With the increasingly severe cyberspace security situation, situational awareness has become a focus in the field of Cyberspace Security. The effect of situational awareness decision-making depends on the breadth and depth of data, the rationality of data processing, and the clarity and intuition of the presentation method. This paper designs a situational awareness platform, introduces vulnerability identification and fusion, situational awareness and data visualization. In particular, the data fusion method of online and offline multi-source vulnerability is proposed to enrich the data sources, and the soft splicing technology of display arrays is used to improve the visual compatibility under high resolution. In this paper, the data processing method is continuously optimized through experiments, which improves the overall processing efficiency and prediction accuracy of the platform.

With the rapid development of the big data system, The big data system has the characteristics of large data scale, diverse data and high computational complexity. Its testing method has to be constantly improved. By analyzing the general software quality model, and combining the characteristics of the big data software, a set of quality model for the big data software is formed.

System and software quality evaluation is an important method of quality assurance, and its standardization provides quality requirements and evaluation supported by quality measurement. ISO/IEC organizations have also published a series of standards on system and software quality requirements and evaluation, which is significant for assuring the quality of system and software. This paper introduces quality evaluation based on SQuaRE series standards, and analyzes the relationships among quality model, measurement, requirements, evaluation. Besides, considerations and suggestions are given when applying the measurement function to quality evaluation. Finally, comparison with different methods of quantifying quality characteristics and sub-characteristics with the measured values is analyzed.

Random walk is an effective network-based method for information mining. However, the computational complexity of random walk limits its application on large scale datasets. This study evaluating accuracy and performance of graphics processing units (GPUs) in accelerating random walk computation on heterogeneous networks. We first introduce a general heterogeneous network to describe user-item relevance model. We apply GPU-accelerated random walk method to this network and investigate the performance of the method. We demonstrate our method via large-scale experiments across MovieLens datasets and obtain a more than 12× speedup. The results shows the effectiveness and promise of the approach.

Recently, power consumption of radio access networks (RAN) has attracted a lot of attention since energy cost takes up a vast portion of operational expenditure (OPEX). Specifically, more than half of the energy is consumed by base stations (BS). Baseband Unit (BBU), which is responsible for baseband signal processing, is an important part of BS, and BBU consolidation in the form of a cluster is an promising solution to enhance energy efficiency. Although there are several existing work addressing similar ideas from theoretical perspective, there is still a gap between theoretical and practical solutions. Hence, by leveraging cloud computing and software-defined radio technologies, we develop a BBU cluster testbed, eBase, to consolidates multiple isolated BBUs into a virtualized BBU and then provide unified baseband resource pool for RAN. We present the architecture and implementation of eBase in detail. We also design a distributed resource management middleware to realize comprehensive resource management strategy for BBU cluster in order to improve energy efficiency. Through experiments, we find eBase can reduce total energy consumption by about 20% while satisfying about 95% QoS. These results not only demonstrate BBU cluster's significant capability to reduce energy cost and improve resource utilization, but also show eBase is a capable prototyping testbed for cloud radio access network research.

Recently, power consumption of radio access networks (RAN) has attracted a lot of attention since energy cost takes up a vast portion of operational expenditure (OPEX). Specifically, more than half of the energy is consumed by base stations (BS). Baseband Unit (BBU), which is responsible for baseband signal processing, is an important part of BS but still lacks effective power management solutions to improve energy efficiency. We present a power management middleware, GreenBase, to improve energy efficiency for BBUs. GreenBase consists of a software-based power metering tool, power and performance monitors as well as managers, an extensible power management policy library, and an energy efficiency analyzer. To evaluate this middleware, we construct a BBU testbed running video streaming applications based on software-defined radio platform GNU Radio/USRP. The experimental results validate GreenBase can achieve power and performance target with integrated power management policies. In particular, we find GreenBase can reduce total energy consumption by about 13% while satisfying target statistic QoS using a stochastic QoS controller.

