International Journal of High Performance Systems Architecture (22 papers in press)
A Soft Error Tolerant Register File for Highly Reliable Microprocessor Design
by Nastaran Rajaei, Ramin Rajaei, Mahmoud Tabandeh
Abstract: Dealing with radiation-induced soft errors is of the main design challenges in todays nanometer design of embedded systems especially in safety critical applications. Register file is a vulnerable section of a microprocessor that needs to be protected against soft errors. This paper proposes a soft error tolerant structure for the register file of the safety-critical embedded processors. In this structure, the double modular redundancy (DMR) technique based on a new hardware implementation is employed for the normal values. Moreover, the unused bits of the registers are used to be further redundant for the used ones for the narrow-width values. We show that the proposed structure offers much more reliability improvement in comparison with the conventional techniques for protection of register files such as DMR, triple modular redundancy and error detection and correction solutions based on Hamming code.
Keywords: Double Modular Redundancy (DMR); Triple Modular Redundancy (TMR); Register File; Single Event Upset (SEU); Soft Error.
An exclusive cache replacement policy based on read priority and dynamic sliding
by Debabala Swain, Banchhanidhi Dash
Abstract: The conventional cache replacement algorithms have massive hardware cost with ambiguous logic and measurability. Increasing the cache levels do not give a better solution for the hardware complexity and performance issues in multi-core processors.Rather number of read misses on executing the complex memory intensive program can increase the execution time in multi-core processors. This paper proposes a new way of cache replacement policy Weight based Read Priority Replacement (WRPR), which works on the read priority of a cache line in all levels of cache. By making it read prior instead of write, the cache lines with more read access are highly weighted. During replacement, the cache eviction is done from dynamic logical cache regions based on its weight. The algorithm performance is tested using multi-core cache simulator with different benchmark workloads in the SMPCache simulator. The proposed replacement policy can work on any exclusive cache level in a multi-level hierarchy. It shows an improved performance from the hit rate context.
Keywords: Exclusive cache; read priority; dynamic sliding; multi-core; multi-level hierarchy; WRPR.
Runtime Power-Aware Energy Saving Scheme for Parallel Applications
by Vaibhav Sundriyal, Masha Sosonkina
Abstract: Energy consumption has become a major design constraint in modern computing
systems. With the advent of petaflops architectures, power-efficient software
stacks have become imperative for scalability. Modern processors provide techniques, such as dynamic voltage and frequency scaling (DVFS),
to improve energy efficiency on-the-fly. Without careful application, however, DVFS and throttling may cause significant performance loss due to the system overhead. Typically, these techniques are used by constraining a priori the application performance loss, under which the energy savings are sought. This paper discusses potential drawbacks of such usage and proposes an energy-saving scheme that takes into account the instantaneous processor power consumption as presented by
the ``running average power limit'' (RAPL) technology from Intel. Thus, the need for the user to predefine a performance loss tolerance is avoided.
Experiments, performed on NAS parallel benchmarks and large-scale linear
system solvers from the pARMS package, show that the proposed
scheme saves more energy than the approaches based on the predefined performance
Keywords: Dynamic Voltage and Frequency Scaling (DVFS);Energy Modeling;Workload Modeling; Intel RAPL; NAS Parallel Benchmarks.
Research and application of an uncertain genetic neural network in landslide hazard prediction
by Yimin Mao, Jiawei Wang, Xinrong Lu, Dinghui Mao, Xiaodong Gao, Tian Liang
Abstract: Due to difficulties in obtaining and effectively processing rainfall and other uncertain factors in landslide hazard prediction, as well as the existence of local minima and the slow training speed of the standard back-propagation algorithm, a prediction method based on an uncertain genetic neural network in order to improve the hazard prediction accuracy has been proposed. The method is founded on an optimized genetic algorithm and the back-propagation neural network classification algorithm. Briefly, combining the prediction theory of landslide disaster with rainfall and other uncertainties associated with landslides, we propose the concept of separation of uncertain data, elaborate the processing methods of uncertain property data, and build the uncertain genetic neural network and a landslide hazard prediction model. The experiment conducted in the Baota district of Yan'an showed that the effective and overall accuracies of the method are 92.1% and 86.7%, respectively, and prove the feasibility of an uncertainty genetic neural network algorithm in landslide hazard prediction.
