ENERGY EFFICIENT CONTROL OF VIRTUAL MACHINE CONSOLIDATION UNDER UNCERTAIN INPUT PARAMETERS FOR THE CLOUD

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1 ENERGY EFFICIENT CONTROL OF VIRTUAL MACHINE CONSOLIDATION UNDER UNCERTAIN INPUT PARAMETERS FOR THE CLOUD ENRICA ZOLA, KARLSTAD ENGINEERING AND CONTROL FOR RELIABLE CLOUD SERVICES, SEPTEMBER 11, 2015, GHENT, BELGIUM.

2 ENERGY CONSUMPTION IN DATACENTERS Example: Facebook 678 m KW (509 m) 2012 (2011): 30% increase Average Datacenter energy consumption 2.2 (2.6) MW in 2012 (2013) Source: N. America Campos Survey Results", Digital Reality, 2013

3 RESOURCE MANAGEMENT TAXONOMY Resource Management Static Dynamic Cloud Brokering where Where/when What to do now Mapping Scheduling Loadbalancing VM Placement Service Placement Server Loadbalancing VM/Server Consolidation Capacity Planning Future actions Workflow Scheduling Adjusted from: M. Guzek, P. Bouvry, and E.-G. Talbi, Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing," IEEE Computational Intelligence Magazine, 2015 vol.10 no.2 pp

4 RESOURCE MANAGEMENT TAXONOMY Resource Management Static Dynamic Cloud Brokering where Where/when What to do now Mapping Scheduling Loadbalancing VM Placement Service Placement Server Loadbalancing VM/Server Consolidation Capacity Planning Future actions Workflow Scheduling Adjusted from: M. Guzek, P. Bouvry, and E.-G. Talbi, Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing," IEEE Computational Intelligence Magazine, 2015 vol.10 no.2 pp

5 VM CONSOLIDATION: BACKGROUND Determine, (when??) a physical server is Overloaded Migrating away a set of potential VMs to maintain QoS based on SLA Underloaded Migrating away ALL VMs and shut down server to minimize energy consumption Select (which??) The set of potential VMs that are subject to migration Find (How??) Where the candidate VMs should be migrated to

6 VM CONSOLIDATION: MOTIVATION VM Workload Varies over time due to unpredictable workload May require VM resizing, VM creation, VM termination Result in the physical servers to be Underutilized Overutilized Consequences for Cloud Operators SLA Violations versus Minimum Energy Consumption Case Study Evaluated Workload of 6 VMs in KAU Compute Service Department

7 VM CONSOLIDATION: KAU WORKLOAD TRACES VM Demand Varies over time EXAMPLE: KAU Datacenter workload traces

8 VM CONSOLIDATION: KAU WORKLOAD TRACES VM Demand Varies within bounds

9 VM CONSOLIDATION: EXAMPLE Before Server Consolidation VM1 VM2 VM3 VM4 60% 35% 20% 50% Can save 50% Energy in this example Load Monitoring Migration Controller VM1 S1 VM2 S2 VM3 S3 VM4 S4 VM1 VM3 VM2 VM4 80% 85% Power on Power off After Server Consolidation VM1 S1 VM2 S2 VM3 S1 VM4 S2

10 CLASSICAL OPTIMIZATION FRAMEWORKS Almost all models for Cloud Optimisation (e.g. VM Consolidation) assume perfect knowledge! MIN c T (x) s.t. Ax<=d Once x* calculated, it is used BUT: Many factors not known precisely, e.g. VM Resource Demands Energy Model of Servers We can only assume incomplete knowledge in A, d, c Consequence (Ben Tal+Nemirovski, 2000): Small errors in parameters can make x* highly unfeasible

11 ROBUST OPTIMIZATION PARADIGM Assume uncertainty model for data is known (e.g. bounds) Define a solution is robust feasible as one that is guaranteed to remain feasible for all admissible data values (out of uncertainty set) Optimize objective over set of robustly feasible solutions Robust counterpart may be much harder to solve than original problem Need for approximations Nominal boundary objective approximate x* nominal becomes infeasible robust

