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Consumer Decision Support for Resource Procurement in IaaS Markets

Consumer Decision Support for Resource Procurement in IaaS Markets. Kurt Vanmechelen , Ruben van den Bossche Research Group Computational Programming. Cloud Futures 2010 Workshop, 8 th April. Search trends…. EC2 activity growth.

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Consumer Decision Support for Resource Procurement in IaaS Markets

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  1. Consumer Decision Support for Resource Procurement in IaaS Markets Kurt Vanmechelen, Ruben van den Bossche Research Group Computational Programming Cloud Futures 2010 Workshop, 8th April

  2. Search trends…

  3. EC2 activity growth Approximated number ofEC2 Instances launched per day (Observations by RightScale)

  4. Defining a cloud… © annegretrichter

  5. Service models

  6. IT infrastructure as a utility • On-demand access to resources Elasticity • Pay-per-use D. Parkhill, The challenge of the Computer Utility. Addison Wesley Educational Publishers, 1966N. Carr, The Big Switch, W.W. Norton & Co., 2008

  7. Etymology

  8. Etymology

  9. The Cloud?

  10. Provider differentiation

  11. Drivers for commoditization • Consumers fear vendor-lock in • CAPEX -> OPEX • What OPEX? • Provider capabilities • Standardization • VM Image format (OVF) • OGF’s Open Cloud Computing Interface (OCCI) • … • Tooling for interoperability / integration • Condor • Service Domain Manager (SGE) • Wolfram Matlab • …

  12. Product diversification

  13. Product diversification

  14. EC2 on-demand instances 1 One ECU delivers performance of a 2007 Opteron @ 1.0-1.2 GHz

  15. Performance Dejun et al. (2009) EC2 Performance Analysis for Resource Provisioning of Service-Oriented Applications, 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing.

  16. Performance S. Ostermann et al. (2009) A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Proceedings of CloudComp 2009, LNICST 34, pp. 115-131.

  17. Drivers for non-uniform pricing • Locality-dependence of DC costs • Real estate • Power • Bandwidth • Taxation A. Qureshi et al. (2009) Cutting the Electric Bill for Internet-Scale Systems, ACM SIGCOMM, pp. 123-134 .

  18. Drivers for non-uniform pricing • Temporal and locality dependence • Demand • Supply • Provider cost differentiation • Network • Storage • Compute • Compute resources are non-storable commodities • Cost of underutilization • Transfer of risk through reservations • Reduction of risk through volume discounts and oversubscription

  19. EC2 reserved instances

  20. EC2 Spot Market http://cloudexchange.org/

  21. Decision support Allocation problem Valuation problem

  22. Scope • Not whether to move to the cloud • Data confidentiality, security, reliability, legal issues, … • Lease or buy decision (economics of private DC) [Walker2009] • Organizational impact • … • …but how • Current focus on computational workloads E. Walker (2009) The real cost of a CPU hour, Computer Vol. 42(4), pp. 35-41.

  23. Allocation problem • Given • Application set A = {A1, …, An} • Workload Wi = {CPUreq, Storagereq, I/Oreq, DAG} • QoS requirements Qi = {Q1, …, Qm} • Provider product offering set P = {P1, …, Pk} • Devise an allocation over P that adheres to Q at minimum cost • Performance modeling • Impact of VM properties • Degree and level of parallelism • Benchmarking studies (Ostermann, Dejun, Iosup, …) • Workload modeling (Smith, Iverson, Hellinckx, …) • Automated bidding / portfolio management • Complexity • Consumers have access to internal resources (hybrid cloud) • Minor technical issues (e.g. hourly vs real-time usage metering)

