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ClouDiA : A Deployment Advisor for Public Clouds

ClouDiA : A Deployment Advisor for Public Clouds. Tao Zou , Ronan Le Bras, Marcos Vaz Salles*, Alan Demers , Johannes Gehrke. Cornell University * University of Copenhagen (DIKU). Instance Allocation in Public Clouds. Cloud Provider’s View . Cloud Tenant’s View .

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ClouDiA : A Deployment Advisor for Public Clouds

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  1. ClouDiA: A Deployment Advisor for Public Clouds Tao Zou, Ronan Le Bras, Marcos VazSalles*, Alan Demers, Johannes Gehrke Cornell University *University of Copenhagen (DIKU)

  2. Instance Allocation in Public Clouds Cloud Provider’s View Cloud Tenant’s View

  3. Instance Allocation in Public Clouds Core Cloud Provider’s View Aggregation …… Sub-Aggregation TOR TOR TOR ……

  4. Network Latencies in Public Clouds • Mean Latency Heterogeneity in EC2 • Mean Latency Stability in EC2 Challenge: How to avoid long links? Challenge: Can this be done out-of-the-box? (i.e. no changes to the cloud or the application)

  5. Communication Graphs Examples of Latency Sensitive Applications Scientific Simulation Key-value Store (time-to-solution) (response time) Opportunity: Communication graphs are not complete. A careful logical to physical mapping can help! Longest Link Search Aggregation Service Pipelines (response time) (response time) Opportunity: Gamble with over-allocation. Longest Path

  6. Architecture of ClouDiA ClouDiA Public Cloud Tenant Allocate Instances (+ Extra Instances) Communication Graph Get Measurements Objectives Search Mapping Start Application Deployment Plan Terminate Extra Instances

  7. Measuring Network Distance • Goal: obtain a reliable estimate of link costs • Approximations that are easy to obtain • Hop counts • Length of Common IP Prefix • Accurate distance: pair-wise network latencies • Large number of measurements for each pair • To observe enough latency jitters • Interferences at end points • Heavily application and network dependent • Can’t model exactly  measure without interference do not work

  8. Measuring Network Latencies • Without interference • Most efficient method: “staged” Stage i Stage i+1 • No concurrent send/recv at end points • Parallelism with minimal coordination

  9. Architecture of ClouDiA ClouDiA Public Cloud Tenant Allocate Instances (+ Extra Instances) Communication Graph Get Measurements Objectives Search Mapping Start Application Deployment Plan Terminate Extra Instances

  10. Search Mapping (Longest Link) • NP-Hard • To find a solution of cost • To find a solution of cost • To find a solution of cost • Goal: find a “good” solution within timeout • Two formulations: • Mixed-Integer Programming • O() boolean variables • Constraint Programming (*) • O() integer variables • an objective cost has to be given a priori Hard to approximate

  11. Searching using Constraint Programming • A lot of distinct latency values • Bi-section search?  finding “no solution” takes time • k-means clustering?  works well with proper k Give an objective c: 1. Remove all links with cost > c 2. Find a subgraph isomorphism Give an objective c: 1. Remove all links with cost > c 2. Find a subgraph isomorphism k=4 cost 0 Timeout Timeout

  12. Experimental Settings • 100 to 150 m1.large instances in EC2 • IBM ILOG CPLEX Optimizer/CP Optimizer • Multi-core, a single machine • Three workloads: • Behavioral simulation • Synthetic aggregation query • Key-value store

  13. Effect of Over-Allocation • 100 instances + 10%-50% over-allocation • Get Measurements + Search Deployment < 10 minutes

  14. Overall Improvement • 10% over-allocation

  15. Thank you! Conclusion • ClouDiA is a deployment advisor for public clouds • Out-of-the-box • 15%-55% time reduction for latency sensitive applications • Could be adopted by cloud services providers • With API changes

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