
(building C)
perfSONARperfSONAR
Cluster
(building C)

Supercomputer Center Deployment
• High-performance networking is assumed in this environment
– Data flows between systems, between systems and storage, wide area, etc.
– Global filesystem often ties resources together
• Portions of this may not run over Ethernet (e.g. IB)
• Implications for Data Transfer Nodes
• “Science DMZ” may not look like a discrete entity here
– By the time you get through interconnecting all the resources, you end up with most of the
network in the Science DMZ
– This is as it should be – the point is appropriate deployment of tools, configuration, policy
control, etc.
• Office networks can look like an afterthought, but they aren’t
– Deployed with appropriate security controls
– Office infrastructure need not be sized for science traffic

Supercomputer Center
Virtual
Circuit
Routed
Border Router
WAN
Core
Switch/Router
Firewall
Offices
perfSONAR
perfSONAR
perfSONAR
Supercomputer
Parallel Filesystem
Front end
switch
Data Transfer
Nodes
Front end
switch

Supercomputer Center Data Path
Virtual
Circuit
Routed
Border Router
WAN
Core
Switch/Router
Firewall
Offices
perfSONAR
perfSONAR
perfSONAR
Supercomputer
Parallel Filesystem
Front end
switch
Data Transfer
Nodes
Front end
switch
High Latency WAN Path
Low Latency LAN Path
High Latency VC Path

Common Threads
• Two common threads exist in all these examples
• Accommodation of TCP
– Wide area portion of data transfers traverses purpose-built path
– High performance devices that don’t drop packets
• Ability to test and verify
– When problems arise (and they always will), they can be solved if the infrastructure is built
correctly
– Small device count makes it easier to find issues
– Multiple test and measurement hosts provide multiple views of the data path
• perfSONAR nodes at the site and in the WAN
• perfSONAR nodes at the remote site

Overview

Performance Monitoring
• Everything may function perfectly when it is deployed
• Eventually something is going to break
– Networks and systems are complex
– Bugs, mistakes, …
– Sometimes things just break – this is why we buy support contracts
• Must be able to find and fix problems when they occur
• Must be able to find problems in other networks (your network may be fine, but
someone else’s problem can impact your users)
• TCP was intentionally designed to hide all transmission errors from the user:
– “As long as the TCPs continue to function properly and the internet system does
not become completely partitioned, no transmission errors will affect the users.”
(From RFC793, 1981)

Soft Network Failures – Hidden Problems
• Hard failures are well-understood
– Link down, system crash, software crash
– Routing protocols are designed to cope with hard failures (route around damage)
– Traditional network/system monitoring tools designed to quickly find hard failures
• Soft failures result in degraded capability
– Connectivity exists
– Performance impacted
– Typically something in the path is functioning, but not well
• Soft failures are hard to detect with traditional methods
– No obvious single event
– Sometimes no indication at all of any errors
• Independent testing is the only way to reliably find soft failures

Rebooted router to
fix forwarding table
Gradual failure
of optical line
card
Sample Soft Failures
Gb/s
normal
performance
degrading
performance
repair
one month

Testing Infrastructure – perfSONAR
• perfSONAR is:
– A widely-deployed test and measurement infrastructure
• ESnet, Internet2, US regional networks, international networks
• Laboratories, supercomputer centers, universities
• Individual Linux hosts at key network locations (POPs, Science DMZs, etc.)
– A suite of test and measurement tools
– A collaboration that builds and maintains the toolkit
• By installing perfSONAR, a site can leverage over 1400 test servers deployed around the world
• perfSONAR is ideal for finding soft failures
– Alert to existence of problems
– Fault isolation
– Verification of correct operation

Lookup Service Directory Search:
http://stats.es.net/ServicesDirectory/
© 2016, Energy Sciences Network26 – ESnet Science Engagement (engage@es.net) - 11/1/2016

perfSONAR Dashboard: http://ps-
dashboard.es.net
© 2016, Energy Sciences Network27 – ESnet Science Engagement (engage@es.net) - 11/1/2016

