The Biologically Inspired Distributed
File System: An Emergent Thinker
Instantiation
Presented by
Dr. Ying Lu
CAS (Complex Adaptive Systems)
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Systems that have
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a large number of members with simple functions
limited communications among them
CAS property
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be able to adapt quickly to changing
environmental conditions
Emergent Computation
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An emergent computation model
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agents follow simple rules to affect their states
and/or environment to produce a system wide
result, an emergent computation
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all computations (e.g. aggregation, resource
allocation, classification, assignment, path selection,
decision, etc.) can be obtained by emergent
computations of simple activities
Regular vs. Emergent Computation
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A regular computation
–
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a CPU computation like arithmetic and logic
operations
An emergent computation
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make use of regular computation or other emergent
computations to achieve its outcomes
Challenge
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How to identify the relationship between the emergent
computation and the local agents’ properties or actions?
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Necessary and sufficient conditions to obtain certain
emergent computations?
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How a specific property produces an emergent
outcome?
Long-term Goal
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Control and manage the agent’s properties to
obtain desired global outcomes
Determining Factors for
Global Outcome
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Local properties & actions
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Propagation models:
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different propagation approaches lead to different
global, emergent self-* properties
CAS propagation model concerns the spread and
its affects on the amplification of the agents’
actions to eventually give an emergent result
Emergent Thinker Paradigm
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The CAS emergent computation model is
used as the building block for the paradigm
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Emergent function services (self-* property )
are provided to application by the CAS
Biologically Inspired P2P Distributed
File System (BPD)
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
An instantiation of the Emergent Thinker
paradigm
BPD
–
–
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an alternative to deterministic techniques proposed
in P2P and DFS
modeling natural behavior in its foundation services
to solve distributed systems’ design challenge
environment: computing devices with ad hoc
behavior (i.e. joining and leaving network); no
central server or controller
Overview of BPD
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A P2P system with hundreds or thousands of computing
devices (peers)
Each peer execute basic, independent actions with
minimum or no communication among them
Emergent computations achieved by the actions provide
computing services required by the DFS
A user or application accesses File System services for
its file management needs through calls to the DFS
emergent computation engine that resides in each peer
Necessary Services for DFS
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Allocation
Retrieval
Replication
Discovery
For each BPD DFS service provided, there
is an independent spatially decentralized
domain of agent actions that execute on
the same physical P2P system
CAS Algorithms
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For DFS allocation services
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For DFS discovery services
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Based on squirrel hoarding mechanism, both
are achieved as emergent function services
Allocation Service in BPD
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Squirrels hoard acorn in dispersed caches,
where they are allocating resources (land
space) to storage demands (acorns) in such
a way that resources are balanced
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Allocate data acorns evenly among nodes in
BPD
Discovery Services in LDS
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Large Distributed System (LDS) property:
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dynamic, self-organization, and ad-hoc
connectivity and operation of its decentralized
members
only constant: variability of the member’s
connectivity to LDS (on/off/fail/disconnected)
Adaptable, scalable search
Discovery Services in LDS (cont)
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Previous search algorithms:
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structured search: too rigid
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blind search (poor resource utilization)
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informed search (such as PlanetP, requiring tables
or indices maintenance, not suitable for LDS with
extremely variable member population)
Emergent Search in Large Distributed
Systems
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Emergent search
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based on CAS, it is local to foster peer independence
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emergent outcomes result from the member activities
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compared to blind search, it minimizes messages by grouping
several searches (acorn identifiers) within one message
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CAS algorithm provides a system-wide, scalable search with
reliability
Squirrel Emergent Search
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Each location has
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its own independent squirrels
shared resources, e.g. shared files (data acorns)
Squirrel Emergent Search (cont)
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A new search arrives at a location, the
location’s squirrel puts the acorn id in a bag
together with other acorn ids already existing
and hoards them in nearby locations
Squirrel Emergent Search (cont)
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If a bag with acorn ids is placed in a location,
the acorn ids are searched within this
location
Questions?