Daniel Gmach

Recently, the demand for data center computing has surged, increasing the total energy footprint of data centers worldwide. Data centers typically comprise three subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes heat generated by these subsystems. This work presents a novel approach to model the energy flows in a data center and optimize its operation. Traditionally, supply-side constraints such as energy or cooling availability were treated independently from IT workload management. This work reduces electricity cost and environmental impact using a holistic approach that integrates renewable supply, dynamic pricing, and cooling supply including chiller and outside air cooling, with IT workload planning to improve the overall sustainability of data center operations. Specifically, we first predict renewable energy as well as IT demand. Then we use these predictions to generate an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce both the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing techniques, while still meeting the Service Level Agreements.

Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future services in such pools. This paper considers issues of workload analysis, performance modeling, and capacity planning. Our goal is to automate the efficient use of resource pools when hosting large numbers of enterprise services. We use a trace based approach for capacity management that relies on i) the characterization of workload demand patterns, ii) the generation of synthetic workloads that predict future demands based on the patterns, and m) a workload placement recommendation service. The accuracy of capacity planning predictions depends on our ability to characterize workload demand patterns, to recognize trends for expected changes in future demands, and to reflect business forecasts for otherwise unexpected changes in future demands. A workload analysis demonstrates the busrtiness and repetitive nature of enterprise workloads. Workloads are automatically classified according to their periodic behavior. The similarity among repeated occurrences of patterns is evaluated. Synthetic workloads are generated from the patterns in a manner that maintains the periodic nature, burstiness, and trending behavior of the workloads. A case study involving six months of data for 139 enterprise applications is used to apply and evaluate the enterprise workload analysis and related capacity planning methods. The results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95% of the time. The accuracy of predictions for required capacity suggests that such resource savings can be achieved with little risk.

Recently, the demand for data center computing has surged, increasing the total energy footprint of data centers worldwide. Data centers typically comprise three subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes heat generated by these subsystems. This work presents a novel approach to model the energy flows in a data center and optimize its operation. Traditionally, supply-side constraints such as energy or cooling availability were treated independently from IT workload management. This work reduces electricity cost and environmental impact using a holistic approach that integrates renewable supply, dynamic pricing, and cooling supply including chiller and outside air cooling, with IT workload planning to improve the overall sustainability of data center operations. Specifically, we first predict renewable energy as well as IT demand. Then we use these predictions to generate an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce both the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing techniques, while still meeting the Service Level Agreements.

Recent advances in hardware and software virtualization offer unprecedented management capabilities for the mapping of virtual resources to physical resources. It is highly desirable to further create a "service hosting abstraction" that allows application owners to focus on service level objectives (SLOs) for their applications. This calls for a resource management solution that achieves the SLOs for many applications in response to changing data center conditions and hides the complexity from both application owners and data center operators. In this paper, we describe an automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales. Simulation and experimental results confirm that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.