Overview

Dedicated Systems – The Data Transfer Node
• The DTN is dedicated to data transfer
• Set up specifically for high-performance data movement
– System internals (BIOS, firmware, interrupts, etc.)
– Network stack
– Storage (global filesystem, Fibrechannel, local RAID, etc.)
– High performance tools
– No extraneous software
• Limitation of scope and function is powerful
– No conflicts with configuration for other tasks
– Small application set makes cybersecurity easier
• Limitation of application set is often a core security policy component

Data Transfer Tools For DTNs
• Parallelism is important
– It is often easier to achieve a given performance level with four parallel connections than
one connection
– Several tools offer parallel transfers, including Globus/GridFTP
• Latency interaction is critical
– Wide area data transfers have much higher latency than LAN transfers
– Many tools and protocols assume a LAN
• Workflow integration is important
• Key tools: Globus Online, HPN-SSH

Say NO to SCP (2016)
• Using the right data transfer tool is very important
• Sample Results: Berkeley, CA to Argonne, IL (near Chicago ) RTT = 53 ms, network capacity =
10Gbps.
• Notes
– scp is 24x slower than GridFTP on this path!!
– to get more than 1 Gbps (125 MB/s) disk to disk requires RAID array.
– (Assumes host TCP buffers are set correctly for the RTT)
Tool Throughput
scp 330 Mbps
wget, GridFTP, FDT, 1 stream 6 Gbps
GridFTP and FDT, 4 streams 8 Gbps (disk limited)
31 – ESnet Science Engagement (engage@es.net) -
11/1/2016

Overview

The Data Transfer Superfecta: Science DMZ Model
Data Transfer Node
• Configured for data transfer
• High performance
• Proper tools
perfSONAR
• Enables fault isolation
• Verify correct operation
• Widely deployed in ESnet
and other networks, as well
as sites and facilities
Science DMZ
• Dedicated location for DTN
• Proper security
• Easy to deploy - no need to redesign the whole
network
Engagement
• Resources & Knowledgebase
• Partnerships
• Education & Consulting

Context Setting
• DOE, NSF, and other agencies are investing
billions of dollars in state-of-the-art
cyberinfrastructure to support data-intensive
science.
• Many researchers do not understand the
value of these services and have difficulty
using them.
• A proactive effort is needed to drive adoption
of advanced services and accelerate science
output: Science Engagement
11/1/2016 © 2016, Energy Sciences Network

Science Engagement
• The ESnet Science Engagement team's mission is to ensure that science collaborations at
every scale, in every domain, have the information and tools they need to achieve maximum
benefit from global networks through the creation of scalable, community-driven strategies and
approaches.
• Science Engagement team works in several areas at once
– Understand key elements which contribute to desired outcomes
• Requirements analysis – what is needed
• Also identify choke points, road blocks, missing components
– Network architecture, performance, best practice
– Systems engineering, consulting, troubleshooting
– Collaboration with others
– Workshops and webinars
11/1/2016 © 2016, Energy Sciences Network

• The Science DMZ design pattern provides a flexible model for supporting high-performance
data transfers and workflows
• Key elements:
– Accommodation of TCP
• Sufficient bandwidth to avoid congestion
• Loss-free IP service
– Location – near the site perimeter if possible
– Test and measurement
– Dedicated systems
– Appropriate security
– Science Engagement to foster adoption
Science DMZ Wrapup

Links and Lists
– ESnet fasterdata knowledge base
• http://fasterdata.es.net/
– Science DMZ paper
• http://www.es.net/assets/pubs_presos/sc13sciDMZ-final.pdf
– Science DMZ email list
• Send mail to sympa@lists.lbl.gov with subject "subscribe esnet-sciencedmz”
– perfSONAR
• http://fasterdata.es.net/performance-testing/perfsonar/
• http://www.perfsonar.net
– Globus
• https://www.globus.org/

Thanks!
Eli Dart
dart@es.net
Energy Sciences Network (ESnet)
Lawrence Berkeley National Laboratory
http://my.es.net/
http://www.es.net/
http://fasterdata.es.net/

