Recent Advances in Internet, Cluster, Grid, and Pervasive Computing

Recent Advances in
Internet, Cluster, Grid,
and Pervasive Computing
Kai Hwang
Internet and Pervasive Computing Laboratory
University of Southern California
Email: kaihwang@usc
usc.edu
Web site: http://ceng
ceng.usc.edu/~
/~kaihwang
May 20, 2003 K. Hwang at USC 1

Information Technology:
Today and the Future
g What are hot in Year 2001
1.5 GHz microprocessors, 256 Mb RAM,
Gigabit Ethernet, Unix/Linux,Windows NT,
Clusters, Java, Internet, digital TV, Smart
Network Devices, Pervasive Computing, etc.
g How about 3 years from now
2-4 GHz microprocessors, 1-4 Gb RAM,
flash memory (NVSM), 1 Tbps LAN, Satellitebased
WWW, Petaflops Supercomputers,
Cluster OS, Grid Metacomputing, etc.
May 20, 2003
K. Hwang at USC
2

….. a billion people interacting
with a million E-Businesses
E
with a trillion intelligent
devices interconnected ..…
Lou Gerstner,
IBM Chairman and CEO
Mainframe Computing (50 -70’s)
Personal Computing (80 - 90’s)
Cluster Computing (90 - 00’s)
Pervasive Computing (00 - )
Grid Metacomputing (10 - )
May 20, 2003
K. Hwang at USC
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Attributes
Networking
Network-Based
Computing Paradigms
Cluster
Computing
System Area
Network
Grid
Computing
Internet or Wide
Area Network
Pervasive
computing
Wireless LAN,
IrDA
Communication
Protocols
Mostly
TCP/IP
IP/ATM, DNS
IP/HiPPi
HiPPi,
routing
WAP: Wireless
Application
Protocol
Operating
system support
Environment
and tool sets
Most UNIX
and Windows
variants
MPI, PVM,
Score, Codine
Most UNIX,
Windows
variants
GLOBUS, GSI,
LEGION,
CONDOR
Pocket PC (CE),
Palm OS, Inferno,
Chorus OS
JINI, UPnP,
Bluetooth,
May 20, 2003
K. Hwang at USC
4

Scalable clusters providing SSI
services are gradually replacing the
SMP, cc-NUMA, and MPP in Servers,
Web Sites, and Database Centers
May 20, 2003
K. Hwang at USC
5

Issues in Cluster Design
g Size Scalability (physical & application)
g Enhanced Availability (failure management)
g Single System Image (Middleware, OS extensions)
g Fast Communication (networks & protocols)
g Load Balancing (CPU, Net, Memory, Disk)
g Security and Encryption (clusters and Grids)
g Distributed Environment (User friendly)
g Manageability (Jobs and resources )
g Programmability (simple API required)
g Applicability (cluster- and grid-awareness)
May 20, 2003
K. Hwang at USC
6

Trojans Linux Cluster
with Middleware for Security
and Checkpoint Recovery
Programming Environments
(Java, EDI, HTML, XML)
Web Windows
User Interface
Other Subsystems
(Database, OLTP, etc.)
Single-System Image and Availability Infrastructure
Security and Checkpointing Middleware
Linux Linux Linux
Pentium
PC
Pentium
PC
Pentium
PC
Gigabit Network Interconnect
May 20, 2003
K. Hwang at USC
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Scaled Workload leads to Linear
Speedup on The Trojan Cluster
May 20, 2003
K. Hwang at USC
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Distributed RAID Embedded in a
Cluster or in a Storage-Area Network:
g I/O Bottleneck in Cluster Computing
n CPU/Memory and disk-IO speed gap widens
as µP P doubles in speed every year
n Pervasive applications are often I/O-bound
g Disks connected to hosts are often subject to
failure by hosts themselves. Distributed RAID has
much higher availability by fault isolation,
rollback recovery, and automatic file migration.
Source: K. Hwang, H. Jin, and R. S. Ho, “Orthogonal Striping and Mirroring
in Distributed RAID for I/O-Centric Cluster Computing”, IEEE-Trans. on
Parallel and Distributed Systems, accepted July 13, 2001. (in press)
May 20, 2003
K. Hwang at USC
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Distributed RAID-x x Architecture
Cluster Network
Node 0 Node 1 Node 2
Node 3
P/M
P/M
CDD
P/M
CDD
P/M
CDD
CDD
B0
B12
B24
B25’
B26’
B27’
D0 D1 D2 D3
B1
B13
B25
B14’
B15’
B24’
B2
B14
B26
B3’
B12’
B13’
B3
B15
B27
B0’
B1’
B2’
D4 D5 D6
D7
B4
B16
B28
B29’
B30’
B31’
B5
B17
B29
B18’
B19’
B28’
B6
B18
B30
B7’
B16’
B17’
B7
B19
B31
B4’
B5’
B6’
B8
B20
B32
B33’
B34’
B35’
D8 D9 D10 D11
B9
B21
B33
B22’
B23’
B32’
CCD: Cooperative Disk Drivers
B10
B22
B34
B11’
B20’
B21’
B11
B23
B35
B8’
B9’
B10’