SUMMARY: ChemMapper is an online platform to predict polypharmacology effect and mode of action for small molecules based on 3D similarity computation. ChemMapper collects >350 000 chemical structures with bioactivities and associated target annotations (as well as >3 000 000 non-annotated compounds for virtual screening). Taking the user-provided chemical structure as the query, the top most similar compounds in terms of 3D similarity are returned with associated pharmacology annotations. ChemMapper is designed to provide versatile services in a variety of chemogenomics, drug repurposing, polypharmacology, novel bioactive compounds identification and scaffold hopping studies. AVAILABILITY: http://lilab.ecust.edu.cn/chemmapper/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides' profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products. AVAILABILITY: http://lilab.ecust.edu.cn/ptid/. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. RESULTS: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. CONCLUSIONS: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller challenging, especially when application-level performance is considered as the measured output. Application-level performance is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address these challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on representative server workloads show that STFC outperforms popular controllers such as Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth resources under both static and dynamic workloads. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation.

Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy cost. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct two application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.

Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller more challenging, especially when response time is considered as the measured output. Response time is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address the challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on an E-Commerce benchmark show that STFC outperforms popular controllers such as Kalman filter, ARMA and adaptive PI by at least 16% and 37% under both static and dynamic workloads, respectively. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation.

Power consumption has become one of the most important design considerations for modern high density servers. To avoid system failures caused by power capacity overload or overheating, system-level power management is required. This kind of management needs to control power consumption precisely. Conventional solutions to this problem mostly rely on feedback controllers which only concern the power itself, known as black-box approaches. They may not respond to the variation of system quickly. This paper presents a gray-box strategy to design a model-predictive feedback controller based on a pre-built power model and a performance prediction model to constraint the peak power consumption of a server. In contrast to the existing strategies, this gray-box approach uses the performance events, which bring more insights of the behaviors and power consumption of a system, for the purpose of model prediction. We implemented a prototype of this controller and evaluated it using SPECweb2005 benchmark on a web server. This controller can settle the power consumption below the power cap within 2 control periods for more than 75% of the power overloading regardless of workload variations, outperforming black-box approaches. Meanwhile, the performance of application can be maximized with this controller.

Both power and performance are important issues in today's datacenters. It is hard to achieve optimization in both aspects on shared infrastructures due to system dynamics. Previous work mostly emphasized on either aspect or relied on models that were trained off-line for specific workload. In this paper, we present vPnP, a feedback control-based coordination system that provides guarantees on a service level agreement with respect to performance and a power budget in virtualized environments. This system can adapt gracefully to workload change. It consists of two self-tuning model predictors and a utility function optimizer. The predictors correlate system resource allocation to power and performance, respectively. The optimizer finds the optimal solution for a tradeoff between power and performance. Experimental results using TPC-W benchmark show vPnP can achieve different levels of tradeoff in a more flexible way than an existing two-layer feedback control approach. More importantly, vPnP shows its robustness over a variety of workloads. It reduces performance relative deviation by 17% compared with the two-layer feedback controller.

Power efficiency is an important design issue in mobile devices with limited power supplies. In this paper, we study a reward-based packet scheduling problem in wireless environments. We consider a general scenario in which a transmitter communicates with multiple receivers periodically. To guarantee timely transmission of data, each packet is associated with a delay constraint. The periodic data streams have different importance levels, power functions, and levels of data sizes. The more data a transmitter delivers, the more rewards it obtains. Our objective is to develop schemes that selectively transmit data streams of different data sizes at different transmission rates so that the system reward can be maximized under given time and energy constraints. We show that the problem is NP-hard and develop a dynamic programming algorithm for the optimal solution in pseudopolynomial time. A fast polynomial-time heuristic approach based on clustering of states in state space is presented to achieve close approximation. Simulation results demonstrate the effectiveness of the optimal solution and show that the proposed polynomial-time approach can achieve near-optimal results. Both approaches make a significant improvement over other approaches adapted from existing studies at a marginal runtime overhead.

Power efficiency is an important design issue in embedded devices with limited power supplies. In this paper, we study a reward-based packet scheduling problem in wireless environments. We consider a general scenario in which a transmitter communicates with multiple receivers periodically. To guarantee timely transmission of data, each packet is associated with a delay constraint. The periodic data streams have different importance levels, power functions, and levels of data sizes. The more data a transmitter delivers, the more rewards it obtains. Our objective is to develop schemes that selectively transmit data streams of different data sizes at different transmission rates to maximize the system reward under given time and energy constraints. We show that this problem is NP-hard and develop a dynamic programming algorithm for the optimal solution in pseudo-polynomial time. A fast polynomial-time heuristic approach is presented to achieve close approximation. Simulation results demonstrate that the proposed heuristic approach can achieve a significant improvement over other approaches adapted from existing studies at a small runtime overhead.