Energy Optimization in Sustainable Buildings
Buildings are responsible for around 40% of total energy consumed in our country. Taking advantage of renewable energy sources to protect the environment and reduce global warming has gained high global interest. Inspired by hybrid cars, we call such buildings that are run by hybrid sources of energy, i.e. renewable sources as well as fossil fuels, "hybrid buildings." Solar energy is extensively available in Iran and can be fairly easily converted to electricity by solar panels, but these panels are expensive and taking best advantage of them such that the financial gain is maximized is a major concern. One way to improve financial advantage of solar electricity in buildings is to store the solar electricity during off-peak hours and cleverly pass it to appliances at peak hours which are usually at evenings. But given the limited capacity of batteries normally used, if the stored energy is not fully used during peak hours, it is actually wasted since the day after new solar energy is produced as well. In this work, we developed a distributed control system that cleverly switched each appliance between the electricity obtained from the grid and from the solar panels such that total financial gain of using solar energy is maximized. The system uses AI techniques to predict future peak-time consumers so as to decide whether pass the stored energy to current consumers or keep it for a future better use.
Energy-Aware Resource Allocation in Cloud Computing
Cloud computing is an emerging technology which promises reduced upfornt costs as well as elsticity for highly varying workloads encountered many times in today IT services over the internet. At the back end of this technology, an important enabling and underlying technology is Hypervisor that enables a single physical machine to simultaneously run several virtual machines (VM) each providing its own service to the users without being bound to the physical machine. This allows the VM to be freely, and even lively, moved across physical machines without the user even noticing it. This capability has enabled new opportunities to improve energy efficiency by smart distribution and management of physical resources among VMs in the cloud. In this work, we are investigating network-aware, storage-aware, memory-aware, placement-aware, and cooling-aware allocation, binding, and scheduling of physical resources among the VMs in the cloud to reduce total energy consumption.
Acceleration by FPGA, Co-Design and GPU
There are numerous applictions and tasks, spanning image processing, video processing, network processing, network security, scientific computing, and many more, that need acceleration to achieve higher throughput and/or reduced response time. Moreover, there are many modern platforms that enable new methods for acceleration. Very high capacity FPGAs are available at commodity costs and can contain very large custom computing engines. Field-Programmable System-on-Chip (FPSoC) devices allow closely-coupled hardware-software implementation without needing the expensive manufacturing that was traditionally unavoidable. Dynamic (run-time) reconfiguration capability of modern FPGA and FPSoC devices has also enabled one more category of acceleration techniques. Availability of FPGA boards with PCI-Express connectivity has allowed them to communicate with high end processors on desktop/server motherboards at high speeds such that new and much larger scales of hardware-software co-design are now possible. GPU cards with hundreds to thousands of small but fast cores, available at commodity prices are enablers of yet another category of methods to accelerate computation-intensive tasks and applications.