Download 12Introspection - BNRG - University of California, Berkeley

Berkeley-Helsinki Summer Course
Lecture #12: Introspection and
Adaptation
Randy H. Katz
Computer Science Division
Electrical Engineering and Computer Science Department
University of California
Berkeley, CA 94720-1776
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Outline
•
•
•
•
Introspection Concept and Methods
SPAND Content Level Adaptation
MIT Congestion Manager/TCP Layer Adaptation
ICAP Cache-Layer Adaptation
2
Outline
•
•
•
•
Introspection Concept and Methods
SPAND Content Level Adaptation
MIT Congestion Manager/TCP Layer Adaptation
ICAP Cache-Layer Adaptation
3
Introspection
• From Latin introspicere, “to look within”
– Process of observing the operations of one’s own mind with
a view to discovering the laws that govern the mind
• Within the context of computer systems
– Observing how a system is used (observe): usage patterns,
network activity, resource availability, denial of service
attacks, etc.
– Extracting a behavioral model from such use (discover)
– Use this model to improve the behavior of the system, by
making it more proactive, rather than reactive, to how it is
used
– Improve performance and fault tolerance, e.g., deciding
when to make replicas of objects and where to place them
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Introspection in Computer
Systems
• Locality of Reference
– Temporal: objects that are used are likely to be used again
in the near future
– Geographic: objects near each other are likely to be used
together
• Exploited in many places
–
–
–
–
Hardware caches, virtual memory mechanisms, file caches
Object interrelationships
Adaptive name resolution
Mobility patterns
• Implications
– Prefetching/prestaging
– Clustering/grouping
– Continuous refinement of behavioral model
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Example: Wide-Area Routing and
Data Location in OceanStore
• Requirements
– Find data quickly, wherever it might reside
» Locate nearby data without global communication
» Permit rapid data migration
– Insensitive to faults and denial of service attacks
» Provide multiple routes to each piece of data
» Route around bad servers and ignore bad data
– Repairable infrastructure
» Easy to reconstruct routing and location information
• Technique: Combined Routing and Data Location
– Packets are addressed to GUIDs, not locations
– Infrastructure gets the packets to their destinations and
verifies that servers are behaving
John Kubiatowicz
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Two-levels of Routing
• Fast, probabilistic search for “routing cache”
– Built from attenuated Bloom filters
– Approximation to gradient search
– Not going to say more about this today
• Redundant Plaxton Mesh used for underlying
routing infrastructure:
– Randomized data structure with locality properties
– Redundant, insensitive to faults, and repairable
– Amenable to continuous adaptation to adjust for:
» Changing network behavior
» Faulty servers
» Denial of service attacks
John Kubiatowicz
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Basic Plaxton Mesh
Incremental suffix-based routing
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4
NodeID
0x79FE
NodeID
0x23FE
NodeID
0x993E
4
NodeID
0x035E
3
NodeID
0x43FE
4
NodeID
0x73FE
3
NodeID
0x44FE
2
2
4
3
1
1
3
NodeID
0xF990
2
3
NodeID
0x555E
1
NodeID
0x73FF
2
NodeID
0xABFE
NodeID
0x04FE
NodeID
0x13FE
4
NodeID
0x9990
1
2
2
NodeID
0x423E
3
NodeID
0x239E
1
NodeID
0x1290
John Kubiatowicz
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Use of Plaxton Mesh
Randomization and Locality
John Kubiatowicz
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Use of the Plaxton Mesh
(Tapestry Infrastructure)
• As in original Plaxton scheme:
– Scheme to directly map GUIDs to root node IDs
– Replicas publish toward a document root
– Search walks toward root until pointer locatedlocality!
• OceanStore enhancements for reliability:
– Documents have multiple roots (Salted hash of GUID)
– Each node has multiple neighbor links
– Searches proceed along multiple paths
» Tradeoff between reliability and bandwidth?