Keywords: Uncertain data; Landslide; Genetic algorithm; Back-propagation neural network; Hazard prediction.
Big Data Analytics in the Context of Internet of Things and the Emergence of Real-Time Systems: A Systematic Literature Review
by Tahereh Saheb
Abstract: The way modern enterprises and industries are behaving has been disrupted with the advent of IoT devices generating massive amounts of real-time event and stream data at a very high pace, called Big Data. This paper is a systematic review of the papers on the field of IoT Big Data Analytics (IoT BDA) with a concentration on real-time feature of the IoT systems. This paper shows that IoT BDA have challenged the relational databases in various forms, such as in terms of their flexibility, anomaly detection, real-time response, next-generation of hardware-software installment, and interoperability of multitude systems. This paper also explores the new feature of IoT BDA: real-time analysis of events and streams and explores real-world applications of these new formats of analysis in order to explore various forms of insight generated by predictive and real-time analysis of IoT big data. This paper also reviews challenges of security, privacy and interoperability within an IoT BDA system. This paper also reviews IoT BDA platforms, new and advanced analytical methods, and new system architectures and frameworks that are designed and developed by the papers. This paper explores two main application of a mobile sensor and app in an IoT BDA system: the ECG real-time monitoring and the real time tracking of things and dangerous behaviors. This paper triggers broader discussion regarding future research agenda in the field of real time analysis of IoT Big Data both in practice and in theory.
Keywords: big data analytics; internet of things; real time analysis; streaming analysis.
The Construction of Winter Wheat Smart Water Saving Irrigation System Based on Big Data and Internet of Things
by Peng Zhang, Qian Zhang, Fusheng Liu, Ning Cao, Changqing Song, Junqing Li, Cuinan Yang, Russell Higgs, Gregory M.P. O'Hare, Lina Xu
Abstract: The advent of emerging big data and Internet of Things, a large number of agriculture data such as the seeding condition, soil moisture, fertilizer, water, pests and meteorology , which could be effectively analyzed to guide the smart agriculture. In this paper the winter wheat smart water saving irrigation system is established based on technologies including big data and Internet of Things. Through the automatic monitor system of winter wheat growing environment, the big data center intelligently stores, screens, calibrates, mines and extracts the monitoring data, a system of winter wheat irrigation and fertilization decision-making based on big data was built. According to the testing data and the weather forecast data, this system can forecast the water requirement of winter wheat in different growth periods and make decisions on automatic irrigation and fertilization, leading to the timely and proper irrigation of crops.
Keywords: smart agriculture; big data; Internet of Things; smart water saving irrigation.
A Novel Service Discovery Algorithm Via Service Repository Center in Internet of Things
by Bing Jia, Tao Zhou, Chuxuan Zhang
Abstract: In recent years, the Internet of Things (IoT) has attracted more and more attention, and IoT services has increased rapidly. The service discovery which has important implications for the service integration of IoT services has become more difficult in large-scale registration. In traditional matching method, in order to get a better match results, all the matching parameters for services had to be calculated together, thus it would waste a lot of computing resources and computing time. This paper propose a centralize four-layer model for service discovery via multi-stage semantic service matching to improve the performance of service discovery, namely interactive interface layer, parsing annotation layer, service matching layer and data semantic layer, so as to meet the requirement of service discovery in large-scale IoT services. We also propose a hybrid service matching degree measurement by synthetically calculating the concept logic and semantic similarity for each layer singly. Experimental analysis confirms the effectiveness of the proposed mechanism in improving the precision and recall.
Keywords: service discovery; service matching; IoT service; semantic similarity; service repository center.
Soft Skills Requirements in Mobile Applications Development Employment Market
by JIngdong Jia, Zupeng Chen, Xi Liu
Abstract: The soft skills of developers have a major influence on the quality of software product and project. However, which soft skills are important for mobile applications development remains unknown. Additionally, it is necessary to examine the differences of soft skills requirements between traditional software and mobile applications development. In this article, based on text mining including word segmentation, similarity calculation and clustering analysis, we analyse lots of advertisements, and extract 13 categories of soft skills requirements for mobile applications development. We also compare the categories with those for traditional software development. We find that communication and teamwork are still the most important two soft skills. However, fast learning is more important for mobile developers, and we identified four soft skills that are not proposed before. Additionally, season has a minor impact on soft skills requirements of mobile applications development.
