An alternate multiplicity 2 task assignment scheme for distributed computations
Download
1 / 17

An Alternate Multiplicity-2 Task Assignment Scheme for Distributed Computations - PowerPoint PPT Presentation


  • 65 Views
  • Uploaded on

An Alternate Multiplicity-2 Task Assignment Scheme for Distributed Computations. D. Szajda, J. Owen , B. Lawson, A. Charlesworth University of Richmond Richmond, VA USA. Outline. Volunteer Distributed Computing Simple Redundancy Alternatives - Vertical Partitioning - Clustering

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' An Alternate Multiplicity-2 Task Assignment Scheme for Distributed Computations' - willoughby-roland


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
An alternate multiplicity 2 task assignment scheme for distributed computations

An Alternate Multiplicity-2 Task Assignment Scheme for Distributed Computations

D. Szajda, J. Owen, B. Lawson, A. Charlesworth

University of Richmond

Richmond, VA

USA


Outline
Outline Distributed Computations

  • Volunteer Distributed Computing

  • Simple Redundancy

  • Alternatives

    - Vertical Partitioning

    - Clustering

  • Analysis

  • Conclusions


I volunteer distributed computing
I. Volunteer Distributed Computing Distributed Computations

  • Organization of the distributed computation:

    - tasks

    - participants (anonymous)

    - supervisor

  • Examples:

    - [email protected], [email protected]

    - DNA sequence alignment

    - commercial applications (exhaustive regression, graphics rendering, etc.)


  • When many PCs are involved… POWER! Distributed Computations

  • Key idea: only significant results are returned to the supervisor

  • Issue addressed here:

    TRUST

    - Code executing in unknown environments

    - Significant results may be withheld

    - Cheating: credit for work not performed


Ii simple redundancy
II. Simple Redundancy Distributed Computations

  • In an N-task computation, create identical copies and assign to 2N participants

  • Doubles the work required

  • Supervisor’s MO:

     if the two returned copies do not match, this signals a problem (check manually)

     if the two returned copies do match, it is usually assumed the work is correct


Biggest weakness collusion
Biggest Weakness: Collusion Distributed Computations

  • Many participants (and thus tasks) could be under control of a single individual

    - Results may be corrupted (either intentionally or unintentionally)

    - Significant results may be withheld


Example exhaustive regression
Example: Exhaustive Regression Distributed Computations

  • One dependent/response variable (Y), five independent/predictor variables (X1, …, X5)

    Goal: find the “best” linear regression equation using any/all of the predictor variables

    Y = 0 + iXi + jXj + …

    25 – 1 = 31 possible regression equations


Participant Assignments Distributed Computations

With 2N = 8 participants, we can divide the computation into N = 4 tasks: A, B, C, D

Tasks

collusion potential??


Iii alternative 1 vertical partitioning
III. Alternative #1: Vertical Partitioning Distributed Computations

  • Idea: spread the job of verifying the work of one participant to all other participants.

Tasks

Participant Assignments

Subtasks


Advantages: Distributed Computations

Verifying a single task’s worth of work checks all N participants

Exploits the finer task granularity and has improved control of result verification

Identification of colluding parties / adversaries

Drawbacks:

Ability to subtask required

Adversary with two tasks will always control a subtask

Task assignment database management (prohibitive for large N)


Practical vertical partitioning clustering
Practical Vertical Partitioning: Clustering Distributed Computations

  • Idea: break the computation into several clusters and apply the vertical partitioning strategy within each cluster

    Example: 4 tasks with 3 subtasks each, C = 2 clusters

Participant Assignments


Advantages to the clustering method
Advantages to the Clustering method: Distributed Computations

  • An adversary who controls multiple participants is not guaranteed matching subtasks

  • Decreased task tracking overhead

  • Flexibility: designs can range from the two extreme cases of simple redundancy to vertical partitioning


Iv analysis
IV. Analysis Distributed Computations

Suppose an adversary controls a certain proportion p of the 2N participants.

For the three procedures, consider:

  • expected number of tasks/subtasks under her control?

  • variance?

  • probability of detecting the adversary if only a single task is verified?


Punch lines
Punch lines Distributed Computations

  • Expected # of subtasks compromised:

    pN(2pN – 1) (for all schemes)

  • Variance of subtasks compromised:

    SR:

    VP: 0

  • Probabilities are difficult but can be expressed


60 tasks with c 1 vp 3 4 6 60 sr
60 tasks, with Distributed ComputationsC = 1 (VP), 3, 4, 6, 60 (SR)

VP +

C = 3 

C = 4 

C = 6 o

SR 


Conclusions
Conclusions Distributed Computations

  • Presented a novel, tunable approach for applying redundancy in distributed computations

  • No additional computational burden

  • Improved detection of adversaries and collusion

  • Things get even better when ringers are employed


Questions
Questions? Distributed Computations

An Alternate Multiplicity-2 Task Assignment Scheme for Distributed Computations

Jason Owen

[email protected]


ad