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Networked Systems Research Projects @ McGill

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Networked Systems Research Projects _at_ McGill Muthucumaru Maheswaran Advanced Networking Research Lab School of Computer Science McGill University – PowerPoint PPT presentation

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Date added: 26 June 2018
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Title: Networked Systems Research Projects @ McGill


1
Networked Systems Research Projects _at_ McGill
  • Muthucumaru Maheswaran
  • Advanced Networking Research Lab
  • School of Computer Science
  • McGill University
  • Montreal, QC H3A 2A7

2
Outline
  • Ongoing Projects
  • Galaxy A Quality of Service Aware Public
    Computing Utility
  • RAN Resource Addressable Network
  • Trusted Gossip
  • GINI A Toolkit for User-Level Networks
  • Future Projects
  • RASAN Resource and Service Addressable Network
  • ALVIN Application Layer Virtual Internetworking

3
Motivation for RASAN
  • RASAN Resource and Service Addressable Network
  • New technology trends
  • Radio frequency IDs (RFIDs)
  • Pervasive wireless access
  • Very low cost/power sensors
  • Creating new resource and service discovery
    problems.
  • Examples of such discovery problems
  • Locating the best doctors and nurses who should
    be brought into a team to respond to particular
    emergency situations,
  • Locating and allocating resources and services
    that are necessary for conducting disaster relief
  • Logistical scheduling of different types
  • New discovery problems enabled by the evolution
    of network beyond a system that merely
    interconnects clients and servers via a packet
    switched network

4
What is RASAN?
  • RASAN is a real-time large-scale directory
    service that is targeted to include heterogeneous
    resources (wired, wireless, sensors, people, etc)
  • RASAN Goal
  • Flexible search (multiple search dimensions)
  • Minimal overhead
  • Fast response times
  • Late binding to determine real-time scenarios

5
What is RASAN
  • RASAN architecture
  • Organized in a P2P manner that self-organizes
    with resource arrival and departure events
  • Allows searches along multiple attribute spaces
    for locating resources and services
  • Uses space filling curves (SFC) to reduce
    multi-dimensional search to single dimensional
    problem (used in RAN with success on the Internet
    for locations)
  • Instead of a single SFC, it uses a hierarchy of
    SFCs
  • Enables multi-resolution searches to reduce error
    accumulation

6
RASAN Design Requirements
  • Scalable system Obvious scalability dimension is
    the number of devices. Others include number of
    search attributes and resource classes.
  • Dynamic system support Resources and services
    can attach and detach from the directory services
    without prior notice.
  • Heterogeneous and multi-resolution search RASAN
    is meant to search along multiple attribute
    dimensions. One way to make the search efficient
    is to perform the search in progressively
    increasing resolutions.

7
RASAN Design Req
  • Resource efficient implementation Due to its P2P
    nature, a RASAN kernel would run on each
    resource. To include resource challenged sensors
    into RASAN, the implementation should be able to
    run with limited memory and processing
    capacities. Further, resources with restrictive
    battery capacities should be able to participate
    in stub configurations with minimal transit
    traffic.
  • Operation with localized trust Resource should
    have some credential to establish it identity.
    Localized reputation should be used to evaluate
    behavior trust
  • Shared fate A resource or service that does not
    exist need not be indexed by the directory

8
Resource Addressable Network
  • RAN middle layer between services and resources.
  • Attribute-based and location-based discovery.

ODC Service
Naming the resources based on their attributes
Profile-based naming
Network positioning mechanism, assigning
coordinates for each node in the network delay
space
Landmark-aided positioning
Physical Resources
9
RAN Overlay
Location ID
Neighborhood pointers connect the rings
Hilbert indexing
decides the location
LAP
decides the ring
PBN/Hilbert indexing
Type rings
Profile ID
Resources with the same profile ID form a ring
Route pointers in the nodes creates the overlay
structure
10
Network Positioning
  • Network positioning assigning coordinates for
    the nodes in a virtual Cartesian space, from
    which real network delay can be predicted.

