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CIS 6930.008: Internet-Scale Networked Systems


CIS 6930.008: Internet-Scale Networked Systems Adriana Iamnitchi (Anda) – PowerPoint PPT presentation

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Title: CIS 6930.008: Internet-Scale Networked Systems

CIS 6930.008 Internet-Scale Networked Systems

Adriana Iamnitchi (Anda)

Contact Info
  • Email
  • Office ENB 334
  • Office hours Wed 2-4 and by appointment (email
  • Course page http//

CIS 6930.008 Course Goals
  • Primary
  • Gain deep understanding of fundamental issues
    that affect design of large-scale federated
    distributed systems
  • Map primary contemporary research themes
  • Gain experience in distributes systems research
  • Secondary
  • By studying a set of outstanding papers, build
    knowledge of how to present research
  • Learn how to read papers evaluate ideas

What Ill Assume You Know
  • Basic Internet architecture
  • Basic principles of distributed computing
  • Asynchrony (cannot distinguish between
    communication failures and latency)
  • Partial global state knowledge (cannot know
    everything correctly)
  • Failures happen. In very large systems, even rare
    failures happen often
  • If there are things that dont make sense, ask!

Examples of Distributed Systems
ATT web
Gnutella network
The Internet
A Sensor Network
Definition (a version)
  • A distributed system is a collection of
    autonomous, programmable, failure-prone entities
    that are able to communicate through a
    communication medium that is unreliable.
  • Entitya process on a device (PC, PDA, mote)
  • Communication MediumWired or wireless network
  • Internet-Scale
  • Spanning multiple institutional or network
    (DNS) domains
  • (Much) Larger than cluster

This semesters Theme (a proposal)
  • Exploiting
  • Emergent Behavior
  • in Large-Scale Distributed Systems

Filecules and Small Worlds in a Scientific
Workload Characteristics and Significance
Grid Resource-Sharing Environment
  • Users
  • 1000s from 10s institutions
  • Well-established communities
  • Resources
  • Computers, data, instruments, storage,
  • Owned/administered by institutions
  • Applications data- and compute-intensive
  • Approach common infrastructure

The Problem
  • We have now
  • Mature grid deployments running in production
  • We do not have yet
  • Quantitative characterization of real workloads.
  • How many files, how much input data per process,
  • And thus, benchmarks, workload models,
    reproducible results
  • Costs
  • Local solutions, often replicating work
  • Temporary solutions that become permanent
  • Far from optimal solutions
  • Impossible to compare alternatives on relevant

Still, Why Should We Care?
  • Impossibility results, high costs Tradeoffs are
  • Solution Select tradeoffs based on
  • User requirements (of course)
  • Usage patterns
  • Patterns exist and can be exploited. Examples
  • Zipf distribution for request popularity (web
    caching) Breslau et al., Infocom99
  • Network topology

Partial Topology
Random 30 die
Targeted 4 die
from Saroiu et al., MMCN 2002
The DØ Experiment
  • High-energy physics data grid
  • 72 institutions, 18 countries, 500 physicists
  • Detector Data
  • 1,000,000 Channels
  • Event rate 50 Hz
  • So far, 1.9 PB of data
  • Data Processing
  • Signals physics events
  • Events about 250 KB, stored in files of 1GB
  • Every bit of raw data is accessed for
  • Past year overall 0.6 PB
  • processes PBs/year
  • processes 10s TB/day
  • uses 25 50 remote computing

Filecules and Small Worlds in Scientific
Communities Characteristics and Significance
  • Joint work with
  • Matei Ripeanu (UBC) and
  • Ian Foster (ANL and UChicago)

Yellow Submarine Les Bonbons
No 24 in B minor, BWV 869 Les Bonbons
Yellow Submarine Wood Is a Pleasant Thing to
Think About
Wood Is a Pleasant Thing to Think About
The DØ Collaboration
6 months of traces (January June 2002) 300
users, 2 million requests for 200K files
Small average path length
Small World!
Large clustering coefficient
Small-World Graphs
  • Small path length, large clustering coefficient
  • Typically compared against random graphs
  • Think of
  • Its a small world!
  • Six degrees of separation
  • Milgrams experiments in the 60s
  • Guares play Six Degrees of Separation

Other Small Worlds
D. J. Watts and S. H. Strogatz, Collective
dynamics of small-world networks. Nature,
393440-442, 1998 R. Albert and A.-L. Barabási,
Statistical mechanics of complex networks, R.
Modern Physics 74, 47 (2002).
Web Data-Sharing Graphs
Data-Sharing Relationships in the Web, Iamnitchi,
Ripeanu, and Foster, WWW03
DØ Data-Sharing Graphs
28 days, 1 file
7days, 1file
KaZaA Data-Sharing Graphs
2 hours 1 file
28 days 1 file
1 day 2 files
4h 2 files
12h 4 files
7day, 1file
Small-World File-Sharing Communities, Iamnitchi,
Ripeanu, and Foster, Infocom 04
Interest-Aware Data Dissemination
Interest-Aware Information Dissemination in
Small-World Communities, Iamnitchi and Foster,
Current Work Tagging Communities
Tracking User Attention in Collaborative Tagging
Communities, Elizeu Santos-Neto, Matei Ripeanu,
and Adriana Iamnitchi, Workshop on Contextualized
Attention Metadata (CAMA'07), Vancouver, Canada,
June 2007.
DØ Workload Characterization
  • Joint work with
  • Shyamala Doraimani (USF) and Gabriele Garzoglio

