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Corona Linearization Analysis

by Dianne Foreback Advanced Operating Systems Kent State University November 2013. Corona Linearization Analysis. Linearization Algorithm Model. Peer-to-peer overlay network of N processes Each peer has a unique ID non-FIFO message passing system

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Corona Linearization Analysis

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  1. by Dianne Foreback Advanced Operating Systems Kent State University November 2013 Corona Linearization Analysis

  2. Linearization Algorithm Model • Peer-to-peer overlay network of N processes • Each peer has a unique ID • non-FIFO message passing system • copy-store-forward (stores id of right & left neighbor) • all IDs are known • Weakly connected channel connectivity graph (CC) and message based links • channel process graph (CP)--locally stored neighboring ids • CC/CP--message links • Goal to Linearize the system • Consequent processes • cnsq(a, b), if (∀c : c ∈ N : (c < a) ∨ (b < c))

  3. Corona Linearization Algorithm Example [1] Example taken directly from reference.

  4. Linearization Algorithm (2 actions) linearize—remove message from channel and process timeout—reintroduce p to left and right (omits sending to infinities)

  5. Experimental Model I (random strongly conn components) • 100 randomly placed nodes • Varying graph diameters ranging from 10 to 100 in increments of 10 • Timeout action and Linear action not equally executed CC \ CP a' t’ m’ k' e’ s’ CP a t m k e s

  6. Results I (random strongly conn components) • Analysis • As diameter increases, processing of linear messages decreases (“speed” of linearization increases) . Same a Results I. • As diameter increases, less timeout actions exec (due to more messages in channel). Differs from Results II. Measurement: # of actions

  7. Experimental Model II (linear strongly conn components) • 100 Nodes • Varying Graph Diameters ranging from 10 to 100 in increments of 10 • Timeout execution CC \ CP a' b’ c’ d' e’ f’ CP a b c d e f

  8. Results II (linear strongly conn components) • Analysis • As diameter increases, processing of linear messages decreases (“speed” of linearization increases) . Same a Results I. • As diameter increases, more timeout actions exec (due to fewer messages in channel)

  9. Challenges • Randomly Generate Strongly Connected Components • runtime too long with timeout having equal probability as linear action CC \ CP a' m’ t’ c' e’ s’ CP a m t c e s • Strongly connected components do not have evenly distributed nodes • Place remaining nodes in one component—no • Distribute remaining nodes • Number of runs • 10 (results inconclusive) • 100 (better results) • 1000 (best results)

  10. Future Work • Timeout Action—vary the probability of executing the timeout action • Randomize number of processes in each strongly connected component (make • Vary number of nodes

  11. References [1] Rizal Mohd Nor, Mikhail Nesterenko, and Christian Scheideler. Corona: A stabilizing deterministic message-passing skip list. In 13th. International Symposium on Stabilization, Safety and security of Distributed Systems (SSS) pages 356-370, October 2011c.

  12. Thank You

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