Keywords: soft skill; mobile application development; job advertisement; text mining; cluster analysis.
Energy Optimized Cryptography (EOC)for Low Power Devices in Internet of Things
by RAJESH G, Vamsi Krishna C, Christopher Selvaraj B, Roshan Karthik S, Arun Kumar Sangaiah
Abstract: Internet of Things(IoT) has a plethora of devices ranging from high capacity servers to low powered devices that works with Bluetooth, ZigBee, GPRS, RFID and WiFi etc,. These the low power devices are constrained to security, power management, reliability and privacy limitations. The existing traditional security algorithms could not be applied to these low power devices, due tothe high processing and battery power requirements. Here proposed an Energy Optimized Cryptography (EOC) for low power devices in IoT. Here the security of the low power devices are providedby two light weight security techniques called R2CV, a sub key generation method and Optimized Message Authentication Code Generation Function (OMGF) tomaintain security without compromising energy and processing power consumption. The proposed security algorithms reduce the computational requirements for sub key generation and MAC generation in low power devices. The experimental results are compared with the existing security algorithms like RC5 and SHA, and is proven that R2CV and OMGF reduce the time consumed, increase battery life and in turn it extends the network life time.
Keywords: IoT Security; low-power devices; Message authentication code; Energy efficiency; Internet of Things.
Special Issue on: Internet of Things Principles, Methodologies, and Applications
Analysis of WSN Routing Protocols for the Application of Forest Fire Prevention
by Ning Cao, Yuanyuan Zhang, Guofu Li, Xiujuan Fei, Hua Yu, Mei Wu, Shaohua Cao, Bin Xue
Abstract: Forests are precious and indispensable natural resource. Using wireless sensor networks (WSN) to establish forest fire prevention system can effectively reduce loss of human and economy. However, monitoring a very large and complex area such as the entire forest by WSN would entail a large number of sensor nodes, which are hard to be replaced or recharged. Such working conditions and constrains give rise to the necessity to make careful design on network protocol for the WSN. In this paper, we compare and analyse several key parameters for the location-based routing protocols and hierarchical routing protocols. This research will assist the users to design or select the best routing protocols for their applications.
Keywords: Wireless Sensor Network; Routing Protocols; Network Lifetime
A New Ontology Ranking Method with OntoDUIA for Ontology Retrieval System
by Jianghua Li, Boyu Li
Abstract: Ontology is being widely used for data integration and knowledge discovery in the field of data engineering. Ontology ranking is one of the important function of ontology search engine, which ranks the searched ontologies in a reasonable order. A good ranking method can help a user acquiring the satisfied ontology efficiently from a considerable amount of search results. However, existing methods in literatures are unable to rank ontologies to well meet users ranking demand due to their inherent shortcomings. In this paper, a novel ontology ranking method OntoDUIA is proposed and presented. It evaluates user-query related ontologies and ranks them based on a set of metrics of query relevance, usability, instance distribution and authority of ontology. To evaluate OntoDUIA, a series of experiments are conducted to compare the performance among OntoDUIA and some existing ontology ranking methods as well as human experts. Experimental results show that OntoDUIA can effectively meet users ontology ranking demand, and it achieves stable and reliable ranking results. Finally, OntoDUIA can also be applied to ontology retrieval system.
Keywords: Ontology ranking; Relevance metrics; Usability quality metrics; Instance metrics.
