Increasing robustness in the BioNetworking architecture: A Distributed Approach

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Increasing robustness in the Bio-Networking
architecture A Distributed Approach

• Vishakh
• UC Irvine School of Information Computer Science

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Introduction

• Problem Centralized means of providing network
  services wont cope.
• Solution Bio-Networking architecture (Bionet)
• Problem Bionet needs efficient mechanism to be
  robust.
• Solution Price Propagation

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Todays Internet The putative news site

• The CNN web server offers web pages with news.
• Users connect to this server and request pages.

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Todays internet Usage patterns and wastage of
resources

• Server bandwidth and capacity based on usage
  patterns.
• Resource wastage at most times

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Todays internet Another problem

• Spurts in service demand.
• Servers may not cope.
• E.g. online Starr Report in 98
• Failure rates
• CNN.com- 32
• MSNBC.com- 47
• Current methods arent robust!

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Todays Internet The Larger Problem

• Internet expanding and changing.
• Current centralized methods of providing services
  wont cope.
• We need a way to make services on the internet
  more dynamic

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A Solution!- The Bio-Networking Architecture

• The Bio-Networking Architecture (Bionet) is being
  developed by Dr. Tatsuya Suda and his team at
  UCI.
• Decentralized way to provide network services.
• Adaptive, scalable and evolvable.
• Based on biological systems such as ant colonies.

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Bionet Cyber-Entities

• Several agents knows as Cyber-Entities (CEs)
  instead of one central server.
• CEs travel across the network, providing services
  to users.
• CE populations adapt to number of users.

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Bionet The Problem

• Ideally
• CE population should constantly change to reflect
  user demand.
• CE distribution should constantly change to
  reflect user locations.
• In other words, need to maintain robustness.
• We need a mechanism to regulate reproduction and
  migration among CEs in an efficient manner.

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Using Economics

• Economics- how and where to allocate resources to
  benefit us optimally.
• In Bionet, CE population and distribution must be
  set to service users optimally.
• Market economies use prices- elegant
  decentralized way of gauging supply and demand.

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Using Economics Prices as indicators

• Suppose I am a trader of Llama milk in Peru.
• I look at prices in Peruvian cities.
• I distribute milk where I get can charge highest
  price.

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Using Economics Price equilibriums

• Ideally, supply and demand ironed out using
  prices.
• We have an equilibrium
• Efficient distribution of Llama milk.
• Traders and consumers happy.
• Why not use this for Bionet?

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Using Economics Prices and Bionet

• Users provide CEs with energy, i.e. they supply
  it.
• CEs look for energy, i.e. they demand it.
• Associate a price with energy, i.e. Energy
  Supply/demand

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Using Economics Price propagation

• Price Propagation (PP) is a mechanism for
  generating and distributing energy prices over a
  network.
• Prices can be used by CEs to decide whether to
  reproduce and where to migrate.

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Calculating Prices Getting local prices

• Performed by CEs on every node.
• Each CEs local price (LP) Energy supply on
  current node/ No. of CEs on current node

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Calculating Prices Getting Global Prices

• CEs calculate Global Price (GP) by averaging
  their LP with incoming prices.
• E.g. GP2 (3.3 5 20)/3 9.4

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Calculating Prices Propagating Prices

• GPs on each node are broadcast to connected
  nodes.
• E.g. Node2 sends its LP to Node 1 and Node 3

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Using Prices Migration

• If an incoming price is greater than the Local
  Price, then a CE probably needs to migrate.
• Each CE compares its Global Price to the Maximum
  Incoming Price (MIP).

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Using Prices Migration 2

• CEs calculate migration need (MN) MN
  (MIP-GP)/GP
• Each CE has a migration factor (?m).
• If MNgt ?m, CEs migrate in the direction of the
  MIP.

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Using Prices Migration 3

• CE marked on right migrates from Node 1 to Node
  2.

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Using Prices Reproduction

• If a CE notices that the Global Price is
  significantly more than a preset ideal GP, it
  will reproduce if it can.
• This increases energy demand and lowers the GP.

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Using Prices Death

• If a CE notices that the Global Price is much
  lower than the ideal preset GP, it can
  sacrifice itself.
• This brings down the GP and decreases demand for
  energy.

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Using prices The Big Picture

• Migration ensures that CEs always close to users.
• Reproduction ensures CEs are present in just the
  right numbers.

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Using prices The Big Picture 2
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Current Work

• PP is simple and elegant, yet many wrinkles to be
  ironed out.
• Refine current algorithms
• Comparing behavior with other algorithms.

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Future Work

• Formal verification of PPs efficiency.
• Integrating more advanced ideas from Economics
  and Biology.
• More fine-tuning!

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Conclusions

• The internet is changing .
• Bionet is a dynamic, adaptable, evolvable and
  scalable alternative to failing centralized
  methods.
• Price Propagation promising for ensuring proper
  agent populations and distributions.

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Acknowledgements

• Dr. Tatsuya Suda
• Dr. Tadashi Nakano

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Links

• The Bio-Networking architecture-
  http//netresearch.ics.uci.edu/bionet/
• My email address- vishakh_at_uci.edu
• More information about my research-
  http//www.ics.uci.edu/vishakh/research/bionet
  (Coming Soon!)