Andrew Krioukov

NapSAC: design and implementation of a power-proportional web cluster

Energy consumption is a major and costly problem in data centers. A large fraction of this energy goes to powering idle machines that are not doing any useful work. We identify two causes of this inefficiency: low server utilization and a lack of power-proportionality. To address this problem we present a design for an power-proportional cluster consisting of a power-aware cluster manager and a set of heterogeneous machines. Our design makes use of currently available energy-efficient hardware, mechanisms for transitioning in and out of low-power sleep states, and dynamic provisioning and scheduling to continually adjust to workload and minimize power consumption. With our design we are able to reduce energy consumption while maintaining acceptable response times for a web service workload based on Wikipedia. With our dynamic provisioning algorithms we demonstrate via simulation a 63% savings in power usage in a typically provisioned datacenter where all machines are left on and awake at all times. Our results show that we are able to achieve close to 90% of the savings a theoretically optimal provisioning scheme would achieve. We have also built a prototype cluster which runs Wikipedia to demonstrate the use of our design in a real environment.

Clustera: an integrated computation and data management system

This paper introduces Clustera, an integrated computation and data management system. In contrast to traditional cluster-management systems that target specific types of workloads, Clustera is designed for extensibility, enabling the system to be easily extended to handle a wide variety of job types ranging from computationally-intensive, long-running jobs with minimal I/O requirements to complex SQL queries over massive relational tables. Another unique feature of Clustera is the way in which the system architecture exploits modern software building blocks including application servers and relational database systems in order to realize important performance, scalability, portability and usability benefits. Finally, experimental evaluation suggests that Clustera has good scale-up properties for SQL processing, that Clustera delivers performance comparable to Hadoop for MapReduce processing and that Clustera can support higher job throughput rates than previously published results for the Condor and CondorJ2 batch computing systems.

A parallel IEEE P754 decimal floating-point multiplier

Decimal floating-point multiplication is important in many commercial applications including banking, tax calculation, currency conversion, and other financial areas. This paper presents a fully parallel decimal floating-point multiplier compliant with the recent draft of the IEEE P754 Standard for Floating-point Arithmetic (IEEE P754). The novelty of the design is that it is the first parallel decimal floating-point multiplier offering low latency and high throughput. This design is based on a previously published parallel fixed-point decimal multiplier which uses alternate decimal digit encodings to reduce area and delay. The fixed-point design is extended to support floating-point multiplication by adding several components including exponent generation, rounding, shifting, and exception handling. Area and delay estimates are presented that show a significant latency and throughput improvement with a substantial increase in area as compared to the only published IEEE P754 compliant sequential floating-point multiplier. To the best of our knowledge, this is the first publication to present a fully parallel decimal floating-point multiplier that complies with IEEE P754.

Identifying models of HVAC systems using semiparametric regression

Heating, ventilation, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we have built on the Berkeley campus for modeling and efficient control of HVAC systems, and we use these testbeds as case studies for system identification. The main contribution of this work is that the use of semiparametric regression allows for the estimation of the heating load from occupancy, equipment, and solar heating using only temperature measurements. These estimates are important for building accurate models as well as designing efficient control schemes, and in our other work we have been able to achieve a reduction in energy consumption on a single room testbed using heating load estimation in conjunction with the learning-based model predictive control (LBMPC) technique. Furthermore, this framework is not restrictive to modeling nonlinear HVAC behavior, because we have been able to use this methodology to create hybrid system models that incorporate such nonlinearities.

Building application stack (BAS)

Many commercial buildings have digital controls and extensive sensor networks that can be used to develop novel applications for saving energy, detecting faults, improving comfort, etc. However, buildings are custom designed, leading to differences in functionality, connectivity, controls and operation. As a result todays building applications are hard to write and non-portable. What is required is a form of mass customization that allows applications to automatically adapt to differences in buildings. We present BAS, an application programming interface and runtime for portable building applications. BAS provides a fuzzy query interface allowing application authors to describe the building components they require in terms of functional and spatial relationships. The resulting queries implicitly handle multiple building designs. BAS also incorporates a hierarchical driver model, exposing common functions of building components through standard interfaces. We demonstrate and evaluate BAS by implementing two novel applications -- an occupant HVAC control app and a ventilation optimization app -- on two different buildings using raw building control protocols and then again using BAS. We show that the BAS code is much shorter, easier to understand and does not change for each building.

Energy-Efficient Building HVAC Control Using Hybrid System LBMPC

Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.

Enabling advanced environmental conditioning with a building application stack

There is enormous potential for building-focused applications to improve operation and sustainability, both for classical uses like modeling or fault detection as well as innovative ones like occupant-driven control or grid-aware energy management. We show that a building application stack - that addresses shortcomings of existing antiquated architectures by democratizing sensor data, constructing a framework for reliable and fault-tolerant operation of concurrent applications, and establishing an application programming interface to promote portability throughout the building stock - enables development of advanced applications. We observe the growing importance of applications that integrate sensors and actuators from the building infrastructure with those from “add-on” networks, and show how this design pattern is further empowered by the architecture. To prove the efficacy of the approach, we implement two advanced environmental conditioning applications on a large, commercial building that was not designed for either of them: a demand-controlled ventilation (DCV) system for balancing air quality considerations and energy use in conference and class room settings and a demand-controlled filtration (DCF) system for conserving recirculating fan energy in an intermittently occupied cleanroom setting. The DCV application is able to reduce air quality threshold violations by over 95% and concurrently reduce ventilation energy consumption by over 80%, while the DCF application can reduce recirculating fan power consumption by half with no repercussions on air quality when the room is occupied. Further, the portability of these applications highlights the potential of the architecture to enable widespread and rapid application development throughout the building stock.

Personal building controls

Buildings are some of the largest energy consumers in the world and yet occupants are regularly dissatisfied with the interior environment in large part due to thermal discomfort [7]. Studies show that given personal control over their environment, occupants are comfortable in a much larger range of ambient temperatures [2]. We present a personalized control smartphone application designed with the dual goals of increasing occupant comfort and achieving building-wide energy savings. The application allows occupants to directly control the lighting and heating/cooling in their vicinity. Using wireless localization combined with data from existing sensors in the building, we estimate room occupancy and use this to dynamically adjust ventilation and air conditioning to save energy in the building.

Zone-level occupancy counting with existing infrastructure

Through accurate and dynamic occupancy detection, building actuation systems can fine tune the targets of their actions to better fit the patterns of usage in modern buildings. We outline a method for achieving this through existing wireless infrastructure and present a demonstration of its viability in a corporate environment.

Demo abstract: Personal building controls

Buildings are some of the largest energy consumers in the world and yet occupants are regularly dissatisfied with the interior environment in large part due to thermal discomfort [7]. Studies show that given personal control over their environment, occupants are comfortable in a much larger range of ambient temperatures [2]. We present a personalized control smartphone application designed with the dual goals of increasing occupant comfort and achieving building-wide energy savings. The application allows occupants to directly control the lighting and heating/cooling in their vicinity. Using wireless localization combined with data from existing sensors in the building, we estimate room occupancy and use this to dynamically adjust ventilation and air conditioning to save energy in the building.