A Multi-objective Approach to Virtual Machine Management in Datacenters

Transcription

1 A Multi-objective Approach to Virtual Machine Management in Datacenters Jing Xu and José Fortes Advanced Computing and Information Systems Laboratory (ACIS) Center for Autonomic Computing (CAC) University of Florida Center for Autonomic Computing Advanced Computing and Information Systems Lab

2 Modern Data Centers Growing management complexity Large-scale: keep growing to 10s or 100s of thousands servers Dynamic workloads: over-provisioning to meet peak demand Power and cooling costs: high density creates hotspots 2

3 Virtualized Data Centers Virtualization to the rescue? Consolidation: fewer physical servers with high utilization Adjustable resource allocation Migration: workloads can be easily moved around Unfortunately More management demands More (virtual) machines (VMs) to be managed More decisions to be made How to map/remap VMs to physical servers? How to allocate resources among VMs? 3

4 Virtual Machine Placement Initial placement (previous work*) Place a number of VMs at once on available physical resources Occurs much less frequently than dynamic placement Sophisticated algorithms to search globally Dynamic placement (focus of this paper) Reassign VMs to hosts due to datacenter dynamics Extensive placement changes are impractical due to high overhead Local online search fast solutions *Jing Xu and José A. B. Fortes, Multi-objective Virtual Machine Placement in Virtualized Data Center Environments, Proceedings of 2010 IEEE/ACM International Conference on Green Computing and Communications (GreenCom2010). 4

5 Goal Optimize power, cooling costs and resource usage by managing Virtual Machine (VM) placement Challenges Overview Multiple possibly conflicting optimization objectives Naïve placement can hurt application performance, waste power, or create hotspots Stabilization needed to avoid oscillations Key aspects of proposed solution Two-level control management Local searching algorithm for fast adaption Utility-based approach to combine multiple objectives 5

6 Two-level Control Local controller Determines resource needs and sends requests to global controller Fuzzy-logic based modeling approach (ICAC 2007) Global controller Determines VM placement and resource allocation New VM requests VM placement and migration Global Controller Power model Temperature model Local Controller... Local Controller System state feedback Profiling and modeling VM Virtualization Data Center VM Monitor/ sensor Resource usage Power consumption Temperature 6

7 Cross-layer Management Cross-Layer Profiling, Modeling and Controlling Virtualization layer: resource usage of VMs Platform layer: resource usage, power, and temperature Functionality the Controller Condition detection: when to move VMs VM selection: which VMs to move Host selection: where to move VMs Controller Profiling Host Selection VM Selection Condition Detection Modeling Actuator Virtualization Layer Sensors Actuator Physical Resource Layer Sensors

8 Control Flow for Dynamic VM Migration Thermal emergency: reduce temperature by moving VMs Resource contention: move VMs to maintain performance Low energy efficiency: shut down idle host by moving all VMs Monitor thread Retrieve data from all sensors Hotspots Yes Action thread Start a new thread Identify movable VMs Resource contention Yes Determine destination host(s) Low power efficiency Yes Migrate chosen VMs if hosts are found Terminate the thread

9 Decision Making When to move: two-level detection 1 st level: Sliding-window detection Avoid false detection over transient changes 2 nd level: Trend analysis Avoid unnecessary migration on the basis of trend Which VM to move from a physical host Thermal emergency: VMs with higher CPU utilization and smaller memory size to quickly reduce temperature with less overhead Resource contention: VMs with high resource utilization (CPU or IO) and also small memory size Low energy efficiency: all VMs need to be moved

10 Destination Host Selection Multiple considerations lead to multiple utilities Temperature efficiency: monotonically decreases with temperature; faster near or above safe threshold Power efficiency: workload divided by consumed power, monotonically increases with CPU utilization Performance efficiency: monotonically decreases with increasing resource utilization Combine them via sum of utility functions Each utility functions is normalized into [0~1] range Select host that returns highest combined utility value Confirmed by predicted future values

11 IO utility Power utility Temperature utility Example Utility Functions Cooler is better No contention is faster High utilization is power-efficient Performance includes IO (shown) CPU and network (not shown) IO utilization Temperature CPU utilization

