Longjun Liu

HEB: deploying and managing hybrid energy buffers for improving datacenter efficiency and economy

Today, an increasing number of applications and services are being hosted by large-scale data centers. The massive and irregular load surges challenge data center power infrastructures. As a result, power mismatching between supply and demand has emerged as a crucial issue in modern data centers which are either under-provisioned or powered by intermittent power sources. Recent proposals have employed energy storage devices such as the uninterruptible power supply (UPS) systems to address this issue. However, current approaches lack the capacity of efficiently handling the irregular and unpredictable power mismatches. In this paper, we propose Hybrid Energy Buffering (HEB), the first heterogeneous and adaptive strategy that incorporates super-capacitors (SCs) into existing data centers to dynamically deal with power mismatches. Our techniques exploit diverse energy absorbing characteristics and intelligent load assignment policies to provide efficiency-and scenario- aware power mismatch management. More attractively, our management schemes make the costly energy storage devices more affordable and economical for datacenter-scale usage. We evaluate the HEB design with a real system prototype. Compared with a homogenous battery energy buffering system, HEB could improve energy efficiency by 39.7%, extend UPS lifetime by 4.7×, reduce system downtime by 41% and improve renewable energy utilization by 81.2%. Our TCO analysis shows that HEB manifests high ROI and is able to gain more than 1.9× peak shaving benefit during an 8-years period. It allows datacenters to adapt to various power supply anomalies, thereby improving operational efficiency, resiliency and economy.

Towards sustainable in-situ server systems in the big data era

Recent years have seen an explosion of data volumes from a myriad of distributed sources such as ubiquitous cameras and various sensors. The challenges of analyzing these geographically dispersed datasets are increasing due to the significant data movement overhead, time-consuming data aggregation, and escalating energy needs. Rather than constantly move a tremendous amount of raw data to remote warehouse-scale computing systems for processing, it would be beneficial to leverage in-situ server systems (InS) to pre-process data, i.e., bringing computation to where the data is located. This paper takes the first step towards designing server clusters for data processing in the field. We investigate two representative in-situ computing applications, where data is normally generated from environmentally sensitive areas or remote places that lack established utility infrastructure. These very special operating environments of in-situ servers urge us to explore standalone (i.e., off-grid) systems that offer the opportunity to benefit from local, self-generated energy sources. In this work we implement a heavily instrumented proof-of-concept prototype called InSURE: in-situ server systems using renewable energy. We develop a novel energy buffering mechanism and a unique joint spatio-temporal power management strategy to coordinate standalone power supplies and in-situ servers. We present detailed deployment experiences to quantify how our design fits with in-situ processing in the real world. Overall, InSURE yields 20%~60% improvements over a state-of-the-art baseline. It maintains impressive control effectiveness in under-provisioned environment and can economically scale along with the data processing needs. The proposed design is well complementary to todays grid-connected cloud data centers and provides competitive cost-effectiveness.

Design and implementation of a video display processing SoC for full HD LCD TV

This paper presents a single-chip video display processing SoC design, which is able to provide the complete post-processing solution for Full HD LCD TV. Three novel integrated key techniques have been discussed in detail, including multi-port AXI bus controller, robust film-mode detection and edge-directed content adaptive image interpolation. The overall architecture and algorithms are verified in FPGA platform and fabricated at TSMC 0.13 μm 1P6M CMOS technology node. The SoC chip is also extensively evaluated in a digital HDTV prototype system.

Leveraging Heterogeneous Power for Improving Datacenter Efficiency and Resiliency

Power mismatching between supply and demand has emerged as a top issue in modern datacenters that are under-provisioned or powered by intermittent power supplies. Recent proposals are primarily limited to leveraging uninterruptible power supplies (UPS) to handle power mismatching, and therefore lack the capability of efficiently handling the irregular peak power mismatches. In this paper we propose hPower, the first heterogeneous energy buffering strategy that incorporates supercapacitors into existing datacenters to handle power mismatch. Our technique exploits power supply diversity and smart load assignment to provide efficiency-aware and emergency-aware power mismatch management. We show that hPower could improve energy efficiency by 30 percent, extend UPS lifetime by 4.3×, and reduce system downtime by 36 percent. It allows datacenters to adapt themselves to various power supply anomalies, thereby improving operational efficiency and resiliency.

BAAT: Towards Dynamically Managing Battery Aging in Green Datacenters

Energy storage devices (batteries) have shown great promise in eliminating supply/demand power mismatch and reducing energy/power cost in green datacenters. These important components progressively age due to irregular usage patterns, which result in less effective capacity and even pose serious threat to server availability. Nevertheless, prior proposals largely ignore the aging issue of batteries or simply use ad-hoc discharge capping to extend their lifetime. To fill this critical void, we thoroughly investigate battery aging on a heavily instrumented prototype over an observation period of six months. We propose battery anti-aging treatment (BAAT), a novel framework for hiding, reducing, and planning the battery aging effects. We show that BAAT can extend battery lifetime by 69%. It enables datacenters to maximally utilize energy storage resources to enhance availability and boost performance. Moreover, it reduces 26% battery cost and allows datacenters to economically scale in the big data era.

