PRESC ²: Efficient self-reconfiguration of cache strategies for elastic caching platforms

Elastic caching platforms (ECPs) play an important role in accelerating the performance of Web applications. Several cache strategies have been proposed for ECPs to manage data access and distributions while maintaining the service availability. In our earlier research, we have demonstrated that there is no "one-fits-all" strategy for heterogeneous scenarios and the selection of the optimal strategy is related with workload patterns, cluster size and the number of concurrent users. In this paper, we present a new reconfiguration framework named PRESC2. It applies machine learning approaches to determine an optimal cache strategy and supports online optimization of performance model through trace-driven simulation or semi-supervised classification. Besides, the authors also propose a robust cache entries synchronization algorithm and a new optimization mechanism to further lower the adaptation costs. In our experiments, we find that PRESC2 improves the elasticity of ECPs and brings big performance gains when compared with static configurations. © 2013 Springer-Verlag Wien.Elastic caching platforms (ECPs) play an important role in accelerating the performance of Web applications. Several cache strategies have been proposed for ECPs to manage data access and distributions while maintaining the service availability. In our earlier research, we have demonstrated that there is no "one-fits-all" strategy for heterogeneous scenarios and the selection of the optimal strategy is related with workload patterns, cluster size and the number of concurrent users. In this paper, we present a new reconfiguration framework named PRESC2. It applies machine learning approaches to determine an optimal cache strategy and supports online optimization of performance model through trace-driven simulation or semi-supervised classification. Besides, the authors also propose a robust cache entries synchronization algorithm and a new optimization mechanism to further lower the adaptation costs. In our experiments, we find that PRESC2 improves the elasticity of ECPs and brings big performance gains when compared with static configurations. © 2013 Springer-Verlag Wien

VM image update notification mechanism based on pub/sub paradigm in cloud

Virtual machine image encapsulates the whole software stack including operating system, middleware, user application and other software products. Failure occurred in any layer of the software stack will be treated as image failure. However, virtual machine image with potential failures can be convert to template and spread to a wide range by means of template replication. And this paper refer to this phenomenon as "image failure propagation". Usually, patching is a widely adopted solution to resolve software failures. Nevertheless, virtual machine image patches are difficult to deliver to the final users in cloud computing environment for its openness and multi-tenancy features. This paper described image failure propagation model for the first time and proposed a promoting mechanism based on pub/sub computing paradigm to combat with the patching delivery problem.Virtual machine image encapsulates the whole software stack including operating system, middleware, user application and other software products. Failure occurred in any layer of the software stack will be treated as image failure. However, virtual machine image with potential failures can be convert to template and spread to a wide range by means of template replication. And this paper refer to this phenomenon as "image failure propagation". Usually, patching is a widely adopted solution to resolve software failures. Nevertheless, virtual machine image patches are difficult to deliver to the final users in cloud computing environment for its openness and multi-tenancy features. This paper described image failure propagation model for the first time and proposed a promoting mechanism based on pub/sub computing paradigm to combat with the patching delivery problem

Workload-aware anomaly detection for web applications

The failure of Web applications often affects a large population of customers, and leads to severe economic loss. Anomaly detection is essential for improving the reliability of Web applications. Current approaches model correlations among metrics, and detect anomalies when the correlations are broken. However, dynamic workloads cause the metric correlations to change over time. Moreover, modeling various metric correlations are difficult in complex Web applications. This paper addresses these problems and proposes an online anomaly detection approach for Web applications. We present an incremental clustering algorithm for training workload patterns online, and employ the local outlier factor (LOF) in the recognized workload pattern to detect anomalies. In addition, we locate the anomalous metrics with the Student's t-test method. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to (1) capture workload fluctuations accurately, (2) detect typical faults effectively and (3) has advantages over two contemporary ones in accuracy. © 2013 Elsevier Inc.The failure of Web applications often affects a large population of customers, and leads to severe economic loss. Anomaly detection is essential for improving the reliability of Web applications. Current approaches model correlations among metrics, and detect anomalies when the correlations are broken. However, dynamic workloads cause the metric correlations to change over time. Moreover, modeling various metric correlations are difficult in complex Web applications. This paper addresses these problems and proposes an online anomaly detection approach for Web applications. We present an incremental clustering algorithm for training workload patterns online, and employ the local outlier factor (LOF) in the recognized workload pattern to detect anomalies. In addition, we locate the anomalous metrics with the Student's t-test method. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to (1) capture workload fluctuations accurately, (2) detect typical faults effectively and (3) has advantages over two contemporary ones in accuracy. © 2013 Elsevier Inc