12 Utility functions 12

13 Stabilization System stability System oscillation: VM migrations trigger more migrations Avoid unnecessary migrations due to transient changes Sliding window size (W) selection To ensure profitable migration: stable interval > migration time W for thermal emergency and resource contention is 2 mins W for low power efficiency is 4 mins Two-level thresholds Different thresholds for the two-level detection Catch ascending trend faster, avoid triggering descending one Stable hosts selection Incorporate standard deviation and predicted values in selection 13

14 Evaluation: Testbed Testbed IBM BladeCenter blades each with two dual Xeon 2.33GHz, 8GB RAM for hosting VMs and 73GB SAS disk for VM images Nine blades for hosting 54 VMs, and one for global controller Monitoring Blade power: IBM s advanced management module CPU Temperature: lm-sensors Resource usage: /proc and /sys system files Modeling Power: linear function of CPU utilization CPU temperature: linear function of CPU utilization

15 Evaluation: Profiling and Modeling Testbed IBM BladeCenter HS21 blades each with two dual Xeon 2.33GHz, 4MB L2 cache, 8GB RAM, and 73GB SAS disk Modeling Power: linear function of CPU utilization CPU temperature: linear function of CPU utilization 15

16 Comparison Evaluation: Setup single-objective optimization coolest (SOC): select the host with the lowest temperature fullest (SOF): select the host with the fullest load idlest (SOI): select the host with the lowest load multi-objective optimization without stabilization (MONS) Single threshold detection and single data for host selection multi-objective optimization with stabilization (MOS) Benchmarks Lookbusy: generate loads on CPU (constant or fluctuate), memory and disk Sysbench: three test modes CPU, fileio and oltp Linpack: measure CPU s floating-point rate of execution Workload generator: random loads+ loads that cause anomalies

17 CPU Temperature Disk IO CPU% Evaluation: Real-time Monitoring Data Four event: transient CPU contention, stable IO contention, transient IO contention, a stable CPU contention (also a hotspot) CPU% Stable CPU load Transient CPU load Time(s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host-9 CPU% st VM migration 2nd VM migration Time (s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host-9 Disk IO (%) Transient IO load Stable IO load Time (s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host-9 Disk IO (%) rd VM migration Time (s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host-9 55 Temperature ( C) Hotspot Time (s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host Temperature ( C) Under the safe range Time (s) Host-1 Host-2 Host-3 Host-4 Host-5 Host-6 Host-7 Host-8 Host-9 No control 17 MOS control

18 Real-time Data CPU Utilization (%) 18

19 Real-time Data Disk IO (%) 19

20 Real-time Data Temperature ( F) 20

21 Evaluation: Overall Performance MOS reduce migrations SOF has highest thermal violation and performance violation SOC and SOI has higher performance violation

22 Evaluation: Timing and Overhead Events Time (seconds) CPU (%) overhead Disk (%) overhead Net (MB) overhead Selection of VM and destination < 1 <1 0 0 VM suspend 3~6 10~ VM file copy 20~30 (256M) 40~50 (512M) Source: 20~30 Destination : 20~30 Source: 0 Destination: 60~100 Source: 110~120 Destination: 120~150 VM start 1~2 10~

23 Related Work Workload placement Maximize total number of application placed on shared resources Minimize total number of used servers Thermal and power management Minimize power costs: turn off inactive servers, dynamic voltage/frequency scaling (DVFS) Minimize cooling costs: temperature-aware workload placement Dynamic placement Adapt to dynamic workloads Minimize migration costs 23

24 Workload Placement B. Urgaonkar, A. Rosenberg, & P. Shenoy Online and offline application placement, to maximize the number of applications hosted in a shared platform while satisfying their resource requirements First-fit, best-fit and worst-fit approximation algorithms are used M. Cardosa, M. Korupolu, & A. Singh Power-efficient VM allocation, to maximize overall utility gained from VMs minus the power costs Several heuristics using greedy strategy are evaluated A. Verma, G. Dasgupta, T. Nayak, P. De, & R. Kothari Studied CPU usage correlation between applications over long term to determine VM placement in order to save power and reduce performance violations due to VM consolidation. 24

