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

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

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

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

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

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

 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.

Dynamic Resource Allocation

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

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

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 ¯

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

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

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.

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

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

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

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

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

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