Overview

Context: Science DMZ Adoption
• DOE National Laboratories
– HPC centers, LHC sites, experimental facilities
– Both large and small sites
• NSF CC* programs have funded many Science DMZs
– Significant investments across the US university complex
– Big shoutout to the NSF – these programs are critically important
• Other US agencies
– NIH
– USDAAgricultural Research Service
• International
– Australia https://www.rdsi.edu.au/dashnet
– Brazil
– UK

Strategic Impacts
• What does this mean?
– We are in the midst of a significant cyberinfrastructure upgrade
– Enterprise networks need not be unduly perturbed 
• Significantly enhanced capabilities compared to 3 years ago
– Terabyte-scale data movement is much easier
– Petabyte-scale data movement possible outside the LHC experiments
• ~3.1Gbps = 1PB/month
• ~14Gbps = 1PB/week
– Widely-deployed tools are much better (e.g. Globus)
• Metcalfe’s Law of Network Utility
– Value of Science DMZ proportional to the number of DMZs
• n2 or n(logn) doesn’t matter – the effect is real
– Cyberinfrastructure value increases as we all upgrade

Next Steps – Building On The Science DMZ
• Enhanced cyberinfrastructure substrate now exists
– Wide area networks (ESnet, GEANT, Internet2, Regionals)
– Science DMZs connected to those networks
– DTNs in the Science DMZs
• What does the scientist see?
– Scientist sees a science application
• Data transfer
• Data portal
• Data analysis
– Science applications are the user interface to networks and DMZs
• The underlying cyberinfrastructure components (networks, Science DMZs, DTNs, etc.)
are part of the instrument of discovery
• Large-scale data-intensive science requires that we build larger structures on top of those
components

Science Data Portals
• Large repositories of scientific data
– Climate data
– Sky surveys (astronomy, cosmology)
– Many others
– Data search, browsing, access
• Many scientific data portals were designed 15+ years ago
– Single-web-server design
– Data browse/search, data access, user awareness all in a single system
– All the data goes through the portal server
• In many cases by design
• E.g. embargo before publication (enforce access control)

Legacy Portal Design
10GE
Border Router
WAN
Firewall
Enterprise
perfSONAR
perfSONAR
Filesystem
(data store)
10GE
Portal
Server
Browsing path
Query path
Data path
Portal server applications:
web server
search
database
authentication
data service• Very difficult to improve performance without
architectural change
– Software components all tangled together
– Difficult to put the whole portal in a Science
DMZ because of security
– Even if you could put it in a DMZ, many
components aren’t scalable
• What does architectural change mean?

Example of Architectural Change – CDN
• Let’s look at what Content Delivery Networks did for web applications
• CDNs are a well-deployed design pattern
– Akamai and friends
– Entire industry in CDNs
– Assumed part of today’s Internet architecture
• What does a CDN do?
– Store static content in a separate location from dynamic content
• Complexity isn’t in the static content – it’s in the application dynamics
• Web applications are complex, full-featured, and slow
– Databases, user awareness, etc.
– Lots of integrated pieces
• Data service for static content is simple by comparison
– Separation of application and data service allows each to be optimized

Classical Web Server Model
• Web browser fetches pages from web server
– All content stored on the web server
– Web applications run on the web server
• Web server may call out to local database
• Fundamentally all processing is local to the web server
– Web server sends data to client browser over the network
• Perceived client performance changes with network conditions
– Several problems in the general case
– Latency increases time to page render
– Packet loss + latency cause problems for large static objects
Hosting
Provider
Transit
Network
Residential
BroadbandWEB
Long Distance / High Latency
Web Server
Browser

Solution: Place Large Static Objects Near Client
Hosting
Provider
Transit
Network
Residential
BroadbandWEB
Long Distance / High Latency
CDN
DATA
Short Distance /
Low Latency
Web Server
CDN Data
Server
Browser
• CDN provides static content “close” to client
– Latency goes down
• Time to page render goes down
• Static content performance goes up
– Load on web server goes down (no need to serve static
content)
– Web server still manages complex behavior
• Local reasoning / fast changes for application owner
• Significant win for web application performance