– Routing-level validation of query results
• Dynamic node insertion and deletion algorithms
– Continuous repair and incremental optimization of links
John Kubiatowicz
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OceanStore Domains for
Introspection
• Network Connectivity, Latency
– Location tree optimization, link failure recovery
• Neighbor Nodes
– Clock synchronization, node failure recovery
• File Usage
– File migration
– Clustering related files
– Prefetching, hoarding
• Storage Peers
– Accounting, archive durability, backlisting
• Meta-Introspection
– Confidence estimation, stability
Dennis Geels, [email protected]
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Common Functionality
• These targets share some requirements:
– High input rates
» Watch every file access, heartbeat, packet
transmission
– Both short- and long-term decisions
» Respond to changes immediately
» Extract patterns from historical information
– Hierarchical, Distributed Analysis
» Low levels make decisions based on local information
» Higher levels possess broader, approximate knowledge
» Nodes must cooperate to solve problem
• We can build shared infrastructure
Dennis Geels, [email protected]
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Architecture for Wide-Area
Introspection
• Fast Event-Driven Handlers
– Filter and aggregate incoming events
– Respond immediately if necessary
• Local Database, Periodic Analysis
– Store historical information for trend-watching
– Allow more complicated, off-line algorithms
• Location-Independent Routing
– Flexible coordination, communication
Dennis Geels, [email protected]
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Event-Driven Handlers
• Treat all incoming data as events: messages,
timeouts, etc.
– Leads to natural state-machine design
– Events cause state transitions, finite processing time
– A few common primitives could be powerful: average. count,
filter by predicate, etc.
• Implemented in “small language”
– Counts important primitives for aggregation, database access
– Facilitates implementation of introspective algorithms
» Allows greater exploration, adaptability
– Can verify security, termination guarantees
• E.g., EVENT.TYPE=“file access” : increment
COUNT in EDGES where SRC==EVENT.SRC and
DST==EVENT.SRC
Dennis Geels, [email protected]
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Local Database, Periodic
Analysis
• Database Provides Powerful, Flexible Storage
– Persistent data allows long-term analysis
– Standard interface for event handler scripting language
– Leverage existing aggregation functionality
» Considerable work from Telegraph Project
– Can be lightweight
• Sophisticated Algorithms Run On Databases
– Too resource-intensive to operate directly on events
– Allow use of full programming language
– Security, termination still checkable; should use common mechanisms
• E.g., expensive clustering algorithm operating over edge graph,
using sparse-matrix operations to extract eigenvectors
representing related files
Dennis Geels, [email protected]
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Location-Independent Routing
• Not a very good name for a rather simple idea.
Interesting introspective problems are inherently distributed. Coodination among nodes is
difficult. Needed:
–
–
–
–
Automatically create/locate parents in aggregation hierarchy
Path redundancy for stability, availability
Scalability
Fault tolerance, responsiveness to fluctuation in workload
• OceanStore data location system shares these
requirements. This coincidence is not surprising,
as each are instances of wide-area distributed
problem solving.
• Leverage OceanStore Location/Routing System
Dennis Geels, [email protected]
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Summary: Introspection in
OceanStore
• Recognize and share a few common mechanisms
– Efficient event-driven handlers
– More powerful, database-driven algorithms
– Distributed, location-independent routing
• Leverage common architecture to allow system
designers to concentrate on developing &
optimizing domain-specific algorithms
Dennis Geels, [email protected]
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Outline
•
•
•
•
Introspection Concept and Methods
SPAND Content Level Adaptation
MIT Congestion Manager/TCP Layer Adaptation
ICAP Cache-Layer Adaptation
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SPAND Architecture
Mark Stemm
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SPAND Architecture
Mark Stemm
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What is Needed
• An efficient, accurate, extensible and timeaware system that makes shared, passive
measurements of network performance
• Applications that use this performance
measurement system to enable or improve
their functionality
Mark Stemm
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Issues to Address
• Efficiency: What are the bandwidth and
response time overheads of the system?