Cloud Computing Resources Scheduling Optimization based on Improved Bat Algorithm via Wavelet Perturbations
by Yan Zhang, Zhaobin Liu, Fahong Yu
Abstract: Efficient scheduling of computing resources is a fundamental issue that the cloud computing needs to solve, which involves highly challenging load-balance of multiple computing resources. In order to achieve optimal balance between the executing speed, the average response time and the overall system utilization during cloud computings resource allocation, a cloud computing resources scheduling optimization algorithm is proposed based on Wavelet-Perturbation-based Bat algorithm (WPBA). The algorithm first employs wavelet perturbation to enhance Bat algorithms performance followed by population-entropy-guided substitution to control diversity and improve the converging speed and accuracy. Then it adopts WPBA to achieve resources scheduling optimization of the cloud computing. The experiment shows that using WPBA has significantly improved the overall performance of the algorithm and has also remarkably optimized the resource scheduling capability of cloud computing and heightened the overall resource utilization.
Keywords: Cloud computing; Resource dispatch; Swarm intelligence; Improved bat algorithm.
Smart Generic Cabling and Network Planning in Office Network
by Banghai Xu
Abstract: Smart city intelligently responses to various requirements including environmental protection, public security, urban services, commercial and industrial activities etc. Smart city deals with lots of key information in the whole city by utilizing modern technologies like communication technology. Enterprise network is an enormous and complex system belonging to smart city. It not only provides basic platforms of office automation but also various application services, where information can be delivered to other systems. In this paper, we design a generic cabling according to the requirement of enterprise networks. Our generic cabling is well extensible and compatible. In addition, we also present the network planning for enterprise network such that it provides good service for the whole enterprise.
Keywords: Generic Cabling; Network Planning; Smart City; Enterprise Network.
SparkCUDE: a Spark-based differential evolution for large-scale global optimization
by Hu Peng, Changshou Deng, Shunxu Peng
Abstract: Differential evolution (DE) is one of the efficient evolutionary algorithms over larger-scale global optimization problems. Recently, the new cloud computing models (such as Spark) have drawn attentions to deal with larger-scale global optimization problems. Spark provides effective support for iterative algorithms. However, we have noted that simultaneous combination of the excellent DE variant and the improved Spark computing model to enhance the optimization performance and reduce the computation times has not exploited. In this paper, we propose a Spark-based DE algorithm for larger-scale global optimization problems, called SparkCUDE, in which the Spark computation model with ring topology is introduced and the CUDE algorithm is employed as the internal optimizer. The original CUDE was proposed in our previous work, in which uniform local search enhances exploitation ability and the commensal learning is proposed to adaptively select optimal mutation strategy and parameter setting simultaneously under the same criteria. Experimental studies are conducted on the benchmark functions of CEC2010 on large-scale global optimization. Comprehensive experiments demonstrate the effectiveness and efficiency of the proposed approach.
Keywords: Differential evolution; Large-Scale global optimization; Spark; Migration strategy.
A New Quantum Rotation Angle of Quantum-Inspired Evolutionary Algorithm for TSP
by Jialin Li, Wei Li, Ying Huang
Abstract: In this paper, Quantum Rotate Gate is improved, which is the main operation in the Population Update of the traditional Quantum Evolutionary Algorithm. A new rotation angle is defined, preventing the algorithm from easily falling into local optimum state in the middle and late term. Based on the characteristics of TSP, a modified quantum rotate gate is proposed in this paper to adaptively adjust the rotation angle, according to the evolution generations and the adapt to degree of the value to adaptive dynamic adjustment of the rotation angle, resulting a better global search capability. At the same time, in order to prevent the extramalization of the probability amplitudes and falling into local optimal algorithm, this paper adopted the H ε gate on the probability amplitude of the rotation to make the corrective manipulation. The comparative experimental results showed that the algorithms stability and accuracy have been greatly improved in solving the TSP problem, compared with the conventional quantum evolutionary algorithm.
Keywords: Quantum-Inspired Evolutionary; Quantum Rotate Gate; Adaptive Quantum Rotation Angle; Traveling Salesman Problem.
Invulnerability Analysis in Intelligent Transportation System
by Fujun Han
Abstract: In recent years, the amount of population and cars increase dramatically due to the rapid development of economy. Therefore traffic congestion becomes an urgent problem in urban transport system. Intelligent transportation systems try to solve these traffic congestion problems by utilizing technologies like information, sensing, controlling and computing etc. This paper constructs a complex network of transportation system, including 73 traffic bus routes and 1003 stations. We analyze the features of public transport system towards the view of complex network theory. The results show that our network model has most features of complex networks. Finally, we compare the robustness of transportation system under fixed attacks and random attacks. Simulation results show that it has better robustness under random attacks.