Architectural Examination of Data Portals
• Common data portal functions (most portals have these)
– Search/query/discovery
– Data download method for data access
– GUI for browsing by humans
– API for machine access – ideally incorporates search/query + download
• Performance pain is primarily in the data handling piece
– Rapid increase in data scale eclipsed legacy software stack capabilities
– Portal servers often stuck in enterprise network
• Can we “disassemble” the portal and put the pieces back together better?
– Use Science DMZ as a platform for the data piece
– Avoid placing complex software in the Science DMZ

Legacy Portal Design
10GE
Border Router
WAN
Firewall
Enterprise
perfSONAR
perfSONAR
Filesystem
(data store)
10GE
Portal
Server
Browsing path
Query path
Data path
web server
search
database
authentication
data service

Next-Generation Portal Leverages Science DMZ
10GE10GE
10GE
10GE
Border Router
WAN
Science DMZ
Switch/Router
Firewall
Enterprise
perfSONAR
perfSONAR
10GE
10GE
10GE
10GE
DTN
DTN
API DTNs
(data access governed
by portal)
DTN
DTN
perfSONAR
Filesystem
(data store)
10GE
Portal
Server
Browsing path
Query path
web server
search
database
authentication
Data Path
Data Transfer Path
Portal Query/Browse Path

Put The Data On Dedicated Infrastructure
• We have separated the data handling from the portal logic
• Portal is still its normal self, but enhanced
– Portal GUI, database, search, etc. all function as they did before
– Query returns pointers to data objects in the Science DMZ
– Portal is now freed from ties to the data servers (run it on Amazon if you want!)
• Data handling is separate, and scalable
– High-performance DTNs in the Science DMZ
– Scale as much as you need to without modifying the portal software
• Outsource data handling to computing centers or campus central storage
– Computing centers are set up for large-scale data
– Let them handle the large-scale data, and let the portal do the orchestration of data
placement

Ecosystem Is Ready For This
• Science DMZs are deployed at Labs, Universities, and computing centers
– XSEDE sites
– DOE HPC facilities
– Many campus clusters
• Globus DTNs are present in many of those Science DMZs
– XSEDE sites
– DOE HPC facilities
– Many campus clusters
• Architectural change allows data placement at scale
– Submit a query to the portal, Globus places the data at an HPC facility
– Run the analysis at the HPC facility
– The results are the only thing that ends up on a laptop or workstation

Petascale DTN Project
• Another example of building on the Science DMZ
• Supports all data-intensive applications which require large-scale data placement
• Collaboration between HPC facilities
– ALCF, NCSA, NERSC, OLCF
• Goal: per-Globus-job performance at 1PB/week level
– 15 gigabits per second
– With checksums turned on, etc.
– No special shortcuts, no arcane options
• Reference data set is 4.4TB of astrophysics model output
– Mix of file sizes
– Many directories
– Real data!

Petascale DTN Project
5.5 Gbps
5.5 Gbps
9.0 Gbps
12.0 Gbps
5.5 Gbps
4.1 Gbps
12.1 Gbps
10.1 Gbps
6.0 Gbps
5.4 Gbps
17.3 Gbps
18.3 Gbps
DTN
DTN
DTN
DTN
alcf#dtn_mira
ALCF
nersc#dtn
NERSC
olcf#dtn_atlas
OLCF
ncsa#BlueWaters
NCSA
Data set: L380
Files: 19260
Directories: 211
Other files: 0
Total bytes: 4442781786482 (4.4T bytes)
Smallest file: 0 bytes (0 bytes)
Largest file: 11313896248 bytes (11G bytes)
Size distribution:
1 - 10 bytes: 7 files
10 - 100 bytes: 1 files
100 - 1K bytes: 59 files
1K - 10K bytes: 3170 files
10K - 100K bytes: 1560 files
100K - 1M bytes: 2817 files
1M - 10M bytes: 3901 files
10M - 100M bytes: 3800 files
100M - 1G bytes: 2295 files
1G - 10G bytes: 1647 files
10G - 100G bytes: 3 files
May 2016