• Accuracy: How closely does predicted value
match actual client performance?
• Extensibility: How difficult is it to add new
types of applications to the measurement
system?
• Time-aware: How well does the system adapt
to and take advantage of temporal changes in
network characteristics?
Mark Stemm
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SPAND Approach: Shared
Passive Measurements
Mark Stemm
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Related Work
• Previous work to solve this problem
– Use active probing of network
– Depend on results from a single host (no sharing)
– Measure the wrong metrics (latency, hop count)
• NetDyn, NetNow, Imeter
– Measure latency and packet loss probability
• Packet Pair, bprobes
– If Fair Queuing, measures “fair share” of bottleneck link b/w)
– Without Fair Queuing, unknown (min close to link b/w)
• Pathchar
– Combines traceroute & packet pair to find hop-by-hop latency &
link b/w
• Packet Bunch Mode
– Extends back-to-back technique to multiple packets for greater
accuracy
Mark Stemm
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Related Work
• Probing Algorithms
– Cprobes: sends small group of echo packets as a simulated
connection (w/o flow or congestion control)
– Treno: like above, but with TCP flow/congestion control
algorithms
– Network Probe Daemon: traces route or makes short
connection to other network probe daemons
– Network Weather Service: makes periodic transfers to
distributed servers to determine b/w and CPU load on
each
Mark Stemm
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Related Work
• Server Selection Systems
– DNS to map name to many servers
» Either round-robin or load balancing
– Boston University: uses cprobes, bprobes
– Harvest: uses round trip time
– Harvard: uses geographic location
– Using routing metrics:
» IPV6 Anycast
» HOPS
» Cisco Distributed Director
» University of Colorado
– IBM WOM: uses ping times
– Georgia Tech: uses per-application, per-domain probe
clients
Mark Stemm
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Comparison with Shared
Passive Measurement
• What is measured?
– Others: latency, link b/w, network b/w
– SPAND: actual response time, application specific
• Where is it implemented?
– Others: internal network, at server
– SPAND: only in client domain
• How much additional traffic is introduced?
– Others: tens of Kbytes per probe
– SPAND: small performance reports and responses
• How realistic are the probes?
– Others: artificially generated probes that don’t
necessarily match realistic application workloads
– SPAND: actual observed performance from applications
Mark Stemm
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Comparison with Shared
Passive Measurement
• Does the probing use flow/congestion control?
– Others: no
– SPAND: whatever the application uses (usually yes)
• Do clients share performance information?
– Others: no; sometimes probes are made on behalf of
clients
– SPAND: yes
Mark Stemm
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Benefits of Sharing and
Passive Measurements
• Two similarly connected hosts are likely to
observe same performance of distant hosts
• Sharing measurements implies redundant
probes can be eliminated
Mark Stemm
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Benefits of Passive Measurements
Mark Stemm
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Design of SPAND
Mark Stemm
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Design of SPAND
• Modified Clients
– Make Performance Reports to Performance Servers
– Send Performance Requests to Performance Servers
• Performance Servers
– Receive reports from clients
– Aggregate/post process reports
– Respond to requests with Performance Responses
• Packet Capture Host
– Snoops on local traffic
– Makes Performance Reports on behalf of unmodified clients
Mark Stemm
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Design of SPAND
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Design of SPAND
• Applications Classes
– Way in which an application uses the network
– Examples:
» Bulk transfer: uses flow control, congestion control,
reliable delivery
» Telnet: uses reliability
» Real-time: uses flow control and reliability
– (Addr, Application Class) is target of a Performance
Request/Report
Mark Stemm
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Issues
• Accuracy
– Is net performance stable enough to make meaningful
Performance Reports?
– How long does it take before the system can service the
bulk of the Performance Requests?
– In steady state, what % of Performance Requests does
the system service?
– How accurate are Performance Responses?
