Transcript Mohammed and the Mou.. - Computer Engineering Research Group

Mohammed and the Mountain
Eric Jul
DIKU
Department of Computer Science
University of Copenhagen
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Mohammed and Mount Safa
IF THE MOUNTAIN WILL NOT COME TO MOHAMMED,
MOHAMMED WILL GO TO THE MOUNTAIN
"If one cannot get one's own way, one must adjust to the
inevitable. “
The legend goes that when the founder of Islam was asked to
give proofs of his teaching, he ordered Mount Safa to come to
him. When the mountain did not comply, Mohammed raised
his hands toward heaven and said, 'God is merciful. Had it
obeyed my words, it would have fallen on us to our
destruction. I will therefore go to the mountain and thank God
that he has had mercy on a stiff-necked generation.”
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Data and Computation
Source
Data
Destination
Question: where should computation go?
Near the source? Or near the destination?
Or somewhere in the middle?
Shall the mountain of data go to the destination?
OR
Shall the computation be moved toward the source?
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Problem Statement
At which points should the data be processed?
And how much? (Just filtering? Content-based?)
Close to destination:
• may be network-inefficient
Close to source:
• more complicated
• may require non-trivial cooperation in network
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Main Point:
Spectrum: Moving Data or Computation
Source
Destination
Data
Spectrum ranging from moving all data to destination to moving
all computation to the source
Broker
Publisher
Broker
Subscriber
Broker
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Overview
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Mobility of Data and Computation
Grid Computing Example
Mobile Objects in Emerald
Group Communication in Emerald
Mobile Grid Applications – Evil Man
Advice from experiences with dozens of Ph.D. projects
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Mobility of Data and Computation
What is computation?
Pub-sub systems: just filtering?
Flooding vs. Match-first
Flooding: filter at destination – simple, but network inefficient
Match-first: network efficient
Moving work closer to source as to reduce data transferred.
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Motivating Example with huge data sets
The Large Hadron Collider at CERN
ATLAS project
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LHC – Large Hadron Collider
Atlas project
All of LHC…
Problem: Huge amounts of data
The problem is the large amount of data (Petabytes!) and the
geographical distribution of the scientists wanting to look at /
compute upon the data
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LHC Data Distribution
CERN
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Data is collected at CERN & distributed to ~12 data centers.
Data is then distributed to sub-centers.
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Programs and Data Meet Up
Scientists can access the data and perform computation by
submitting jobs to the sub-centers, e.g., via a Grid middleware
So both computation and data are moved – and meet at Grid
centers
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Techniques to Consider
There are a number of general ideas/schemes to consider:
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Compressing Data
Decoupling data and metadata, e.g., video & videoinfo
Content Routing Protocol (Pascal’s talk)
Caching
Multicast
Providing Mobile Objects and Mobile Computation
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Mobile Objects
Problem: efficient placement of data and computation in a
distributed system
A solution: an Object-Oriented Language with:
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objects
mobility of objects
transparent remote calls
group communication for pub-sub
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Mobile Objects in Emerald
Developed 1984-1988 at the University of Washington
Distributed language and run-time support system
Emerald is an OO language:
• “Pure” OO like Smalltalk – all data represented as objects (no
primitive types)
• Algol-family syntax (statements are NOT objects)
• Process concept (threads)
• Synchronization (Hoare monitors)
• Conformity based type system (worth several talks in itself)
• Like Java, but simpler
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Distribution Features
Concept of location: A node is merely a machine
(within a semi-closed network)
• Mobility: move X to Y
• Attachment allows groups to be moved
• Location: loc <- locate X
• “Remote” object invocation – transparent! X.f
• Checkpoint: stable version to disk
• Node failure: failure handler, unavailability
• Immutable objects (instead of primitives)
Emerald Spring Cleaning
Collector
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Example: Kilroy
const Kilroy == object Kilroy
process
var i:
Integer <- 0
var myNode:
Node <- locate self
var myList:
Nodelist
var remoteNode:
Node
myList <- myNode.getActiveNodes
for (i <- 0; i < myList.upperbound; i <- i+1)
remoteNode <- myList(i)$theNode
move Kilroy to remoteNode
end loop
end process
end Kilroy
Emerald Spring Cleaning
Collector
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Example: Call By Move
const CallByMove == object CallByMove
process
var myData:
LocalDataSet
var myHomeNode:
Node <- locate self
var remoteNode:
Node
X.f[move myData]
move self to X
X.f[myData]
move self to myHomeNode
end process
end Kilroy
Emerald Spring Cleaning
Collector
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Example: Call By Visit
const CallByVisit == object CallByVisit
process
var myData:
LocalDataSet
var myHomeNode:
Node <- locate self
var remoteNode:
Node
X.f[visit myData]
move self to X
X.f[myData]
move self to myHomeNode
end process
end Kilroy
Emerald Spring Cleaning
Collector
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Group Communication
Added group communication to Emerald
Introduce a group concept and a group multi-cast call
Calling a function on a group object calls all the group members
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Group Communication
const GroupExample == object GroupExample
process
var subscriberGroup: Group.of[Subscriber]
subscriberGroup.Add[s1] ...
mySubscriberGroup <- subscribeGroup.getGroup
% using a group
mySubscriberGroup.notify[data]
end process
end GroupExample
Emerald Spring Cleaning
Collector
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New Problem Area
Switching to an entirely new problem area
Problem:
moving applications to Grid computing sites
Trust code?