User
Level
Remote Disk Access using a Central
NFS vs. . using Cooperative Disk
Drivers in a Distributed RAID
User
Application
NFS Server
User
Level
Kernel
Level
1
NFS Client
6
5
2
3
Traditional
Device Driver
4
Kernel
Level
Client side
Server side
(a) Parallel disk I/O using the NFS in a server/client cluster.
User
Level
Kernel
Level
1
(b) Using CDDs to achieve a SIOS in a serverless cluster.
May 20, 2003
User
Application
CDD
Client side
6
5
2
K. Hwang at USC
NFS Server
is bypassed
CDD
Server side
4
3
User
Level
Kernel
Level
11

Parallel Write Performance of four
Distributed RAIDs on USC Linux Cluster
18
Aggregate Bandwidth (MB/s)
16
14
12
10
8
6
4
2
0
RAID-x
Chained Declustering
RAID-10
RAID-5
2 4 8 12 16
Disk Numbers
May 20, 2003
K. Hwang at USC
12

with Adaptive Supersampling
•
Scene
Description
Parallel Polygon Rendering
root
PU 1
PU 1
PU 1 PU 1
PU 2 PU 2 Data PU 2 PU 2
redistribution
PU n
root
Display
Geometric
database
Decompose
dataset by object
Pre-processing phase
Simplify Model
(Object-parallelism)
Transformation
phase
Transform
Model
Rasterization phase
Rasterize Model
Rendering Looping
Collect partial
images
(Image parallelism)
3D objects 2D polygons pixels
Display
Source: S. W. Lin, R. W. Lau, K. Hwang, X. Lin, and P. Y. Cheung,
“Adaptive Parallel Rendering on Multiprocessors and Workstation Clusters “,
IEEE Trans. on Parallel and Distributed Systems, Vol.11, No.3, March 2001.
May 20, 2003
K. Hwang at USC
13

Speedup Performance of Two Parallel Renderer on the
SGI SMP Server and Unix WS Cluster at HKU
High-Performance Computing Research Lab.
Speedup, S
7
6
5
4
3
2
1
0
Our renderer onSMP
Crow's renderer on SMP
Our renderer on Cluster
Crow's renderer on Cluster
2 4 6 8 10 12
Machine Size, n
May 20, 2003
K. Hwang at USC
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Securing Clusters, LANs, Intranets,
WANs, , Grids, and Internet Resources
with intrusion detection and automatic
recovery from malicious attacks
Fully
secured
Increasing
Security
No
protection
Design Goals: A distributed network architecture
with Dynamic Security and Privacy (DSP)
supporting fine-grain resources access
with automatic intrusion prevention,
Intranets or WANs
protected by gateway
firewalls under a
Server
Clusters
or Web sites
with no security
protection
static policy, fixed
cryptography
and limited
scalability
detection and responses, based on
dynamic security policies,
multicast protocols, and
adaptive cryptographic
engines in a scalable
network environment
Cluster/LANs Intranet/WANs Grid/Internet
Increasing scalability
May 20, 2003
K. Hwang at USC
15

Security Threats
in Mobile Agent-Based Systems
Masquerading - Identity misuse
Denial of Service - Resource occupation
Unauthorized Access - Intrusions
Repudiation - Dispute services provided
Eavesdropping - Secrecy interception
Alteration - Data/code integrity
Copy and Reply - Clone of agents
May 20, 2003
K. Hwang at USC
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Security Component
Technologies
Firewalls and Cryptography
Cluster Middleware for Security
Anti-virus and Immune Systems
Intrusion Detection and Response
Distributed Software RAIDs
Security & Assurance Policies
May 20, 2003
K. Hwang at USC
17

