With the rapid development of technology such as social networks, e-commerce, and mobile internet, a large amount of text data with emotional content is generated. Researchers and practitioners have been actively engaged in the development of sentiment analysis (SA) systems to analyze this content. However, SA systems are not robust enough. To address this issue, we propose MTSA, a multi-granularity metamorphic testing framework that automatically generates test cases to evaluate the SA system's robustness. We summarize fourteen metamorphic relations (MRs) at different granularities, including character level, word level, and sentence level. MTSA employs these MRs to generate test cases that simulate real user input to evaluate SA systems. In our experiment, we employed MTSA to evaluate nine different applications, including four commercial SA systems, four academic models, and one software package. The results show that MTSA achieves error triggering rates (ETR) in the range of 3.36% to 73.03 % across the nine software under test (SUT). Our empirical evaluation demonstrates that all types of SA systems, including business SA systems, academic models, and software packages, have an average ETR above 25%. Therefore, MTSA is capable of generating valid test cases to evaluate SA systems.

NAND flash has been widely adopted in storage systems due to its better read and write performance and lower power consumption over traditional mechanical hard drives. To meet the increasing performance demand of modern applications, recent studies speed up flash accesses by exploiting access latency variations at the device level. Unfortunately, existing flash access schedulers are still oblivious to such variations, leading to suboptimal I/O performance improvements. In this paper, we propose DLV, a novel flash access scheduler for exploring scheduling opportunities due to device level access latency variations. DLV improves flash access speeds based on process variations and data retention time difference across flash blocks. More importantly, DLV integrates access speed optimization with access scheduling such that the average access response time can be effectively reduced on flash memory storage systems. Our experimental results show that DLV achieves an average of 41.5% performance improvement over the state-of-the-art.

NAND flash memory has been widely used in storage systems by offering greater read/write performance and lower power consumption than mechanical hard drives. Recently, the tradeoff between endurance, write speed, and read speed has been exploited from many ways for I/O performance improvement, which also induce the read/write latency variation. In this paper, the latency variation is exploited in I/O scheduling for access characteristic guided read and write latency minimization. First, with the understanding of the relationship among read latency, write latency and raw bit error rates (RBER), different ways to exploit the relationship for read and write latency reduction is discussed. Then, an I/O scheduling scheme is proposed by using hotness and retention age of accessed data to determine the speed of writes or reads, giving scheduling priority to fast writes and fast reads for conflict reduction. Experiments with various traces reveal that the proposed technique achieves significant read and write performance improvements.

Modern storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world proprietary datasets are too large to be copied onto a test storage system, and most data cannot be shared due to privacy issues, a benchmark needs to generate data synthetically. To ensure that the result is accurate, it is necessary to generate data content based on the characterization of real-world data properties that influence the storage system performance during the execution of a benchmark. The existing approach, called SDGen, cannot guarantee that the benchmark result is accurate in storage systems that have built-in word-based compressors. The reason is that SDGen characterizes the properties that influence compression performance only at the byte level, and no properties are characterized at the word level. To address this problem, we present TextGen, a realistic text data content generation method for modern storage system benchmarks. TextGen builds the word corpus by segmenting real-world text datasets, and creates a word-frequency distribution by counting each word in the corpus. To improve data generation performance, the word-frequency distribution is fitted to a lognormal distribution by maximum likelihood estimation. The Monte Carlo approach is used to generate synthetic data. The running time of TextGen generation depends only on the expected data size, which means that the time complexity of TextGen is O ( n ). To evaluate TextGen, four real-world datasets were used to perform an experiment. The experimental results show that, compared with SDGen, the compression performance and compression ratio of the datasets generated by TextGen deviate less from real-world datasets when end-tagged dense code, a representative of word-based compressors, is evaluated.