25 Dynamic placement G. Khanna, K. Beaty, G. Kar, & A. Kochut Dynamic VM placements is triggered by the violation of a resource utilization threshold. The goal is to maximize the gains associated with migration minus the cost of migration Online heuristic using greedy strategy G. Jung, M. Hiltunen, K. Joshi, R. Schlichting, & C. Pu When a VM workload deviates from a pre-specified workload band, it triggers dynamic VM migration The goal is to optimize total utility including application utility, power costs, and transient adaption costs. A modified bin-packing algorithm and then constructing a search graph from the current configuration with best-first search technique T. Wood, P. Shenoy, A. Venkataramani, & M. Yousif VM Migration is initiated to avoid violation of application SLAs or resource utilization thresholds. The algorithm attempts to move workloads from the most overloaded servers to the least-loaded ones, while minimizing data transferred during migration. 25

26 Thermal and Power Management Chen, Y., A. Das, W. Quin, A. Sivasubramaniam, Q. Wang, N. Gautam, 2005 Dynamic server provisioning and power management in a dedicated data center. Determine how many servers and choose operating frequency so that the energy cost is minimized while meeting SLAs Q. Tang, S. Gupta & G. Varsamopoulos Study thermal-aware task placement in a dedicated data center to reduce overall energy cost. A genetic algorithm and a sequential quadratic programming method are used to solve the optimization problem Y. Chen, D. Gmach, C. Hyser, Z. Wang, C. Bash, C. Hoover, & S. Singhal Integrate IT management and cooling infrastructure management to improve overall efficiency of data center 26

27 Comparison with related work 27

28 Related Work Y. Chen, D. Gmach, C. Hyser, Z. Wang, C. Bash, C. Hoover, & S. Singhal Integrate IT management and cooling infrastructure management to improve overall efficiency of data center Common with our work Consider thermal effect when migrating VMs Difference from our work Optimization objective Yu s: balance between migrations, consolidation, and cooling efficiency Our: balance between power consumption, thermal distribution, and resource contention Optimization solution Yu s: candidate solutions are generated and evaluated using genetic algorithms Our: heuristic-based online algorithm for fast adaption 28

29 Related Work (cont.) G. Jung, K. Joshi, M. Hiltunen, R. Schlichting, C. Pu Cost-sensitive VM adaptations (VM replication, migration, and capacity controls) using a combination of predictive models and graph search techniques. Common with our work Consider VM migration benefit and cost when determining control actions Difference from our work Optimization objective Jung s: only consider effect on application performance Our: balance between power consumption, thermal distribution, and resource contention Optimization solution Jung s: best-first search algorithm Our: heuristic based online algorithm for fast adaption 29

30 Related Work (cont.) D. Gmach, J. Rolia, L. Cherkasova, G. Belrose, T. Turicchi, & A. Kemper Integrate of workload placement and workload migration controllers to support resource pool management. Common with our work Dynamic migrate VMs to adapt to workload changes and shut down underloaded server to save energy Difference from our work Host selection Gmach s: the least loaded server that has sufficient resources Our: the host with the best balance between power consumption, thermal distribution, and resource contention 30

31 Related Work (cont.) A. Verma, G. Kumar, R. Koller Study the duration, resource consumption and the performance impact of VM migration actions Common with our work VM migration requires a considerable amount CPU resources on source host Live migration is not a good candidate to handle hotspot Difference from our work Verma s: focus on studying the cost of VM migration itself Ours: study how to determine when, which and where to migrate VMs considering multiple objectives 31

32 Summary Two-level control of virtualized data centers helps reduce management complexity. Multi-objective optimization seeks balance among conflicting objectives. Initial placement + dynamic adjustments Incorporate information from different layers into all the decisions on VM migrations. When, which and where to Avoid unstable hosts Utility-based evaluation combines multiple objectives. Effective approach confirmed experimentally Up to 80% reduction in unnecessary migrations Up to 30% application performance improvements Up to 20% improved power efficiency 32