• Stability
– Performance results must not vary much with time
• Implications of Connection Lengths
– Short TCP connections dominated by round trip time; long
connections by available bandwidth
Mark Stemm
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Application of SPAND:
Content Negotiation
Web pages look good
on server LAN
Mark Stemm
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Implications for Distant Access,
Overwhelmed Servers
Mark Stemm
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Content Negotiation
Mark Stemm
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Client-Side Negotiation Results
Mark Stemm
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Server-Side Dynamics
Mark Stemm
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Server-Side Negotiation:
Results
Mark Stemm
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Content Negotiation Results
• Network is the bottleneck for clients and
servers
• Content negotiation can reduce download times
of web clients
• Content negotiation can increase throughput
of web servers
• Actual benefit depends on fraction of
negotiable documents
Mark Stemm
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Outline
•
•
•
•
Introspection Concept and Methods
SPAND Content Level Adaptation
MIT Congestion Manager/TCP Layer Adaptation
ICAP Cache-Layer Adaptation
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Congestion Manager
(Hari@MIT, Srini@CMU)
• The Problem:
– Communications flows compete for same limited bandwidth resource
(especially on slow start!), implement own congestion response, no shared
learning, inefficient, within end node
• The Power of Shared Learning and Information Sharing
f1
f2
Server
f(n)
Internet
Client
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Adapting to Network
f1
?
Internet
Server
Client
• New applications may not use TCP
– Implement new protocol
– Often do not adapt to congestion: not “TCP-friendly”
Need system that helps applications learn
and adapt to congestion
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State of Congestion Control
• Increasing number of concurrent flows
• Increasing number of non-TCP apps
Congestion Manager (CM): An end-system
architecture for congestion management
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The Big Picture
HTTP
Per-macroflow
statistics
(cwnd, rtt, etc.)
Congestion
Manager
TCP1
Audio
Video1
TCP2
Video2
UDP
API
IP
All congestion management tasks performed in CM
Applications learn and adapt using API
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Problems
• How does CM control when and whose
transmissions occur?
– Keep application in control of what to send
• How does CM discover network state?
– What information is shared?
– What is the granularity of sharing?
Key issues: API and information sharing
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The CM Architecture
Applications
(TCP, conferencing app, etc)
API
Congestion
Controller
Prober
Sender
Scheduler
CM
protocol
Congestion
Detector
Responder
Receiver
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Feedback about Network State
• Monitoring successes and losses
– Application hints
– Probing system
• Notification API (application hints)
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Probing System
• Receiver modifications necessary
– Support for separate CM header
– Uses sequence number to detect losses
– Sender can request count of packets received
• Receiver modifications detected/negotiated via
handshake
– Enables incremental deployment
IP header
IP payload
CM header
IP payload
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Congestion Controller
• Responsible for deciding when to send a packet
• Window-based AIMD with traffic shaping
• Exponential aging when feedback low
– Halve window every RTT (minimum)
• Plug in other algorithms
– Selected on a “macro-flow” granularity
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Scheduler
• Responsible for deciding who should send a packet
• Hierarchical round robin
• Hints from application or receiver
– Used to prioritize flows
• Plug in other algorithms
– Selected on a “macro-flow” granularity
– Prioritization interface may be different
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Sequence number
CM Web Performance
TCP Newreno
With CM
Time (s)
CM greatly improves
predictability
and consistency
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Layered Streaming Audio
Sequence number
450
400
350
Competing TCP
300
TCP/CM
250
200
150
Audio/CM
100
50
0
0
5
10
15
Time (s)
20
25
Audio adapts to available bandwidth
Combination of TCP & Audio compete equally with normal TCP
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Congestion Manager Summary
• CM enables proper & stable congestion behavior
• Simple API enables app to learn/adapt to network
state
• Improves consistency/predictability of net xfers
• CM provides benefit even when deployed at senders
alone
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Outline
•
•
•
•
Introspection Concept and Methods
SPAND Content Level Adaptation
MIT Congestion Manager/TCP Layer Adaptation
ICAP Cache-Layer Adaptation
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How Internet Content is
Delivered Today
Server
farm
database
Boston
Internet
New York
English
Spanish
mainframe
component
solution
Last Mile
Access
Broadband
Connections
cable modems
DSL, dial-up,
wireless
Internet
Caching & Internet
Content Delivery
localizes content
Centralized
Servers
Applications &
Multiple versions
of content are
centralized
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What is iCAP?