Major Data Site Deployment
• In some cases, large scale data service is the major driver
– Huge volumes of data (Petabytes or more) – ingest, export
– Large number of external hosts accessing/submitting data
• Single-pipe deployments don’t work
– Everything is parallel
• Networks (Nx10G LAGs, soon to be Nx100G)
• Hosts – data transfer clusters, no individual DTNs
• WAN connections – multiple entry, redundant equipment
– Choke points (e.g. firewalls) just cause problems

Data Site – Architecture

Data Site – Data Path
VC
Virtual
Circuit
Border
RoutersWAN
HA
Firewalls
Site/Campus
LAN
perfSONAR
perfSONAR
perfSONAR
Data Service
Switch Plane
Provider Edge
Routers
Virtual
Circuit
VC
Data Transfer
Cluster

• Goal – disentangle security policy and enforcement for science flows
from security for business systems
• Rationale
– Science data traffic is simple from a security perspective
– Narrow application set on Science DMZ
• Data transfer, data streaming packages
• No printers, document readers, web browsers, building control systems, financial
databases, staff desktops, etc.
– Security controls that are typically implemented to protect business resources often cause
performance problems
• Separation allows each to be optimized
Science DMZ Security

Science DMZ As Security Architecture
• Allows for better segmentation of risks, more granular application of controls to
those segmented risks.
– Limit risk profile for high-performance data transfer applications
– Apply specific controls to data transfer hosts
– Avoid including unnecessary risks, unnecessary controls
• Remove degrees of freedom – focus only on what is necessary
– Easier to secure
– Easier to achieve performance
– Easier to troubleshoot

Performance Is A Core Requirement
• Core information security principles
– Confidentiality, Integrity, Availability (CIA)
– Often, CIA and risk mitigation result in poor performance
• In data-intensive science, performance is an additional core mission
requirement: CIA  PICA
– CIA principles are important, but if performance is compromised the science mission
fails
– Not about “how much” security you have, but how the security is implemented
– Need a way to appropriately secure systems without performance compromises

Placement Outside the Firewall
• The Science DMZ resources are placed outside the enterprise firewall
for performance reasons
– The meaning of this is specific – Science DMZ traffic does not traverse the
firewall data plane
– Packet filtering is fine – just don’t do it with a firewall
• Lots of heartburn over this, especially from the perspective of a
conventional firewall manager
– Lots of organizational policy directives mandating firewalls
– Firewalls are designed to protect converged enterprise networks
– Why would you put critical assets outside the firewall???
• The answer is that firewalls are typically a poor fit for high-performance
science applications

Firewall Internals
• Typical firewalls are composed of a set of processors which inspect
traffic in parallel
– Traffic distributed among processors such that all traffic for a particular connection
goes to the same processor
– Simplifies state management
– Parallelization scales deep analysis
• Excellent fit for enterprise traffic profile
– High connection count, low per-connection data rate
– Complex protocols with embedded threats
• Each processor is a fraction of firewall link speed
– Significant limitation for data-intensive science applications
– Overload causes packet loss – performance crashes

What’s Inside Your Firewall?
• Vendor: “but wait – we don’t do this anymore!”
– It is true that vendors now have line-rate 10G firewalls
– 10GE has been deployed in science environments for about 15 years
– Firewall internals have only recently started to catch up with the 10G world
– 40GE hosts are being deployed now, 100GE host interfaces are available now
– Firewalls are behind again
• In general, IT shops want to get 5+ years out of a firewall purchase
– This often means that the firewall is years behind the technology curve
– When a new science project tries to deploy data-intensive resources, they get
whatever feature set was purchased several years ago

Firewall Capabilities and Science Traffic
• Firewalls have a lot of sophistication in an enterprise setting
– Application layer protocol analysis (HTTP, POP, MSRPC, etc.)
– Built-in VPN servers
– User awareness
• Data-intensive science flows typically don’t match this profile
– Common case – data on filesystem A needs to be on filesystem Z
• Data transfer tool verifies credentials over an encrypted channel
• Then open a socket or set of sockets, and send data until done (1TB, 10TB, 100TB, …)
– One workflow can use 10% to 50% or more of a 10G network link
• Do we have to use a firewall?

