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Amir Epstein Joint work with David Breitgand

Improving Consolidation of Virtual Machines with Risk-aware Bandwidth Oversubscription in Compute Clouds. Amir Epstein Joint work with David Breitgand. Motivation. Network Bandwidth is a critical Data Center resource

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Amir Epstein Joint work with David Breitgand

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  1. Improving Consolidation of Virtual Machines with Risk-aware Bandwidth Oversubscription in Compute Clouds Amir EpsteinJoint work withDavid Breitgand

  2. Motivation • Network Bandwidth is a critical Data Center resource • Network Bandwidth may become a bottleneck for consolidation • Accurate and efficient network bandwidth demand estimation is difficult • Common practice: fully provision for peak loads • Consequences: resource waste

  3. Full Provisioning VS. Multiplexing ∑i maxt di(t) >> maxt ∑i di(t) • The aggregate demand of VMs may be much smaller than the sum of the maximum demand of each VM: Max(VM1)+Max(VM2)=110

  4. Full Provisioning VS. Multiplexing Max(VM1+VM2)=71 < Max(VM1)+Max(VM2)=110

  5. Statistical Multiplexing • Consider each VM dynamic bandwidth demands as a random variable • Consider the aggregate bandwidth demand which is a sum of the random variables representing VMs Bandwidth demands • As the number of VMs increases: • The ratio between standard deviation of the aggregate bandwidth demand and the mean decreases

  6. Overcommit • Cloud provider aims at improving cost-efficiency • Overcommit resources using statistical multiplexing • Our focus is bandwidth

  7. Stochastic Bin Packing Problem (SBP) • S={X1,…, Xn} – Set of items • Xi – random variable representing the size (bandwidth demand)of item i • p – overflow probability • Goal: Partition the set S into the smallest number of subsets (bins) S1,…,Sk such that p represents a probabilistic SLA / policy

  8. SBP with Normal Distribution • We assume that each item i independently follows normal distribution N(μi ,σi2) . • When σi,=0, for all i, then Xi=μi and the problem reduces to the classical bin packing problem • The focus of this work is SBP with normal variables

  9. Related Work – Bin Packing • The problem is NP-hard • Bin packing is hard to approximate to a factor better than 3/2 unless P=NP. • First Fit Decreasing (FFD) has asymptotic approximation ratio of 11/9 and (absolute) approximation ratio of 3/2. • MFFD algorithm has asymptotic approximation ratio of 71/60. • AFPTASexists. • Online bin packing • First Fit (FF) has competitive ratio of 17/10. • Best upper and lower bounds are 1.58899 and 154014, respectively.

  10. Related Work – Stochastic Bin Packing • -approximation for SBP with Bernoulli variables [Kleinberg et. al 1997] • SBP with Poisson, Exponential and Bernoulli variables [Goel and Indik 1999] • PTAS exists for Poisson and exponential distributions. • Quasi-PTAS exists for Bernoulli variables. • These results relax bin capacity and overflow probability constraints by a factor 1+ε. • - competitive algorithm for SBP with normal variables [Wang et. al 2011]

  11. Our Results • 2-approximation algorithm for SBP with normal variables • (2+ε)-competitive algorithm for online SBP with normal variables • Observe the existence of a dual PTAS for SBP with normal variables.

  12. Definitions • Definition: The effective load of bin j is where and the quantile function is the inverse function of the CDF Фof N(0,1). • Observation: A packing is feasible for a given overflow probability p iff for every bin j, The load of bin j is normally distributed with mean and variance

  13. Simple solution approach • Reduce the problem to the classical bin packing problem with item sizes , thus • A feasible solution to the classical bin packing problem is a feasible solution SBP, since • The optimum for the classical bin packing instance with the new sizes may be significantly larger than the optimum for SBP.

  14. Effective Size • Thus, the effective size of item i on bin jcan be viewed as

  15. Approximation Algorithm Algorithm 1: First Fit VMR decreasing • Order the items in non-increasing order of VMR • Place the next item in the first bin into which it can be feasibly packed • If no such bin exists, open a new bin to pack this item Variance to Mean Ratio (VMR) is

  16. Approximation Algorithm Theorem 1: Algorithm 1 is a 2-approximation algorithm for SBP with normal variables.

  17. Integer Program for SBP

  18. Mathematical Program Relaxation

  19. Fractional Algorithm (Algorithm 2) • Order the items in non-increasing order of VMR • Place the next item in the bin with remaining capacity. If the item causes an overflow to the bin, assign maximum fraction of this item to the bin. Then, open a new bin to pack the remaining part of this item. Variance to Mean Ratio (VMR) is

  20. Analysis Lemma: There exists a feasible solution to the MP with the following property. For any pair of items k,l and a pair of bins i<j, if xkj>0 and xli>0, then dl ≥ dk. Observation: Fractional algorithmproduces a feasible fractional solution to the MP. • This implies that collocating items with high VMR (bursy) minimizes the total effective size of the items Variance to Mean Ratio (VMR) is

  21. Proof Outline • Consider a feasible solution to the MP with lexicographically maximal standard deviation (STD) vector of the bins S=(S1,…,Sm), where • Assume by contradiction that the items are not packed into the bins according to non-increasing order of VMR • Thus, there exists at least one pair of items that are not placed in this order (i.e., item with smaller VMR is packed to a bin with smaller index than the other item). • We show that we can exchange fractions of these items between the bins, such that • the new solution is feasible • The STD vector of the bins in the solution is lexicographically greater than the one in the original solution • Contradiction

  22. Online Algorithm • VMR • Let • Class 0: • Class 1≤k≤C: • Class C+1:

  23. Online Algorithm Algorithm 3: • Classify next item according to the VMR classes • Place the next item in the first bin of its class into which it can be feasibly packed • If no such bin exists, open a new bin to pack this item Theorem 2: Algorithm 3 is a (2+O(ε))-approximation algorithm for SBP with normal variables.

  24. Simulation Study • Compare our proposed algorithms to previous reported ones • Data set • Real trace from production data center used to compute mean and standard deviation of bandwidth consumption of 6000 VMs over a few hours period. • Synthetic traces with statistical properties similar to those of the real traces

  25. Algorithms • Algorithms 1-3 • First Fit (FF) with deterministic item sizes μi+βσi • First Fit Decreasing (FFD) with deterministic item sizes μi+βσi • Group Packing (GP) [Wang et. al 2011] For the online algorithms (Algorithm 3 and Group Packing), we set ε=0.1.

  26. Real Instance (Online) (Approx.) (L.B)

  27. Real Instance (Online) (Approx.) (L.B)

  28. Real Instance (Online) (Approx.) (L.B)

  29. Online Algorithms • Large synthetic instances 9% 8%

  30. Summary • We studied SBP under the assumption that virtual machines bandwidth demand obeys normal distribution • We showed a 2-approximation algorithm • We showed (2+ε)-competitive algorithm • We observed the existence of a dual PTAS for SBP • We studied the performance and applicability of our algorithms using synthetic and real data • The performance evaluation showed that our proposed algorithms considerably reduce the number of bins compared to the best known algorithms for the problem

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