Leveraging distributed UPS energy for managing solar energy powered data centers

As the essential infrastructures for cloud computing, data centers are facing increasing pressure of capping tremendous power consumption and carbon emission. Currently, many proposals have leveraged energy storage devices (in the form of UPS batteries) to provide buffered energy during peak power demands for reducing data center power cost. In addition, energy storage devices are also used to smooth out power supply variation in renewable energy powered data centers for effectively cutting down carbon emissions. However, a joint management of peak power shaving and renewable energy harvesting in data centers is still lacking. Existing schemes either incur green energy efficiency degradation or sacrifice data center availability and workload performance. In this paper, we propose a novel power delivery scheme Re-UPS, which is based on existing UPS infrastructures and employs renewable energy to cap peak power consumption and carbon emission in data centers. Re-UPS leverages distributed energy storage architecture and dynamic online heuristic energy management strategy to enable data centers to achieve the best optimization among maximizing renewable energy harvest, shaving peak power demands and improving UPS system availability. We evaluate Re-UPS under different environmental conditions and workload. Our results show that Re-UPS can increase green energy utilization by 74.1%, improve backup energy capacity by 16.5%, and extend the battery life by 10.6%.

Towards an Adaptive Multi-Power-Source Datacenter

Big data and cloud computing are accelerating the capacity growth of datacenters all over the world. Their energy costs and environmental issues have pushed datacenter operators to explore and integrate alternative energy sources, such as various renewable energy supplies and energy storage devices. Designing datacenters powered by multi-power supplies in the smart grid environment is becoming a promising trend in the next few decades. However, gracefully provisioning various power sources and efficiently manage them in datacenter is a significant challenge. In this paper, we explore an unconventional fine-grained power distribution architecture for multi-source powered datacenters. We thoroughly investigate how to deliver and manage multiple power sources from the power generation plant outside of the datacenter to datacenter inside. We then propose a novel Power Switch Network (PSN) for datacenters. PSN is a reconfigurable multi-power-source distribution architecture which enables datacenter to distribute various power sources with a fine-grained manner. Moreover, a tailored machine learning based power sources management framework is proposed for PSN to dynamically select different power sources and optimize user-demanded performance metrics. Compared with the conventional single-switch system, evaluation results show that PSN could improve solar energy utilization by 39.6%, reduce utility power cost by 11.1% and improve workload performance by 33.8%, meanwhile enhancing battery lifetime by 9.3%. We expect that our work could provide valuable guidelines for the emerging multi-power-source datacenter to improve their efficiency, sustainability and economy.

A high performance and low cost video processing SoC for digital HDTV systems

This paper proposes a video processing SoC for Flat Panel Displays and describes the employed video processing algorithms. Three key techniques integrated in the proposed chip are introduced, including spatio-temporal adaptive TV decoder, square-nonlinear interpolation scaler and efficient memory controller. The overall video processing architecture is fabricated at 0.18um CMOS technology node, and the IC is extensively evaluated in a prototype HDTV Set. The proposed SoC chip can well supports both SDTV and HDTV signals, while providing high quality images.

Managing Battery Aging for High Energy Availability in Green Datacenters

Energy storage devices (ESD), such as UPS batteries, have been repurposed in datacenter as a promising tuning knob for peak power shaving and power cost reducing. However, batteries progressively aging due to irregular usage patterns, which result in less effective capacity and even pose serious threat to server availability. Nevertheless, prior proposals largely ignore the aging issues of battery which may lead to low energy availability for datacenter servers. To fill this critical void, we thoroughly investigate battery aging on a heavily instrumented prototype system over an observation period of ten months. We propose Battery Anti-Aging Treatment Plus (BAAT-P ), a novel power delivery architecture included aging management algorithms from the perspective of computing system to hide, reduce, mitigate and plan the battery aging effects for high energy availability in datacenter. Our techniques exploit diverse battery aging mechanisms and dynamic aging management algorithms to provide system-level availability guarantee for datacenter. We evaluate the BAAT-P design with a real prototype. Compared with a battery powered datacenter without aging management policies, the results show that BAAT-P can extend battery lifetime by 72 percent, reduce battery cost by 33 percent and effectively improve energy availability for datacenter servers while maintaining workload performance for the performance critical workloads.

Towards Automated Provisioning and Emergency Handling in Renewable Energy Powered Datacenters

Designing eco-friendly system has been at the forefront of computing research. Faced with a growing concern about the server energy expenditure and the climate change, both industry and academia start to show high interest in computing systems powered by renewable energy sources. Existing proposals on this issue mainly focus on optimizing resource utilization or workload performance. The key supporting hardware structures for cross-layer power management and emergency handling mechanisms are often left unexplored. This paper presents GreenPod, a research framework for exploring scalable and dependable renewable power management in datacenters. An important feature of GreenPod is that it enables joint management of server power supplies and virtualized server workloads. Its interactive communication portal between servers and power supplies allows datacenter operators to perform real-time renewable energy driven load migration and power emergency handling. Based on our system prototype, we discuss an important topic: virtual machine (VM) workloads survival when facing extended utility outage and insufficient onsite renewable power budget. We show that whether a VM can survive depends on the operating frequencies and workload characteristics. The proposed framework can greatly encourage and facilitate innovative research in dependable green computing.