In recent years, the development and utilization of Lithium-ion batteries have further promoted the development of electric vehicle and related battery management systems. Estimating the battery state of charge (SoC) is essential for battery management systems. Most studies on SoC estimation have focused on model-based observer designing. The sliding mode control strategy is suitable for estimating the battery SoC due to its strong robustness, insensitivity to parameter perturbation and external disturbance, and high estimation accuracy. This paper aims to design a battery SoC estimator using the proposed fuzzy sliding mode control strategy. The fuzzy control logic is introduced to suppress chattering by regulating the switching gain in the sliding mode control law. Firstly, the second-order Thevenin equivalent circuit model of the battery is established. Then a fuzzy sliding mode observer is designed based on the equivalent circuit model to estimate the battery’s SoC. Finally, the designed observer is simulated and verified.

Companies like Alibaba operate tens of data centers (DCs) across geographically distributed locations. These DCs collectively provide the storage space and computing power for the company, storing EBs of data and serving millions of batch analytics jobs every day. In Alibaba, as our businesses grow, there are more and more cross-DC dependencies caused by jobs reading data from remote DCs. Consequently, the precious wide area network bandwidth becomes a major bottleneck for operating geo-distributed DCs at scale. In this paper, we present Yugong --- a system that manages data placement and job placement in Alibaba's geo-distributed DCs, with the objective to minimize cross-DC bandwidth usage. Yugong uses three methods, namely project placement, table replication, and job outsourcing, to address the issues of high bandwidth consumption across the DCs. We give the details of Yugong's design and implementation for the three methods, and describe how it cooperates with other systems (e.g., Alibaba's big data analytics platform and cluster scheduler) to improve the productivity of the DCs. We also report comprehensive performance evaluation results, which validate the design of Yugong and show that significant reduction in cross-DC bandwidth usage has been achieved.

Our global economy increasingly depends on our ability to gather, analyze, link, and compare very large data sets. Keeping up with such big data poses challenges in terms of both computational performance and energy efficiency, and motivates different approaches to explore data center systems and architectures. To better understand the processor design decisions in context of data analytics in data centers, we conduct comprehensive evaluations using representative data analaytics workloads on representative conventional multi-core and many-core processors. After a comprehensive analysis of performance, power, energy efficiency and performance-cost efficiency, we have the following observations: contrasted with the conventional wisdom that uses wimpy many-core processors to improve energy-efficiency, the brawny multi-core processors with SMT (simultaneous multithreading) and dynamic overclocking technologies outperform the counterparts in terms of not only execution time, but also energy-efficiency for most of data analytics workloads in our experiments.

Systems chemical biology integrate chemistry, biology and computation tools as a whole system, which can help researchers to deeply study the interaction and relationship among small molecules, such as genes, proteins, targets, compounds and so on. With systems chemical biology, researchers can concentrate on new way of drug discovery, including drug target path discovery, which can not only help biomedical researchers to find evidences for existing disease associate genes, but also to design new effect medicine based on targets. Network based approaches are the state-of-art solutions for drug target path discovery, however, there are still some challenges: 1) The quality of the network dominate the efficiency and accuracy of the results, therefore a well designed network is quite important on drug target path discovery mission; 2) the existing network based approaches only work on small graph, it can not handle massive data well. In the paper, we designed a novel framework of systems chemical biology based on semantic big data. In the paper, we proposed a novel drug target path discovery approach. It can identify targets associated with specific medicines (disease) and the path of relationship based on a RDF semantic D-T network. The ranking of candidate targets is performed through an improved parallel random walk with restart algorithm. The experimental studies show that the proposed approaches can efficiently discover drug target relationship path, meanwhile, the approaches have good scalability which are suitable for big data analysis.

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite-BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. Currently, we choose 19 big data benchmarks from dimensions of application scenarios, operations/ algorithms, data types, data sources, software stacks, and application types, and they are comprehensive for fairly measuring and evaluating big data systems and architecture. BigDataBench is publicly available from the project home page http://prof.ict.ac.cn/BigDataBench. Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity, which measures the ratio of the total number of instructions divided by the total byte number of memory accesses; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache (L1I) misses per 1000 instructions (in short, MPKI) of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.

Along with today's data explosion and application diversification, a variety of hardware platforms for data centers are emerging and are attracting interests from both industry and academia. The existing hardware platforms represent a wide range of implementation approaches, and different hardware have different strengths. In this paper, we conduct comprehensive evaluations on three representative data center systems based on BigDataBench, which is a benchmark suite for benchmarking and ranking systems running big data applications. Then we explore the relative performance of the three implementation approaches with different big data applications, and provide strong guidance for the data center system construction. Through our experiments, we has inferred that a data center system based on specific hardware has different performance in the context of different applications and data volumes. When we construct a system, we can take into account not only the performance or energy consumption of the pure hardwares, but also the application-level characteristics. Data scale, application type and complexity should be considered comprehensively when researchers or architects plan to choose fundamental components for their data center system.

The rapid growth of data volume brings big challenges to the data center computing, and energy efficiency is one of the most concerned problems. Researchers from various fields are now proposing solutions to green the data center operations. Power usage effectiveness metric plays an important role in the energy saving research. However, the exising usage effectiveness metrics focus on measuring the relationship between the total facility energy consumed and the IT equipment energy consumed, without reflecting the energy efficiency of applications. In this paper, we analyze the requirements of application-level metrics for power usage efficiency of the data centers, and propose two novel energy efficiency metrics to provide strong guidance and useful insight to data center design and optimization.

Online aggregation is a promising solution to achieving fast early responses for interactive ad-hoc queries that compute aggregates on massive data. To process large datasets on large-scale computing clusters, MapReduce has been introduced as a popular paradigm into many data analysis applications. However, typical MapReduce implementations are not well-suited to analytic tasks, since they are geared towards batch processing. With the increasing popularity of ad-hoc analytic query processing over enormous datasets, processing aggregate queries using MapReduce in an online fashion is therefore an emerging important application need. We present a MapReduce-based online aggregation system called COLA, which provides progressive approximate aggregate answers for both single table and multiple joined tables. COLA provides an online aggregation execution engine with novel sampling techniques to support incremental and continuous computing of aggregation, and minimize the waiting time before an acceptably precise estimate is available. In addition, user-friendly SQL queries are supported in COLA. Furthermore, COLA can implicitly convert non-OLA jobs into online version so that users don't have to write any special-purpose code to make estimates.

Cloud-based data management systems are emerging as scalable, fault-tolerant, and efficient solutions to manage large volumes of data with cost effective infrastructures, and more and more data analysis applications are migrated to the cloud. As an attractive solution to provide a quick sketch of massive data before a long wait of the final accurate query result, online processing of aggregate queries in the cloud is of paramount importance. This problem is challenging to solve because of the large block based data organization and distributed processing mode in the cloud. In this paper, we present COLA, a system for Cloud Online Aggregation to provide progressive approximate answers for both single tables and joined multiple tables. We develop an online query processing algorithm for MapReduce to support incremental and continuous computing of aggregations on joins which minimizes the waiting time before an acceptable estimate is achieved. We formulate a statistical foundation that supports block-level sampling for single-table online aggregations and effective estimation of approximate results and confidence intervals of statistical significance. We also develop a two-phase stratified sampling method to support multi-table aggregations to improve the approximate query answers and speed up the convergence of confidence intervals. We implement COLA in Hadoop, and our experiments demonstrate that COLA can deliver reasonable precise online estimates within a time period two orders of magnitude shorter than that used to produce exact answers.