12 ROBUST VM CONSOLIDATION MODEL Model VM Consolidation as a ROBUST Mixed Integer Linear Problem Robustness Input to the Model such as Physical Server Power Model and VM Resource Demands Cardinality Constrained Uncertainty Set (Gamma Robustness, see Bertsimas) Consider Probability of Constraint Violation Objective is to Minimise Power Consumption Balance Migration Cost

13 UNCERTAINTY ON SERVER POWER MODEL Power of server can be modeled as linear function of resource utilization (e.g. CPU load, etc) But errors up to 10-14% due to processor optimizations, etc Power consumption is random variable from uncertainty set symmetrically distributed between with zero mean Decision variable Constraints depend on VM utilization, see next slide

14 UNCERTAINTY ON VM RESOURCE DEMANDS Power consumption depends on resource demands of VMs, which are uncertain Resource demand is random variable symmetrically distributed with zero mean plus fixed demand Utilization Budget constraint Resource demands of Old assignment VMs migrating towards server VMs migrating away Overprovisioning Factor

15 OTHER CONSTRAINTS Make sure that Migrations make sense: VM k can only migrate away from server j if already deployed there originally VM k can only migrate towards server j, if not deployed there originally, etc

16 UNCERTAINTY MODEL PRICE OF ROBUSTNESS Uncertainty set Defines deviations from nominal values, i.e. mean values plus deviation bounds Protection from deviation by introducing hard constraints that cut-off feasible solutions that may become unfeasible ones for some deviations Cardinality constraint uncertainty model Defines upper bound on number of coefficients that deviate to worst value Price of robustness Cloud Operator can tradeoff by modifying G Optimal value of robust solution typically worse than original problem Higher risk aversion consider more unlikely deviations higher protection higher energy consumption Opportunistic solution less protection less energy consumption

17 HOW MUCH RISK TO TAKE? TUNING OF G Probability of constraint violation w coefficients may deviate Upper bound can be computed according to (Bertsimas, Sim) For small w need to ensure full protection (setting G to max) to ensure small violation probability

18 EVALUATION Implementation in Matlab with IBM CPLEX Not suitable for online optimization Benchmark for heuristics Small example to demonstrate model capabilities 0.1 CPU = 1 core 0.1 RAM = 512 MB Initial allocation: S1: VM 6, 7, 8, 9 S2: VM 1, 5, 10 S3: VM 2, 4 S4: VM 3 S5: Shutoff

19 VARIATION OF GAMMA FOR UNC. POWER CONSERVATIVE SOLUTION = TOTAL PROTECTION LEVEL (MAX G) = HIGHEST ENERGY ALL PROTECTED UNCERTAINTY ASSUMED TO BE AT MAX. HIGHER ENERGY = PRICE OF ROBUSTNESS PROTECTION AGAINST UNCERTAINTY OF 2 UNITS NO PROTECTION = NOMINAL PROBLEM

20 CPU DEMANDS UNCERTAIN

21 CPU DEMANDS AND POWER UNCERTAIN (5%)

22 CONSTRAINT VIOLATION PROBABILITY (CPU) Probability of Constraint Violation for 10 uncertain variables We vary G to calculate different migration strategies to protect from this uncertainty G > 8 for constraint violation probability < 1%

23 CONCLUSIONS AND FUTURE WORK Conclusions VM Consolidation problem for energy conservation Applied Robust Optimization Framework to cope with unknown and imprecise input data Uncertainty on VM resource demands and Power model of servers G uncertainty and constraint violation probability gives Cloud operators a tool to tradeoff robustness versus energy efficiency Future work Large scale evaluation ongoing Comparison with heuristics Integration of network model and NFV concept (service chain)

24 THANK YOU FOR YOUR ATTENTION! Thank you for your attention!

25 VM MIGRATION: TOOL FOR SERVER CONSOLIDATION Different Approaches Precopy Postcopy Hybrid Inflict Network Stress Additional Serverload Example: Precopy