• iCAP lets clients send HTTP messages to
servers for “adaptation”
– In essence, an “RPC” mechanism (Remote Procedure Call)
for HTTP messages
• An adapted message might be a request:
– Modify request method, URL being requested, etc.
• ...or, it might be a reply
– Change any aspect of delivered content
• iCAP enables edge services
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What iCAP is not
-for now
• A way to specify adaptation policy
• A configuration protocol
• A protocol that establishes trust between
previously unrelated parties
• In other words:
ICAP defines the how, not who, when or why
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iCAP Makes Content Smarter!
iCAP Enables Local Services
Congested, Slow, Distant,
and/or Expensive Link
Clients
ISP
Internet
Network
(Large Backbone ISP)
To Server
Farms
Content distribution
network, or Cache
Local sources of content: Better for everyone
(client, network, server)
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Ad insertion
Why iCAP?
Language
Translator
Virus Checker
Web Server
or Proxy
Legend: iCAP servers for
Compute-Intensive
Operations
Transcoder
Content Filter
• Fast, Simple, Scalable
• Allows services to be customized
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ICAP Benefits
• Very simple operation
– ICAP builds on HTTP GET and POST
• No proprietary APIs Required
• Standards-based
• Leverages the latest Internet infrastructure
developments
• Fast, simple, scalable, and reliable
• Allows you to customise services
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iCAP general design
• Simple, simple, simple: CGI should be able to
turn a web server to an ICAP server
• Based on HTTP (+special headers)
• Three modes:
– Modify a request
– Satisfy a request (like any other proxy)
– Modify a response
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Request Modification
• The request is passed to the ICAP server
(almost) unmodified; just like a proxy would
• The ICAP server sends back a modified
request, encapsulated in response headers
– Body, if any (e.g. POST), may also be modified
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Request Modification
6
Client
1
Proxy cache
5
iCAP client
2
3
4
Origin
server
ICAP
Server
ICAP server modifies a request;
modified request continues on its
way
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Response Modification
• ICAP client always uses POST to send body
• Encapsulated in POST headers may also be:
– Headers used by user to request the object
– Headers used by origin server in its reply
• ICAP server replies with modified content
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Response Modification
Client
6
3
1
2
Proxy Cache
(ICAP Client)
4
Origin Server
5
ICAP
Server
ICAP server modifies a response from an origin server;
might be once, as the object is cached, or once per client served
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Request Satisfaction
6
1
Client
Proxy Cache
(ICAP Client)
2
ICAP
Server
5
Origin Server
4
3
ICAP server satisfies a request just like a proxy;
origin server MAY be contacted by ICAP server (or not)
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Infinite Variations
• Allows innovations. You choose 3rd party
applications.
• iCAP enables many different kinds of apps!
– Edge content sources can pass pages to ad servers
– Expensive operations can be offloaded
– Content filters can respond either with an unmodified
request or HTML (“Get Back to Work!”)
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Next Steps
• ICAP Supporters continue to enhance protocol
– Learn from solutions and fix “bugs”
– Build future functionality later
• IETF
– ICAP Forum will submit the specification to IETF for
draft RFC status in mid 2000
• Additional partners
– Software developers, infrastructure companies and
Internet content delivery service providers will be
solicited for participation
– Need to get on same page
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More on
– Important iCAP info at this site http://www.i-cap.org.
– Become an iCAP Participant by sending an e-mail to
mailto:[email protected]. A reply will be sent outlining
requirements.
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