Internet
l12
Distance prediction
l12 v(x1-x2)2(y1-y2)2
y
(x2, y2)
Cartesian space
(x1, y1)
x
11
Landmark Aided Positioning
  • Landmark aided positioning (LAP) the network
    positioning scheme for RAN.
  • Using a set of landmarks.
  • Normal nodes
  • Select a subset of the total landmarks and ping
    them.
  • Run optimization algorithm to position themselves
    to minimize the total error in distance
    prediction.
  • Two phases of LAP
  • Landmark positioning positioning the landmarks.
  • Node positioning positioning the normal nodes.
  • Simplex and Spring algorithms.

12
Location-based Discovery
  • Finding a resource at specific coordinate/range
  • Multidimensional search.
  • Chose Hilbert curve as the data structure.
  • Hilbert curve
  • A space filling curve.
  • Preserving proximity.
  • Hierarchical Hilbert index ? location ID (LID).

13
Location-based Discovery (cont)
Routing table at node with LID 2.3.3.1.0
  • Routing table for location-based discovery.
  • Non-zero error in pings justifies fixed length
    LIDs.
  • Ring pointers ensuring connectivity jump
    pointers enhancing route complexity.
  • Average search hop complexity h (approx. level)
    ? O(1).

14
Profile-based Discovery
  • Discovery systems implements naming schemes
  • Label-based naming (LBN) DNS, IP Address.
  • Scalable, but not flexible.
  • Description-based naming (DBN) LDAP.
  • Flexible, but with high overhead due to
    information maintenance, complex matching
    algorithms.
  • Introducing profile based naming (PBN)
  • Labels popular attribute-value combinations.
  • Combines the goods of LBN and DBN.
  • Can not discover all the attribute-value
    combinations.
  • Trading off flexibility (performance) for
    scalability.

15
Profile-based Discovery (cont)
Profile-based naming
profiles
profile space
description
1
2
3
Profile IDs
description space
Profile 1 Intel/AMD, 512MB 0. Profile 2
Intel with 1GB 1.0 Profile 3 Intel/AMD,
gt 1GB 1.1,1.2
  • Profile-based routing table is very similar to
    location-based routing table.

16
The Galaxy Architecture
  • The following diagram shows a proposal for Galaxy
    architecture

17
Trust and Incentive Management
  • Public resources remain under control of local
    agents whose behavior may change randomly
  • resource sharing in hostile and friendly
    environments
  • Challenges in trust management in a PCU
  • Internet-scale
  • manage vast pool of distributed resources
  • cross boundary autonomous
  • span across administrative domains
  • handle localized policies varied level of trust
    requirement
  • reliable exchange of peer behavior
  • ensure fair resource exchange resource
    participation

18
GRMS Trust Management Model
Resource Brokers (RBs)
RB1B
Resource Peers (RPs)
RB1A
RB2A
RP2B
RP1B
RP3A
RP2A
Domain B
Domain A
19
Trust Hierarchy
  • Hierarchy local, global trust
  • Helps to reduce overhead needed for
    computing trust
  • scalable flexible localized policing

RB1B
local trust
RP1B
global trust
RP1A
domains are not connected in hierarchy
20
PCU Operations for Allocation
RB1B
RB1A
RP1B
RP1A
21
Negotiation Trust Evaluation
RB1A computes RP1B s global trust GT_ RP1B
LT_RP1B x REP_DBA
RB1B recommends RP1B to RB1A based on RP1B s
local trust LT_ RP1B
Domain C
RB1B
RB1A
REP_DBA reputation of Domain B as measured by
Domain A
RP1B
RP1A
Provider
Resource access is authorized if RB1A considers
GT_RP1B value as trustworthy
Security/fairness mechanisms ensure that RBs and
RPs do not collude or lie to each other
Domain B
Requestor
Domain A
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