To satisfy the ever-increasing performance demand of Big Data and critical applications the data management needs to offer the flexible schema, high availability, light weight replica, high volume and high scalability features so as to facilitate the transaction. The in memory database (IMDB) eliminates the I/O bottleneck by storing data in main memory. We give a deeper analysis of current main-stream IMDB systems performance which focuses on the data structure, architecture, volume, concurrency, availability and scalability. The V3 performance model is proposed to evaluate the Velocity, Volume and Varity of the 19 IMDB systems, in order to highlight the candidates with realtime transaction and high volume processing capacity coordinately. Test results clearly demonstrate that NewSQL is better at dealing with high-frequency trading models. To fully utilize the advantages of the multi-core and many-core processors capability improvements, a three-level optimization design strategy, which includes the memory-access level, the kernel-speedup level and the data-partition level also be proposed using the hardware parallelism for achieving task-level and data-level parallelism of IMDB programs, guarantees the IMDB could accelerate the real-time transaction in an efficient way. We believe that IMDB should become a compulsive option for enterprise users.

The emerging demand for personalized hearing aids requires the filter bank of a hearing aid system to be capable of decomposing the sound waves in accordance with the characteristic of the patient's hearing loss. In this paper, an efficient adjustable filter bank is proposed to achieve this goal. By careful design, the number of the subbands as well as the location of the subbands can be easily adjusted by changing a 4-bit control signal. The proposed filter bank has extremely low complexity due to the adoption of fractional interpolation and the technique of symmetric and complementary filters. Only one prototype filter is needed for each of the stages, the multiple passbands generation stage and masking stage. We show, by means of examples, that the proposed filter bank can meet different needs of hearing loss cases with acceptable delay.

In heterogeneous fault-tolerant system, especially high performance computer system, the issue of providing system with high availability assurance for real-time applications which have availability requirements has been widespread concerned. While, few research concentrates on combining real-time application availability requirement with scheduling algorithm. In this paper, an availability approached task scheduling algorithm is proposed. On the basis of heterogeneous fault-tolerant system scheduler and scheduling algorithm designation, we can improve the system availability without increasing additional hardware costs, and shorten task average response time, in addition, schedule task with high efficiency and reliability. Experiment results show that, such availability approached task scheduling algorithm has a system performance advantage over the traditional system task scheduling algorithms, it achieves the goal of balancing availability and task response time in heterogeneous fault-tolerant system, thus improve the system availability.

This paper presents the design of the Better Life 2.0 framework, which facilitates implementation of large-scale social intelligence application in cloud environment. We argued that more and more mobile social applications in pervasive computing need to be implemented this way, with a lot of user generated activities in social networking websites. We adopted the Case-based Reasoning technique to provide logical reasoning and outlined design considerations when porting a typical CBR framework jCOLIBRI2 to cloud, using Hadoop's various services (HDFS, HBase). These services allow efficient case base management (e.g. case insertion) and distribution of computational intensive jobs to speed up reasoning process more than 5 times. With the scalability merit of MapReduce, we can improve recommendation service with social network analysis that needs to handle millions of users' social activities.

Wireless sensor networks (WSNs) should handle multiple sensing tasks for various applications. How to improve the quality of the data acquired in such resource constrained environment is a challenging issue. In this paper, we propose a sensor-channel co-allocation model for scheduling the sensing tasks. The proposed model considers the capability, coupling and load balancing constraints for sensing data acquisition, and can guarantee transmission of sensed data in real-time while avoiding data incompleteness in an efficient way. A spatiotemporal metric called sensing-span is proposed to evaluate the tasks' execution cost of achieving desired data quality. We extend computation task scheduling a lgorithms to support sensor-channel co-allocation problem and a heuristic called Minimum Service Capability Fragment (MSCF) is introduced for task scheduling to minimize the waste of reserved channel capacity. Simulation results show that MSCF can improve the performance of data acquisition in WSNs as compared with other heuristics, when scheduling a large number of concurrent data acquisition tasks.

Constructing monitoring systems beforehand cannot satisfy the requirement of dynamically organizing resources. This paper describes a script-based approach to automate the deployment of grid monitoring service components. An automated deployment monitoring mechanism uses the information of dynamical