Energy-Efficient Computing and Computing for Efficient Energy Usage

1. Energy-Efficient Computing and Computing for Efficient Energy Usage Yanlei Diao and Prashant Shenoy Department of Computer Science University of Massachusetts, Amherst Yanlei Diao, University of Massachusetts Amherst

2. Energy-Efficient Computing Goal: make computation energy efficient Tools: Hardware, software techniques Two Aspects of Sustainable Computing • Computing for Efficient Energy Usage • Goal: green physical infrastructures and the natural environment • Tools: hardware, software, algorithms, optimizations Yanlei Diao, University of Massachusetts Amherst

3. Monitoring Energy Usage in Buildings • Buildings consume 75% of the electricity in the US! • Sensor-driven energy monitoring a first step to greening of buildings • Sensors in office and residential buildings • Occupancy sensors: detect human presence and track movement • Outlet-level sensors: detect usage at individual outlets (e.g., AC plugs, tap) • Meter-level sensors: detect total usage of buildings (gas, water, electricity) • Our first deployment: instructed an 1700 sq. ft home • 35 outlets and all 33 wall switches monitored every 2 seconds • Meter-level sensor tracking usage every second for 6+ months • Second deployment: deployed 60 outlet sensors in the CS building Yanlei Diao, University of Massachusetts Amherst

4. Sensor Deployment and Appliance Signatures Example: Refrigerator over a 24-hour period Yanlei Diao, University of Massachusetts Amherst

5. Large-Scale Deployments in the Future • Large-scale deployments in the future: • All rooms/zones in a building • All buildings in a district • All buildings in a city A real-time view of energy consumption in a building/district/city. Help develop strategies and trigger actions for more efficient usage. Yanlei Diao, University of Massachusetts Amherst

6. Data Analysis for Efficient Energy Usage • Consumer-view: real-time fine-grain usage streams • Utility view: smart meter streams from 100,000’s of homes • Energy bill capping / peak usage reduction • Use real-time usage data to cap/reduce total and peak consumption • Energy conservation • Identify zones with no active usage, turn off lights/HVAC systems • Correlate occupancy and usage sensor streams to detect these conditions • Anomaly detection: detect unusual usage patterns and alert users • Scaling issues • How to manage 100,000 or million streams from smart meters? • Large-scale, real-time data collection and data analysis is key • Infrastructure for data collection and dissemination • Real-time detection of patterns/trends; integrating usage with billing, identifying real-time incentives; comparing real-time usage with history • Parallel stream processing… Yanlei Diao, University of Massachusetts Amherst

7. Energy-Efficient Data Storage Systems • Eenergy-efficient hardware: Flash memory, SSD’s • Software solutions for data management • Storage-centric sensor networks: • exploit energy-efficient flash storage on sensor nodes • reduce expensive communication • Large database systems: • use flash-based storage for both data and indexes • deal with expensive random writes in index design Yanlei Diao, University of Massachusetts Amherst

8. Energy-Efficient DBMS: New Opportunities • A DBMS optimized for energy efficiency • Single machine: Limited opportunity for software-based energy optimization [SIGMOD’10] • The highest performing configuration is the most energy-efficient. • Shared disks / disk farms: computing and storage are decoupled • SSDs make the storage system energy proportional. • How do we make computing energy proportional? Consolidation? Sharing? • How do we make it scale? • Numerous embedded devices (in the smart planet context): flash memory and CPU integrated within a microcontroller • Flash memory requires the entire chip to operate at a higher voltage. • Lower voltage causes errors in flash writes. • Correct errors using software solutions? Yanlei Diao, University of Massachusetts Amherst