DØ Traces
  • Traces from January 2003 to May 2005
  • 234,000 jobs, 561 users, 34 domains, 1.13 million
    files accessed
  • 108 input files per job on average
  • Detailed data access information about half of
    these jobs (113,062)

Contradicts Traditional Models
  • File size distribution
  • Expected log-normal. Why not?
  • Deployment decisions
  • Domain specific
  • Data transformation
  • File popularity distribution
  • Expected Zipf. Why not? (speculations)
  • Scientific data is uniformly interesting
  • User community is relatively small

Filecules Intuition
Filecules General Characteristics
Filecules in High-Energy Physics Characteristics
and Impact on Resource Management, Adriana
Iamnitchi, Shyamala Doraimani, Gabriele
Garzoglio, HPDC06
Filecules Size
  • Filecules of different sizes
  • Largest filecule17 TB or 51,841 files
  • 28 mono-file filecules

Consequences for Caching
  • Use filecule membership for prefetching
  • When a file is missing from the local cache,
    prefetch the entire filecule
  • Use time locality in cache replacement
  • Least Recently Used (classic algorithm)
  • Implemented
  • LRU with files and LRU with filecules
  • Greedy Request Value prefetching job
  • Does not exploit temporal locality
  • Prefetching based on cache content
  • Our variant of LRU with filecules and job

E. Otoo, et al. Optimal file-bundle caching
algorithms for data-grids. In SC 04
Comparison Caching Algorithms (1)
Comparison Caching Algorithms (2)
of cache change is a measure of transfer costs.
Summary Part 1
  • Revisited traditional workload models
  • Generalized from file systems, the web, etc.
  • Some confirmed (temporal locality), some infirmed
    (file size distribution and popularity)
  • Compared caching algorithms on D0 data
  • Temporal locality is relevant
  • Filecules guide prefetching

  • Workload characterization based on a HEP grid
  • Quantify scale (data processed, number of files)
  • Contradict traditional models
  • Patterns can guide system design
  • Filecules caching, data replication
  • Small world data sharing adaptive information
    dissemination, replica placement

AdministraviaPaper Reviewing (1)
  • Goals
  • Think of what you read
  • Get used to writing paper reviews
  • Reviews due by noon before class
  • Be professional in your writing
  • Have an eye on the writing style
  • Clarity
  • Beware of traps learn to use them in writing and
    detect them in reading
  • Detect (and stay away from) trivial claims.
  • E.g., 1st sentence in the Introduction
  • The tremendous/unprecedented/phenomenal
    growth/scale/ubiquity of the Internet

AdministraviaPaper Reviewing (2)
  • Follow the form provided when relevant.
  • State the main contribution of the paper
  • Critique the main contribution Rate the
    significance of the paper on a scale of 5
    (breakthrough), 4 (significant contribution), 3
    (modest contribution), 2 (incremental
    contribution), 1 (no contribution or negative
    contribution). Explain your rating in a sentence
    or two.
  • Rate how convincing the methodology is.
  • Do the claims and conclusions follow from the
  • Are the assumptions realistic?
  • Are the experiments well designed?
  • Are there different experiments that would be
    more convincing?
  • Are there other alternatives the authors should
    have considered?
  • (And, of course, is the paper free of
    methodological errors?)

AdministraviaPaper Reviewing (3)
  • What is the most important limitation of the
  • What are the three strongest and/or most
    interesting ideas in the paper?
  • What are the three most striking weaknesses in
    the paper?
  • Name three questions that you would like to ask
    the authors.
  • Detail an interesting extension to the work not
    mentioned in the future work section.
  • Optional comments on the paper that youd like to
    see discussed in class.

AdministraviaDiscussion leading
  • Come prepared!
  • Prepare discussion outline
  • Prepare questions
  • What ifs
  • Unclear aspects of the solution proposed
  • Similar ideas in different contexts
  • Initiate short brainstorming sessions
  • Leaders do NOT need to submit paper reviews
  • Main goals
  • Keep discussion flowing
  • Keep discussion relevant
  • Engage everybody (Ill have an eye on this, too)

  • Combine with your research if relevant to the
  • Get approval from all instructors if you overlap
    final projects
  • Dont sell the same piece of work twice
  • You can get more than twice as many results with
    less than twice as much work
  • Aim high!
  • Put one extra month and get a publication out of
  • It is doable (we have proofs)
  • Try ideas that you postponed out of fear its
    just a class, not your PhD.

AdministraviaProject deadlines (tentative)
  • January 30 1-page project proposal
  • Feb. 26 3-page literature survey
  • Know relevant work in your problem area
  • If implementation project, list tools, similar
  • March 31 5-page Midterm project due
  • Have a clear image of whats possible/doable
  • Report preliminary results
  • Last classIn-class project presentation
  • Demo, if appropriate
  • May 1
  • Final report due

Next Classed
  • Lectures on basics of distributed systems
  • Will start reading papers in about 2 weeks

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