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Dynamic Resource Allocation for Shared Data Centers with Workload Prediction Models

This paper explores a dynamic resource allocation framework for shared data centers, utilizing workload prediction models to optimize resource distribution among applications. The objective is to meet Quality of Service (QoS) guarantees while maximizing resource utilization and maintaining robustness against unexpected traffic spikes, illustrated with real-world scenarios such as the 1998 Soccer World Cup. We propose a measurement-based model that predicts future workloads and allows for periodic resource reallocation, addressing challenges like low overhead and the need for rapid responses in changing conditions.

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Dynamic Resource Allocation for Shared Data Centers with Workload Prediction Models

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  1. Share Allocation APPLICATION MODELS PREDICTOR Workload Prediction MONITOR ALLOCATOR System Metrics Resource Shares RESOURCE subject to Model Discontent Di Adaptation Window Goal History Response Time Workload Prediction Workloads Time (min) Dynamic Resource Allocation for Shared Data Centers using Online Measurements Abhishek Chandra, Weibo Gong, Prashant Shenoy UMASS Amherst http://lass.cs.umass.edu • 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 • Dynamic Resource Allocation • Estimate workloads for near future • Periodically re-allocate resources among applications Challenges: • Reallocation at short time-scales • No prior workload profiling/knowledge • Low overhead Expected Load Measured Usage Rsrc Reqmts Allocation Utility Model Application Model Measurement-based Model Prediction Time Series Analysis Workload as time series Discontent function for QoS goal violations Predicted Workload Parameters QoS Reqmts Rsrc Reqmts Allocation Non-linear optimization Application Model Transient Queuing Model Prediction AR(1) Model aj : number of arrivals in jth interval R : Autocorrelation function ej: White noise Relation between mean response time and share Simulation Results: Using Soccer World Cup’98 Web Traces System Discontent

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