# Pull versus Push Mechanism in Large Distributed Networks: Closed Form Results

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1 Pull versus Push Mechanism in Large Distributed Networks: Closed Form Results Wouter Minnebo, Benny Van Houdt Dept. Mathematics and Computer Science University of Antwerp - iminds Antwerp, Belgium Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 1/15

2 Outline Problem description Pull / Push / Hybrid strategies Finite system model Continuous time Markov chain Infinite system model Ordinary differential equations Closed form results Finite system simulations Conclusion Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 2/15

3 Problem Description Set of M / M /1 queues N queues (single server with infinite waiting room) Each has its own arrival stream of jobs: Poisson (λ) Processing time is exponential (µ = 1) Distribute work to reduce response time Some servers may be empty while others have jobs waiting inefficient Distribute waiting jobs (load sharing) Pull strategies (work stealing): Lightly-loaded servers attempt to attract work Push strategies: Heavily-loaded servers attempt to forward work Hybrid strategies: Combine Pull and Push strategy Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 3/15

4 Load sharing strategies Communication via probe messages Random queues are probed according to an Interrupted Poisson process (r) If the target accepts, a task is transfered to it Transfer and probe time is considered zero, i.e., instantaneous Strategies Pull: Idle servers generate probes (r), busy servers accept Push: Servers with pending jobs generate probes (r), idle servers accept Hybrid: Idle and servers with pending jobs generate probes (r = r 1 + r 2 ), servers accept accordingly Performance What is the required probe rate of a strategy to achieve a specified mean delay? Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 4/15

5 Load sharing strategies Relation to existing work Other works frequently use a maximum of Lp probes (in batch or one-by-one) at task arrival / completion instead of rate-based probing Parameter r allows to match any predefined R It is fair to compare strategies if they have a similar (preferably equal) overall probe rate R Remarks Rate-based probing performs better, given the same overall probe rate R, if the Lp probes are sent in batch Both methods are equivalent if overall probe rate R is matched, given that the Lp probes are sent one-by-one until success or the maximum is reached Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 5/15

6 Finite system model - Continuous time Markov chain System state N queues, independent arrivals and completions Define X (N) (t) = (X N 1 (t), X N 2 (t),...) t 0 X (N) i (t) {0,..., N} : Number of nodes with at least i jobs in queue at time t Note that for any state x = (x 1, x 2,...), x i x i+1 for all i 1 State transitions q (N) (x, y) is the transition rate between state x = (x 1, x 2,...) and y = (y 1, y 2,...) Events incurring a state change: Arrival Completion Succesful job transfer Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 6/15

7 Finite system model - Continuous time Markov chain State transitions Arrival : y = x + e i q (N) (x, y) = λ(x i 1 x i ) Completion : y = x e i q (N) (x, y) = (x i x i+1 ) Succesful task transfer : y = x + e 1 e i q (N) (x, y) = r(n x 1)(x i x i+1 ) N Load sharing strategies These transitions describe both pull, push and hybrid strategies, although semantics differ Probe rate target is idle initiator has exactly i jobs Push Probe rate initiator is idle target has exactly i jobs Pull Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 7/15

8 From Finite to Infinite Transition rates Define β l (x/n) = q (N) (x, x + l)/n, such that β ei (x/n) = λ(x i 1 /N x i /N), for i 1 β ei (x/n) = (x i /N x i+1 /N), for i 1 β e1 e i (x/n) = r(1 x 1 /N)(x i /N x i+1 /N) for i 2 Define F (x) = i 1 (e iβ ei (x) e i β ei (x)) + i 2 (e 1 e i )β e1 e i (x) Then the ODEs d dt x(t) = F (x(t)) describe the evolution of the infinite system model Density dependent Markov chain [Kurtz] of infinite dimensionality Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 8/15

9 Infinite system model - Ordinary differential equations Description Let x i (t) be the fraction of nodes with at least i jobs at time t The set d dt x(t) = F (x(t)) can be written as Arrivals d dt x 1(t) = ( {}}{ { }} { λ + rx 2 (t)) (1 x 1 (t)) } {{ } (x 1(t) x 2 (t)) } {{ } Incoming job transfers Completions Completions { }} { d dt x i(t)=λ(x i 1 (t) x i (t)) } {{ } ({}}{ 1 + r(1 x 1 (t))) (x i (t) x i+1 (t)) } {{ } Arrivals Outgoing job transfers Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 9/15

10 Infinite system model - Closed form results Unique fixed point Describes cumulative queue length distribution at t = π = (π 1, π 2,...) with i 1 π i <, explicitly: ( λ π i = λ 1 + (1 λ)r ) i 1 Performance The fixed point is a global attractor Proof by the Krasovskii-Lasalle principle The mean response time is then given by: D = 1 + λ (1 λ)(1 + r) Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 10/15

11 Infinite system model - Closed form results Load sharing strategies D = 1 + Valid for Push / Pull / Hybrid λ (1 λ)(1 + r) Difference lies in the generated overall probe rate R: R = (1 λ)r 1 + r 2 λ (1 λ)r Using r = r 1 + r 2 for the Hybrid strategy Setting (r 1, r 2 ) = (r, 0) and (0, r) results in R pull and R push respectively Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 11/15

12 Infinite system model - Closed form results Mean response time Hybrid strategy is always inferior Using the relationship R, rewrite: D push = λ (1 λ)(λ + R), for R < λ2 /(1 λ) D push = 1, for R λ 2 /(1 λ) D pull = 1 + R 1 λ + R (1+R) 2 +4(1+R) (1+R) Resulting in D push < D pull if λ < 2 If R 0, D push < D pull if λ < φ 1 with φ = (1 + 5)/2 Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 12/15

13 Finite system simulations Finite vs. Infinite Errors are proportional to both load (λ) and probe rate (r) Using a push strategy r decreases with λ, nearly load insensitive error Using a pull strategy r increases with λ, larger error under high load Infinite model is accurate for large N : λ = λ = 0.95 Mean Delay (D push ) λ = λ = λ = Mean Delay (D pull ) λ = λ = λ = Infinity Infinity Number of Nodes (N) Number of Nodes (N) Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 13/15

14 Finite system simulations Finite vs. Infinite Simulation results using N = 100 (crosses) vs. infinite model: Mean Delay (D) push, R=0.5 push, R=1.0 pull, R=0.5 pull, R=1.0 Mean Delay (D) push, R=1.0 pull, R= Load (λ) Load (λ) Infinite model accurately predicts λ where both strategies perform equally well, even for systems of moderate size Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 14/15

15 Conclusion Push / Pull / Hybrid strategies Push outperforms pull (for N = ) if and only if: λ < (1+R) 2 +4(1+R) (1+R) Hybrid strategy is always inferior to pure push or pull strategy (proof in paper) Finite vs. Infinite Infinite model predicts finite model accurately Technical issues to formally prove the convergence of the steady state measures of the finite system model to the infinite system model were identified (see paper for details) 2 Wouter Minnebo, Benny Van Houdt Pull vs Push strategies 15/15

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