  24. Automated bidding • Modeling of spot price dynamics • Fundamental / Stochastic (technical) • Bid timing and bid values • ZI, ZIP, Gjerstadt-Dickhaut, AA, … • Spot price uncertainty driver for derivatives • Comprehensive body of work for financial and electricity markets [Burger2004] • Parallels to electricity market • Non-storable goods • (Unanticipated) events can cause market shocks • Delivery delay and network congestion • Local spot markets hosted by RTOs (balancing markets) through UPA • Impact of Smart Grids on demand elasticity • Unclear what spot dynamics will emerge • EC2 spot market is fully Amazon controlled • Dynamics might be flattened out by EC2 pricing policies J. K. Mason et al. (2006) Automated Markets and Trading Agents, Handbook of Computational Economics, Chapter 28, pp. 1381-1431M.Burger et al. (2004) A spot market model for pricing derivatives in electricity markets, Quantitative Finance Vol.4(1), pp. 109-122

  25. Market-based control • Body of work for distributed systems • Efficiency through value-centric resource allocation • Incentivizes well-considered resource usage • Decentralized decision making • …in pursuit of sustainability and openessof grid systems • CDA, Proportional share, English/Dutch/First-Price/Vickrey auctions, Single-unit / multi-unit / combinatorial, … • …never made it to production on a large scale • Virtual currency • Mechanism complexity • Computational tractability • Value elicitation I. E. Sutherland (1968) A futures market in computer time, Communications of the ACM, Vol.11(6), pp.449–451.S. Clearwater (1995) Market-based control: A paradigm for distributed resource allocation, World Scientific.N. Dube (2008) SuperComputing Futures: The Next Sharing Paradigm for HPC Resources, PhD, Laval.K. Vanmechelen (2009) Economic Grid Resource Management using Spot and Futures Markets, PhD, University of AntwerpA. Byde: A comparison between mechanisms for sequential compute resource auctions, Proceedings of AMAAS 2006, pp. 1199-1201

  26. Valuation problem • Clouds turn users into choosers [Yanosky2008] • Freedom of choice induces complexity, overhead, inefficiencies • Local infrastructure no longer constrains user’s options • System architecture • Delivered quality of service • Dealing with the notion of cost / utility • Expression of value • Oftentimes taken as a given in the economic literature • Hard in practice but positive results exist [Lee2006] R. Yanosky (2008) From Users to Choosers: The Cloud and the Changing Shape of the Enterprise, In The Tower and The Cloud.C. B. Lee and A. Snavely (2006) On the User-Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions, International Journal of High Performance Computing Applications, Vol. 20(4).

  27. Valuation problem • What is my value for running Ai on Pl ? • How does the cost of Ai evolve when changing Wi or Qi? • Unforeseen immediacy costs! • Non-linear effects • Portfolio of reserved instances • Spot market prices • Utilization of private cloud • User-oriented metrics • Absolute costs (historical data, statistics) • $/minute reduction of makespan • $/unit of accuracy increase within deadline Use 24 servers spread over 3 racks for 1 hour Use 1 server for 24 hours

  28. Component overview

  29. Simple problem instance • Applications with batch workloads • Trivially parallel DAG • No I/O performance model, storage requirements • Tasks • Preemptible but cannot switch to different instance type / provider • Runtime associated with each valid instance type • Single QoS property (hard deadline) • Inbound / outbound network traffic per task • Resource supply • On-demand, posted price model • Instance types • # CPUs (normalized) • Available memory • Allocation granularity of one hour

  30. Binary Integer Program

  31. Evaluation • Software • AMPL for model definition • CPLEX solver 12.1 • Ubuntu 9.10 • Hardware • Intel quad core @ 2.83GHz / 6MB L2 / 8 GB RAM • Public cloud setup • 50 apps / 3 providers / S, L, XL • 20 samples / point

  32. Outcome (public)

  33. CPLEX Performance (hybrid) • Addition of zero-cost private cloud with 512 CPUs • MIPGAP of 1% • CPU time

  34. Greedy heuristic • Order instance types on cost • Schedule tasks with a CDT policy • Solves within a second • Efficiency (40 apps, 20 samples)

  35. Market structure Providers Clients

  36. Market structure Providers Clients Broker

  37. Market structure Providers Clients Brokers Brokers Market

  38. MS also takes interest…

  39. Education

  40. Questions? kurt.vanmechelen@ua.ac.be

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