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Dynamic Resource Allocation for Shared Data Centers Using Online Measurements By- Abhishek Chandra, Weibo Gong and Prashant Shenoy
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Overview Outline Motivation System Model Dynamic Allocation Techniques Experimental Results Conclusions
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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
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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
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Research Contribution Generalized processor sharing (GPS) Time domain queuing model & Non-linear optimization technique Prediction algorithm Synthetic Workloads & Real Web Traces
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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
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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.
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Dynamic Resource Allocation
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Monitoring Measure system and application metrics Queue lengths Request response times Monitoring windows Adaptation Window History Measurement Interval Time
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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
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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 ¯
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Optimization-based Resource Allocation Discontent function Non-linear Optimization Problem: Solved using Lagrange multiplier method
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Prediction Short-term prediction of workload characteristics Request arrival process Service demand distribution Use history of measured system metrics
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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.
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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
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Experiments Evaluation Synthetic Web Workload Comparison of static and dynamic resource allocations for a synthetic web workload
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Trace-driven Web Workloads Comparison of static and dynamic resource allocations in the presence of heavy-tailed request sizes and varying arrival rates.
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Adaptation to Transient Overloads The workload and the resulting allocations in the presence of varying arrival rates and varying request sizes
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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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Thank You
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