Workload Partitioning in Cloud Marketplaces

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Best practices to make efficient use of your public and private clouds thereby proving cost effective to the company. Presentation given by Aaron Yan, Ilyas Iyoob & Ton Dieker at the 2013 Informs Annual Meeting.

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Workload Partitioning in Cloud Marketplaces

  1. 1. Workload Partitioning in Cloud Marketplaces Ilyas Iyoob, PhD Gravitant, Inc. Ton Dieker, PhD Georgia Institute of Technology Aaron Yan, M.S. Gravitant, Inc. Partitioning workloads between private and public clouds to minimize cost s 1
  2. 2. • Introduction ▫ IT Demand ▫ Conservative Cloud Approach ▫ Liberal Cloud Approach ▫ Advanced Analytics Approach • Workload Partitioning ▫ Mathematical Formulation ▫ Cost-Optimal Solution ▫ Financial Benefits • Conclusion ▫ Summary Overview s 2
  3. 3. IT Demand • Quantifying IT demand ▫ Number of servers to run your business ▫ Chart shows actual data until August 2013 followed by forecast thereafter 0 50 100 150 200 250 300 350 400 450 Aug-'12 Aug-'13 Aug-'14 Aug-'15 Aug-'16 Aug-'17 Aug-'18 Aug-'19 Aug-'20 Aug-'21 Aug-'22 NumberofServers s 3
  4. 4. 0 50 100 150 200 250 300 350 400 450 Aug-'12 Aug-'13 Aug-'14 Aug-'15 Aug-'16 Aug-'17 Aug-'18 Aug-'19 Aug-'20 Aug-'21 Aug-'22 NumberofServers reserved Conservative Cloud Approach Unutilized Resources • Procure all servers through “Reservation” ▫ Pay for the servers at the beginning of the year (lower price per VM) ▫ 1 year lock-in period for each server ▫ Over-allocate servers to cover peak demand in the future s 4
  5. 5. 0 50 100 150 200 250 300 350 400 450 Aug-'12 Aug-'13 Aug-'14 Aug-'15 Aug-'16 Aug-'17 Aug-'18 Aug-'19 Aug-'20 Aug-'21 Aug-'22 NumberofServers 54% Liberal Cloud Approach On- demand • Procure all servers “On-Demand” ▫ Pay-as-you-go pricing (higher price per VM) ▫ No lock-in period ▫ No over-allocation s 5
  6. 6. 0 50 100 150 200 250 300 350 400 450 Aug-'12 Aug-'13 Aug-'14 Aug-'15 Aug-'16 Aug-'17 Aug-'18 Aug-'19 Aug-'20 Aug-'21 Aug-'22 NumberofServers Advanced Analytics Approach Cloud Option Price Lock Period Price/VM/mo On-Demand $172.8/mo 1 Month $172.8 Reserved $556/yr 12 Months $46.3 Private (128-block chassis) $660,000 120+ Months $43.0 ▫ Determine how to best partition workload across three cloud options ▫ Utilize cloud option trade-offs (short lock period vs. lower price) On- demand reservedprivate s 6
  7. 7. Mathematical Formulation • Model • Decision variables and Parameters Decision Variable Unit Lock Period 𝐵𝑡: On-Demand VM 1 Month 𝑅𝑡: Reserved VM 12 Months 𝑃: Private (128-block chassis) Chassis 120+ Months Parameters Description 𝑑 𝑡 Demand for servers in month t 𝑐 𝑃 Cost of purchasing a private cloud chassis 𝑐 𝑅 Cost of reserving a VM (hold for one year) 𝑐 𝐵 Cost of procuring one VM on-demand (hold for one month) min 𝑃,𝑅,𝐵 𝑐 𝑃 𝑃 + 𝑐 𝑅 𝑡∈𝑇 𝑅𝑡 + 𝑐 𝐵 𝑡∈𝑇 𝐵𝑡 s. t. 128𝑃 + 𝑡′=max(𝑡−11,0 𝑡 𝑅 𝑡′ + 𝐵𝑡 ≥ 𝑑 𝑡 ∀ 𝑡 ∈ 𝑇 𝑃, 𝑅𝑡, 𝐵𝑡 ∈ 0,1,2 … ∀ 𝑡 ∈ 𝑇 s 7
  8. 8. Cost-Optimal Solution s 8 0 50 100 150 200 250 300 350 400 0 12 24 36 48 60 72 84 96 108 120 NumberofServers Months Private Reserved On-Demand IT Demand 1% On- demand 49% reserved 50% private
  9. 9. Financial Benefits Liberal Approach Partially-Conservative Approach 100%0%0% 50%0%50% 1%49%50% $5,250,000 $3,300,000 $1,415,000 Savings ~$3,800,000 Savings ~$1,885,000 s 9 Optimal Solution
  10. 10. • Dramatic financial benefits ▫ Implemented work for current customers – very satisfied ▫ Our solutions are much better than traditional approaches • Key drivers for workload partitioning ▫ Demand variability ▫ Cost of on-demand cloud Summary s 10
  11. 11. Thank you. 11 For more information, please contact analytics-support@gravitant.com

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