Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup.

Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING

The thesis "COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING" was completed through a collaborative partnership between Michigan Technological University and Argonne National Laboratory under a contractual agreement titled "Advanced Vehicle Characterization at Argonne National Laboratory". The goal of this project was to investigate, understand and document the performance and operational strategy of several modern passenger vehicles of various architectures. The vehicles were chosen to represent several popular engine and transmission architectures and were instrumented to allow for data collection to facilitate comparative analysis. In order to ensure repeatability and reliability during testing, each vehicle was tested over a series of identical drive cycles in a controlled environment utilizing a vehicle chassis dynamometer. Where possible, instrumentation was preserved between vehicles to ensure robust data collection. The efficiency and fuel economy performance of the vehicles was studied. In addition, the powertrain utilization strategies, significant energy loss sources, tailpipe emissions, combustion characteristics, and cold start behavior were also explored in detail. It was concluded that each vehicle realizes different strengths and suffers from different limitations in the course of their attempts to maximize efficiency and fuel economy. In addition, it was observed that each vehicle regardless of architecture exhibits significant energy losses and difficulties in cold start operation that can be further improved with advancing technology. It is clear that advanced engine technologies and driveline technologies are complimentary aspects of vehicle design that must be utilized together for best efficiency improvements. Finally, it was concluded that advanced technology vehicles do not come without associated cost; the complexity of the powertrains and lifecycle costs must be considered to understand the full impact of advanced vehicle technology.