Distributed Micro-Firewalls
Firewalls for Dynamic
Security supported by IPChains, , Mobile Agents, RMI, or CORBA.
M. Gangadharan and K. Hwang, “ Intranet Security with Micro Firewalls and
Mobile Agents for Proactive Intrusion Response”, IEEE International Conference
on Computer Networks and Mobile Computing, Beijing, China October 16-19, 2001.
May 20, 2003
K. Hwang at USC
18

Adaptive Security Control
Agents detect threats, learn from intrusion
patterns, and update security safeguards
Adaptive
Security
=
Security
Safeguards
• Micro-Firewalls
•Authentication
•Access control
•Cryptography
Detect network and
threat conditions
+ +
Detect software
vulnerabilities
Adaptive Responses
May 20, 2003
K. Hwang at USC
19

Distributed Firewall Architecture
built in Trojans Cluster at USC
Nodes with Micro-Firewall
Policy Manager
Gatewa y Fire wa ll
Switch
Network
Router
Router
Demilitarized
Zone
Internet
Nodes with Micro-Firewall
May 20, 2003
K. Hwang at USC
20

Implementing Micro-Firewall
in The Linux Kernel
User
Programs
User
Programs
User
Programs
User
Programs
User Space
Kernel Space
System call interface
Packet Filter
Memory, file and Process
Managers
Micro-firewall
TCP/IP Stack
Anomaly
Detection
Access
Logging
Disk Drives
Main Memory
Network Cards
Hardware
K. Hwang and M. Gangadharan, “Micro-Firewalls for Dynamic Security with
Distributed Intrusion Detection”, IEEE International Symposium of Network
Computing and Applications, Cambridge, MA. Oct. 8-12, 2001
May 20, 2003
K. Hwang at USC
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Adaptive Crytographic Engine for IPSec
A. Dandalis and V. K. Pransanna, “ An Adaptive Cryptographic Engine
for Internet Protocol Security Architectures”, submitted to IEEE Trans. on
VLSI Systems, March 2001, USC High-Performance Computing Lab.
ACE
REQUEST
Rijndael: NIST selection for AES Standard
State-of-the-art software-based implementation
Algorithm A
Configuration 1
Configuration 2
Configuration
Controller
Our FPGA-based implementation
µsec
Mbits/sec
400
…
Algorithm B
Configuration 1
2
300
Configuration 2
…
…
…
…
FPGA
1
200
…
Cryptographic
Library
100
INPUT
(Data, Key)
OUTPUT
(Data)
0
Key-Setup Latency
Throughput
0

Comparison of Agents, CORBA,
and RMI for Security-Policy
Update on Intranets or Clusters
Capabilities Mobile Agents CORBA Middleware RMI Middleware
Central policy
coordination
Reaction time
to policy
change
Hosts fortified
with microfirewalls
Security
Mechanisms
Update Process
Termination
Autonomous and require
no coordination once
dispatched
The time increases with
the number of agents
dispatched.
Agents carry most
mechanisms required to
update security policy
Use authentication and
encryption. Still prone to
attacks from hosts/agents.
Multiple agents used
autonomously, Policy
update always completes
The policy manager
coordinates all
communications
Faster than agents or
RMI to react to a
policy change
Requires the ORB
middleware support on
all hosts in the Intranet
Security implemented
with the CORBASec.
Implemented at
application level using
RPC-like semantics
The policy manager acts as
the RMI registry to
coordinate among all nodes
RMI slower than CORBA
and faster than agent based
system for policy update
Requires JVM to be present
on all the hosts.
Security is the best among all
three, implemented with the
Java sandbox model.
Implemented at application
level using RPC-like
semantics
May 20, 2003
K. Hwang at USC
23