Firewalls As Access Lists
• When you ask a firewall administrator to allow data transfers through
the firewall, what do they ask for?
– IP address of your host
– IP address of the remote host
– Port range
– That looks like an ACL to me!
• No special config for advanced protocol analysis – just address/port
• Router ACLs are better than firewalls at address/port filtering
– ACL capabilities are typically built into the router
– Router ACLs typically do not drop traffic permitted by policy

Security Without Firewalls
• Data intensive science traffic interacts poorly with firewalls
• Does this mean we ignore security? NO!
– We must protect our systems
– We just need to find a way to do security that does not prevent us
from getting the science done
• Key point – security policies and mechanisms that protect the
Science DMZ should be implemented so that they do not
compromise performance
• Traffic permitted by policy should not experience performance impact as
a result of the application of policy

If Not Firewalls, Then What?
• Remember – the goal is to protect systems in a way that allows the
science mission to succeed
• I like something I heard at NERSC – paraphrasing: “Security controls
should enhance the utility of science infrastructure.”
• There are multiple ways to solve this – some are technical, and some
are organizational/sociological
• I’m not going to lie to you – this is harder than just putting up a firewall
and closing your eyes

Other Technical Capabilities
• Intrusion Detection Systems (IDS)
– One example is Bro – http://bro-ids.org/
– Bro is high-performance and battle-tested
• Bro protects several high-performance national assets
• Bro can be scaled with clustering: http://www.bro-ids.org/documentation/cluster.html
– Other IDS solutions are available also
• Netflow and IPFIX can provide intelligence, but not filtering
• Openflow and SDN
– Using Openflow to control access to a network-based service seems pretty obvious
– This could significantly reduce the attack surface for any authenticated network service
– This would only work if the Openflow device had a robust data plane

Other Technical Capabilities (2)
• Aggressive access lists
– More useful with project-specific DTNs
– If the purpose of the DTN is to exchange data with a small set of remote collaborators, the
ACL is pretty easy to write
– Large-scale data distribution servers are hard to handle this way (but then, the firewall
ruleset for such a service would be pretty open too)
• Limitation of the application set
– One of the reasons to limit the application set in the Science DMZ is to make it easier to
protect
– Keep desktop applications off the DTN (and watch for them anyway using logging, netflow,
etc – take violations seriously)
– This requires collaboration between people – networking, security, systems, and scientists

Collaboration Within The Organization
• All stakeholders should collaborate on Science DMZ design,
policy, and enforcement
• The security people have to be on board
– Remember: security people already have political cover – it’s called the firewall
– If a host gets compromised, the security officer can say they did their due diligence because
there was a firewall in place
– If the deployment of a Science DMZ is going to jeopardize the job of the security officer,
expect pushback
• The Science DMZ is a strategic asset, and should be understood
by the strategic thinkers in the organization
– Changes in security models
– Changes in operational models
– Enhanced ability to compete for funding
– Increased institutional capability – greater science output

Sample Data Transfer Results (2005)
• Using the right tool is very important
• Sample Results: Berkeley, CA to Argonne, IL (near Chicago). RTT = 53 ms, network capacity =
10Gbps.
Tool Throughput
scp: 140 Mbps
HPN patched scp: 1.2 Gbps
ftp 1.4 Gbps
GridFTP, 4 streams 5.4 Gbps
GridFTP, 8 streams 6.6 Gbps
• Note that to get more than 1 Gbps (125 MB/s) disk to disk requires RAID.
11/1/2016

The Science DMZ

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The Science DMZ