1. Dynamic Resource Allocation for Shared Data Centers Using Online Measurements By- Abhishek Chandra, Weibo Gong and Prashant Shenoy

2. Overview Outline  Motivation  System Model  Dynamic Allocation Techniques  Experimental Results  Conclusions

3. Motivation  Data Centers  Server farms  Rent computing and storage resources to applications  Revenue for meeting QoS guarantees  Goals  Satisfy application QoS guarantees  Maximize resource utilization of platform  Robustness against “Slashdot” effects  Cluster of servers – Dedicated or Shared  Static Allocation is problematic

4. Dynamic Resource Allocation  Periodically re-allocate resources among applications  Estimate resource requirements for near future  Challenges  Reallocation at short time-scales  No prior workload profiling/knowledge  Low overhead  Approach: Online Measurement-based Allocation

5. Research Contribution  Generalized processor sharing (GPS)  Time domain queuing model & Non-linear optimization technique  Prediction algorithm  Synthetic Workloads & Real Web Traces

6. Problem Formulation  Resource Model  Queue are assumed to be served in FIFO order and the resource capacity C is shared among the queues using GPS  Queue is assigned a weight  Allocated a resource share in proportion to its weight.  GPS Scheduler

7.  Problem Definition  If denotes the target response time of application and is its observed mean response time, then the application should be allocated a share, such that.  The discontent of an application grows as its response time deviates from the target di. This discontent function can be represented as follows  System goal then is to assign a share to each application such that the total system-wide discontent, i.e., the quantity is minimized.

8. Dynamic Resource Allocation

9. Monitoring  Measure system and application metrics  Queue lengths  Request response times  Monitoring windows Adaptation Window History Measurement Interval Time

10. Allocating  Invoked periodically to dynamically partition the resource capacity among the various applications running on the shared server.  Resource Model Types  Time-domain Queuing Model  Online optimization-based Model

11. Time Domain Queuing Model  Transient queuing behavior over adaptation window  The request service rate is  Relation between mean response time T ¯ and application share. Average response time in near future:  Relation is parameterized by the measured workload  Arrival rate λ and mean service time s ¯

12. Optimization-based Resource Allocation  Discontent function  Non-linear Optimization Problem:  Solved using Lagrange multiplier method

13. Prediction  Short-term prediction of workload characteristics  Request arrival process  Service demand distribution  Use history of measured system metrics

14. Prediction Techniques  Estimating the Arrival Rate  Accurate estimate of allows the time domain queuing model to estimate the average queue length for the next adaptation window.  We represent Ai at any time by the sequence of values from the measurement history.  To predict, model using the AR(1), a sample value of Ai is estimated as  Estimating the Service Demand  Computes the probability distribution of the per-request service demands  Mean of the distribution is used to represent the service demand of application requests  Measuring the Queue Length  Monitoring module records the no. of outstanding requests at the beginning of each adaptation window.

15. Experiments  Soccer World Cup’98 Traces  Results based on a 24-hour portion of the trace  755,000 requests  Mean req rate: 8.7 req/sec  Mean req size: 8.47 KB

16. Experiments Evaluation  Synthetic Web Workload Comparison of static and dynamic resource allocations for a synthetic web workload

17.  Trace-driven Web Workloads Comparison of static and dynamic resource allocations in the presence of heavy-tailed request sizes and varying arrival rates.

18. Adaptation to Transient Overloads The workload and the resulting allocations in the presence of varying arrival rates and varying request sizes

19. Conclusions  Dynamic Resource Allocation needed for data centers  Measurement-based allocation:  Monitoring and Prediction gather online state  Use this state for application modeling and allocation  Results showed that these techniques can judiciously allocate system resources, especially under transient overload conditions

20. Thank You