Trust Operating System?
Proposed solution – named Evil Man
Move entire application including OS into a Grid Cluster
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OS Migration
Desire: move entire running applications
OS solution: pick up and move the ENTIRE OS
First, use a Virtual Machine Monitor (Zen, VMWare) to separate
OS from hardware
Second, put migration code inside OS – it has all the needed
functionality already – and get a Self-migrating OS
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Laundromat Computing with Evil Man
Evil Man is based on:
• Using virtual machines as containers for untrusted code
• Using live VM migration to make execution independent of
location
• Using micro-payments for pay-as-you-go computing
Evil Man uses Virtual Machine Migration to move actively
executing applications into a Grid Cluster – e.g., to numbercrush data from the Atlas project at CERN
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Evil Man
Prototype cluster management system developed at the Danish
Center for Grid Computing at the University of Copenhagen
One great Ph.D. student deserves most of the credit for the work:
Jacob Gorm Hansen
2008 Eurosys Rodger Needham award for Best Systems Ph.D.
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Virtual Machine Migration
process
process
file
VMM
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VMM
A VM is self-contained, including open files, sockets, shared memory
Interface to virtual hardware is clearly defined – and simple
All dependencies abstracted via fault-resilient network protocols
VMs typically long-lived
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Why Migration Downtime Matters
•Upsets users of interactive applications such as games
•May trigger failure detectors in a distributed system
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Live Migration Reduces Downtime
•The VM can still be used while it is migrating
•Data is transferred in the background, changes sent later
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Self-Migration
Letting the OS move itself
• Pre-copy migration relies on:
– TCP/IP for transferring system state
– Paging for tracking of write accesses
• A VM is self-paging & has a
TCP/IP stack
• Reduce VMM complexity by
performing migration from within
the VM
• No need for networking, threading
or crypto in the TCB
VM
VM
VM
TCP/IP
TCP/IP
TCP/IP
Paging
Paging
Paging
Migration
VMM
Hardware
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An Inspiring Example of Self-Migration
von Münchhausen in the swamp
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First Iteration
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Delta Iteration
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Snapshot/Copy-on-Write Phase
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Dealing with Network Side-effects
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The copy-on-write phase results in a network fork
“Parent” and “child” overlap and diverge
Firewall network traffic during final copy phase
All except migration-traffic is silently dropped in last phase
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Impact of Migration on Foreground Load
httperf
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Laundromat Computing
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Pay-as-you-go Processing
• Laundromats do this already
– Accessible to anyone
– Pre-paid & pay-as-you-go
– Small initial investment
• We propose to manage clusters
the same way
– Micro-payment currency
– Pay from first packet
– Automatic garbage collection
when payment runs out
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Token Payments
• Initial payment is enclosed in
Boot Token
• Subsequent payments keep the
VM alive as resources are
consumed
• Use a simple hash-chain for
recurring payments
– Hn(s), Hn-1(s), …, H(s), s
• Boot Token signed by trusted
broker service
• Broker handles authentication
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Injecting a New VM
• Two-stage boot loader handles different incoming formats
– ELF loader for injecting a Linux kernel image
– Checkpoint loader for injecting a migrating VM
• “Evil Man” service decodes Boot Token “magic ping”
• Evil Man is 500 lines of code + network driver
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Laundromat Summary
• Pros:
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Simple and flexible model
Hundreds instead of millions LOC
Built-in payment system
Hypercube forks
Self-scaling applications
• Cons:
– Needs direct network access
– Network load
– Problems migrating across
firewalls
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Yet another switch
Some advice from my experience with a score of Ph.D. projects
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Things to think about
• State your problem
• Spread out initially, then focus
• Stay close to your focus
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State your problem
• Be able to answer the question: ”What is the problem you are
trying to solve?” -- don’t end up with a ”solution in search of
a problem!”
• State your problem
• Often good with a motivating example
• Often good with a narrow, less-general example that also can
be used as a milestone – even a killer app
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Spread out initially, then focus
• Spread out, then narrow & focus (diagram)
• Establish a dissertation title when you start to focus
• Remember that your thesis focus is a SUBSET of all you have
done – your dissertation is NOT a logbog of your work – nor a
description of ”see what I have done” (diagram)
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Stay close to your focus
• Write a draft of your conclusion early – use it for focus
• Concentrate on your main contribution (diagram)
• Try for a good, narrow example that is implementable and can
be a good demo and an excellent milestone
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Walk away with…
• Spectrum: moving data to computation site or moving
computation to the data site
• Spectrum underlies many decisions concerning architecture of
pub-sub systems
• Moving data is well-known; moving computation more tricky
• Don’t have a solution in search of a problem
• State the problem clearly – and state which part of it you will
solve – use examples/killer apps
• As you go narrow your focus
• Shot for a good contribution
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Questions?
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The End
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