Mining user daily behavior patterns from access logs of massive software and websites

Everyone has a characteristic pattern of daily activities. This study applies cluster analysis to identify a computer user's daily behavior patterns based on 1000 China users' 4-weeks software and web usage. Clustering models are built for 4 different behavior definition methods with different time period divisions and feature measurement selections. With these patterns, we build classification models to predict new users' daily behavior pattern with their half day activity logs. For example, if we know one user use computer for entertainment in the morning, we can predict his behavior in the afternoon and evening. The prediction model can be used to recommend suitable items to users according to their current behavior status. Our method can get 92.5% prediction correctness for the best.Everyone has a characteristic pattern of daily activities. This study applies cluster analysis to identify a computer user's daily behavior patterns based on 1000 China users' 4-weeks software and web usage. Clustering models are built for 4 different behavior definition methods with different time period divisions and feature measurement selections. With these patterns, we build classification models to predict new users' daily behavior pattern with their half day activity logs. For example, if we know one user use computer for entertainment in the morning, we can predict his behavior in the afternoon and evening. The prediction model can be used to recommend suitable items to users according to their current behavior status. Our method can get 92.5% prediction correctness for the best

Detecting performance anomaly with correlation analysis for Internetware

Internetware has become an emerging software paradigm to provide Internet services. The performance anomaly of Internetware services not only affects user experience, but also causes severe economic loss to service providers. Diagnosing performance anomalies has become one of the keys to improving the quality of service (QoS) of Internetware. Existing approaches create a system model to predict performance. Then, the prediction from the model is compared with the observation; a significant deviation may signal the occurrence of a performance anomaly. However, these approaches require domain knowledge and parameterization efforts. Moreover, dynamic workloads affect the accuracy of performance prediction. To address these issues, we propose a correlation analysis based approach to detecting the performance anomaly for Internetware. We use kernel canonical correlation analysis (KCCA) to model the correlation between workloads and performance based on monitoring data. Furthermore, we detect anomalous correlation coefficients by XmR control charts, which detect the anomalous coefficient and trend without a priori knowledge. Finally, we adopt a feature selection method (Relief) to locate the anomalous metrics. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to capture the performance anomaly, and locate the metrics relating to the cause of anomaly. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg.Internetware has become an emerging software paradigm to provide Internet services. The performance anomaly of Internetware services not only affects user experience, but also causes severe economic loss to service providers. Diagnosing performance anomalies has become one of the keys to improving the quality of service (QoS) of Internetware. Existing approaches create a system model to predict performance. Then, the prediction from the model is compared with the observation; a significant deviation may signal the occurrence of a performance anomaly. However, these approaches require domain knowledge and parameterization efforts. Moreover, dynamic workloads affect the accuracy of performance prediction. To address these issues, we propose a correlation analysis based approach to detecting the performance anomaly for Internetware. We use kernel canonical correlation analysis (KCCA) to model the correlation between workloads and performance based on monitoring data. Furthermore, we detect anomalous correlation coefficients by XmR control charts, which detect the anomalous coefficient and trend without a priori knowledge. Finally, we adopt a feature selection method (Relief) to locate the anomalous metrics. We evaluated our approach on a testbed running the TPC-W industry-standard benchmark. The experimental results show that our approach is able to capture the performance anomaly, and locate the metrics relating to the cause of anomaly. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg

Relationships between odors and algae and water quality in Dongting Lake

The Dongting Lake is the second largest freshwater lake in China. It is for the first time to study on the taste and odor compounds in the lake. Five chemical compounds, dimethylsulfide (DMS), dimethyltrisulfide (DTMS), 2-methylisoborneol (MIB), β-cyclocitral, and geosmin (GSM) were detected and the highest concentrations of these compounds were 500.80, 28.80, 21.84, 14.50, and 22.40 ng/L, respectively. DMS, DMTS and β-cyclocitral were closely related to Melosira and/or Stephanodiscus in Bacillariophyceae. This suggested that these algae probably were the sources of DMS, DMTS, and β-cyclocitral in the Dongting Lake. The particle-bound MIB and GSM were not detected, which suggested that MIB and GSM were probably produced by microorganisms in sediment, the soil around Dongting Lake or the rotten algae. The trophic state of the Dongting Lake is mesotrophication with TLI of three regions between 42.7 and 48.3. The concentrations of these five odors were directly influenced by the water environmental factors