Distributed Agent-Based
Intrusion Detection System
Defined
Security Policy
Decision Making
System
Agent
Controller
Data-
Mining
Agents
Intrusion
Database
Agent Security
Infrastructure
Agent PKI
Agent Name Service
Communication Agents
May 20, 2003
K. Hwang at USC
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Distributed Intrusion Detection
and Response in a Linux Cluster
3
3
May 20, 2003
K. Hwang at USC
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Pervasive Computing
Small inexpensive computers and sensors in every
device and appliance - handheld or portable in offices,
homes, cars, stores, classrooms, and factories, etc.
These smart devices are networked to each other and
the Internet (3 trillion devices). They can sense and
react intelligently to the environment changes.
Information appliances, integration into Internet
through phone lines, cables, or wireless LANs
(HomeRF, Bluetooth) ) using protocols Jini, , Inferno, etc.
Neworked household, automobile, personal assistants,
smart spaces (offices, classrooms, etc), and express
delivery (BodyLAN(
BodyLAN) ) services, etc.
May 20, 2003
K. Hwang at USC
26

Information Appliances
vs Personal Computers
US Shipments, Consumer Devices, In Millions
25
20
PCs
IAs
15
10
5
0
1997 1998 1999 2000 2001 2002
Source: US/NIST Information Technology Lab.

Investing in Infrastructure Today for Tomorrow
Active badges
Smart
phones
13 million in 2006
palm-size
computers
28 million in 2006
Screen
phones
E-Commerce
$300 billion in 2001
Forget-Me-Not
May 20, 2003
K. Hwang at USC
Source: NIST/IT Lab.
28

The Rome Architecture for Pervasive
Job Scheduling at Stanford University
Scanner
Web Form
Frontend
Frontend
Frontend
Semantic Translator
Trigger Manager
Internet
Unit
Manager
Auto PC
Unit
Manager
PDA
Unit
Manager
n n n

Computing and Information
Grid for The Future
A metacomputing infrastructure that couples
n computers, information appliances,
(PCs, WSs, , servers, clusters,supers,
laptops, notebooks, PDA, etc,)
n mobile software (e.g., renting expensive
applications on demand),
n distributed databases (e.g., transparent
access to human genome database),
n smart network devices (e.g., Internet
cars, express services, etc.),
n and People ( You, me, and every one ).
May 20, 2003
K. Hwang at USC
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Basic Concept of Grid Computing
(Courtesy of Foster and Kesselman, , 2000)
May 20, 2003
K. Hwang at USC
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Grid Application-Drivers
New applications enabled by coupling
computers, databases, people, etc.
n (distributed) Supercomputing
n Collaborative engineering
n Data-miming in E-commerceE
Computing Power as an Utility Industry
n Renting Software
n Renting CPU Cycles
n On-demand computing
May 20, 2003
K. Hwang at USC
32

Security Issues in Computational Grids
Grid components can insulate themselves from
security breaches elsewhere
Only authorized principals are allowed to use the
available resources
Detection and Recovery from intrusions that occur
from outsiders and compromised insiders.
All communications among grid components
should be immune to tampering and tapping.
The grid should establish the identity of a principal
using the resources reliably across the system.
May 20, 2003
K. Hwang at USC
33

Underlying Technologies for E-Commerce,
E
Digital Government, and E-EverythingE
Everything
RAS
Personalization
Enabling Technologies
Core Technologies
Data Warehousing
Performance
Measurement
Relationship
Management
Decision-Support Technologies
OLAP
HTML/XML
Unix/Linux/NT Security Fast Messaging
Networking RDBMS
Scalability
Open Standards
COM/DCOM/DNA
CORBA/IIOP
Knowledge
Management
Billing/
Payment Systems
Advertising/
Promotions
Data Mining
Supply Chain
Management
May 20, 2003
K. Hwang at USC
(Source: Kalakota and Robinson, e-Business:
Roadmap for Success, Addison-Wesley, 1999)
34

Increasing Demand of Secure Cluster,
Grid, and Pervasive Applications:
LANs, clusters, Intranets, WANs, Grids, and the
Internet all demand security protection, faulttolerance,
and hacker-proof operations, which are
crucial to a digital society and economy.
Distributed storage-area networks demands
HW/SW support of a single I/O space and global file
and database management in all network-based
computing applications.
Many innovative applications exist in remote
network services, E-commerce, telemedicine,
distance education, collaborative design, pervasive
computing, digital entertainment, etc.

Charles Darwin (1809 - 1882)
“It is not the
strongest of
species that
survive, nor the
most intelligent,
but the one
most adaptable
to change.”
May 20, 2003 K. Hwang at USC 36