Camera based cross devices manipulating with augmented reality

Heterogeneous devices with large displays have become pervasive in people lives. While conveying public information, most of them are lack of support for human interaction. This paper proposes a cross devices interaction system for users to conveniently manipulate the content on public displays through personal mobile phones with camera. Users can acquire, insert, and act on elements of the displayed content with simple operations on mobile phones. Cross device elements targeting is done with an efficient visual based object matching and tracking algorithm when users capture the displayed contents as a live video. Augmented information for the elements is shown on the mobile phone screen. The content in the large displays can then be operated by clicking and dragging, etc., on the mobile phone. The proposed system can be applied to interactive advertisements, multi-user games, and conference discussions. An evaluation consisting of a series of experiments demonstrated the usability and efficiency of the proposed system. © 2013 IEEE.Heterogeneous devices with large displays have become pervasive in people lives. While conveying public information, most of them are lack of support for human interaction. This paper proposes a cross devices interaction system for users to conveniently manipulate the content on public displays through personal mobile phones with camera. Users can acquire, insert, and act on elements of the displayed content with simple operations on mobile phones. Cross device elements targeting is done with an efficient visual based object matching and tracking algorithm when users capture the displayed contents as a live video. Augmented information for the elements is shown on the mobile phone screen. The content in the large displays can then be operated by clicking and dragging, etc., on the mobile phone. The proposed system can be applied to interactive advertisements, multi-user games, and conference discussions. An evaluation consisting of a series of experiments demonstrated the usability and efficiency of the proposed system. © 2013 IEEE

Ambipolar inverters using SnO thin-film transistors with balanced electron and hole mobilities

Ambipolar thin film transistors have attracted increasing research interests due to their promising applications in complementary logic circuits and the dissipative charge transporting devices. Here, we report the fabrication of an ambipolar transistor using tin mono-oxide (SnO) as a channel, which possesses balanced electron and hole field-effect mobilities. A complementary metal oxide semiconductor-like inverter using the SnO dual operation transistors is demonstrated with a maximum gain up to 30 and long-term air stability. Such logic device configuration would simplify the circuit design and fabrication process, offering more opportunities for designing and constructing oxide-based logic circuits. VC 2012 American Institute of Physics

Late Holocene elemental and isotopic carbon and nitrogen records from the East China Sea inner shelf: Implications for monsoon and upwelling

The East China Sea (ECS) is characterized by a relatively high riverine martial input that is associated with East Asian monsoon systems. In this study, we investigated a 272 cm-long sediment core (THB-2) from the ECS inner shelf for the AMS C-14 dating, grain size, total organic carbon (TOC), total nitrogen (TN), and stable carbon and nitrogen (delta C-13 and delta N-15) isotopes to provide insights into previous changes in the monsoon-climate and the intensity of the coastal upwelling. A three end-member (riverine, deltaic, and marine) mixing model was applied to determine the temporal variations in sedimentary organic matter sources. The transport and burial of the Changjiang riverine organic carbon in the ECS inner shelf is strongly controlled by the combined effects of the monsoon-climate and human activities during the late Holocene. The sedimentary delta N-15 records of the THB-2 core are more complex and may be best explained by changes in the nutrient utilization in the coastal zone surface waters. The downcore variations of the marine organic matter-delta N-15 are closely related to the intensity of East Asian summer monsoon during the last 3.6 ka. The stronger East Asian summer monsoon likely favored the intensive coastal upwelling and additional Changjiang discharge during the 3.6-2.7 ka and 0.4-0 ka periods, which triggered the greater relative nitrate utilization in the coastal surface water.</p

Atmospheric lead in urban Guiyang, Southwest China: Isotopic sourcesignatures

Total suspended particles (TSP) and their source-related samples from Guiyang, Southwest China, were collected and analyzed for their lead (Pb) concentrations and Pb isotopic compositions, to identify the sources of atmosphere lead in urban Guiyang. Coals from Guizhou Province had significantly high radiogenic Pb, different to those from North China. Local vehicle exhaust had similar Pb isotope ratios to those of other areas in China. Pb isotopic compositions of atmospheric aerosols, rainwaters, plant samples, and acid-soluble fraction of street dusts were similar to each other. The results clearly suggest that the PbeZn ore-related industrial emission, and/or vehicle exhaust, rather than the local coal combustion, are the main sources of atmospheric Pb in Guiyang. Furthermore, binary mixing model indicates that the contribution of coal combustion to the local atmospheric Pb decreased from about 40% in 1988 to about 10% in 2013.</p