Keywords: urban public transport system; complex networks; invulnerability.
Special Issue on: Data Streams Mining and Processing Methodologies, Architectures, and Applications
Analysis of Physico-chemical Variables and their Influence on Water Quality of the Bogota River using Data Mining
by Jairo Rojas, Julian Forero, Paulo Gaona, Carlos Montengro Marin, Ruben Gonzalez Crespo
Abstract: Variation of the flow rate and the concentration of different elements within a flow of water in a river must be important factors for the discovery of patterns of behavior and predictive in terms of space and time models. Based on this, this article presents data analysis carried out based on a historical compendium of measurements on the Bogota River between the years 2008-2015, given in the results of the campaigns of monitoring provided by the Regional Autonomous Corporation CAR, through the scan tool Weka and data the use of J48 algorithm for the generation of decision trees in order to establish the influence of the physical and chemical variables in the water quality of this source, within a process of identification and interpretation at the environmental level of these factors.
Keywords: Water Quality Indicator; WQI; Data Analysis;.
Research on data mining technology for the connotation and measurement of uncertainty for reassembly dimensions
by Conghu Liu, Kang He
Abstract: The uncertainty of remanufactured parts is a key factor in the stability of remanufacturing systems. Therefore, the purpose of this paper is to identify these uncertainties and measure them to improve the optimization management level of remanufacturing production process. Contrasting the ideal dimensional accuracy, manufactured dimensional accuracy and remanufactured dimensional accuracy, we analyses connotation of uncertainty for reassembly dimensions. Then, we constructs the uncertainty measurement model for reassembly dimensions to realize quantitative measurement by entropy. So the coupling mechanism of uncertainty for reassembly dimensions is studied, and the corollary is in conformity with the reality. It can use data mining technology to optimize remanufacturing process management. Finally, the feasibility and effectiveness of the model are verified in grading selection of remanufacturing enterprise parts. This research provides support for the uncertain optimization decision for lean remanufacturing from both theoretical and practical aspects by uncertain data mining techniques.
Keywords: remanufacturing; data mining; uncertainty; entropy.
Extending the Common Information Model for Smart Grids operational computations based on bus-branch models
by Mariacristina Gallo, Antonio Celotto, Massimo De Falco, Alfredo Vaccaro
Abstract: In the modern power systems, known as Smart Grids (SGs), the heterogeneity of elements makes their interoperability difficult. The goal is to integrate the different types of elements, to build a common remote control system which allows the interaction between the different parts. In this sense, the International Electrotechnical Commission (IEC) has introduced some standards (i.e., IEC 61970, IEC 61968, CIM, SCL), aimed at defining a common language for the communication among the different elements of a power system. However, without a harmonization of these standards, the development and implementation of systems and applications will result in a noticeable amount of single engineering design schemes. An ontology-based approach could collect knowledge from different applications, bridging the gap of harmonization among such models. This paper deals with the implementation of a CIM ontology based on a bus-branch model by adopting the Ontology Development Cycle (ODC) process and that aims to support the power system state estimation problem. A bus-branch model is a logical representation of the connections among the elements of the grid that can also support analysis of its data stream.
Resulting ontology has been instantiated by using a case study on a real power flow problem, and evaluated by applying some well-known metrics.
Keywords: Ontology; Common Information Model (CIM); Bus-branch model; Smart Grid; State Estimation.
Resource Scheduling Optimization Algorithm for Containerized Microservice Architecture in Cloud Computing
by Peng Li, Jinquan Song, He XU, Yang Zhou
Abstract: Currently, the containerized microservice architecture has aroused great concern. The single application is developed as a suite of small services to facilitate the application deployment, expansion and management. The traditional scheduling of microservice tends to focus on the load balancing of cluster, ignoring the quality of service(QoS). Therefore, this paper proposes a prediction model of component relevance, by adopting the optimized artificial bee colony algorithm (OABC) on the containerized microservice scheduling. Different assessment strategies are adopted according to the differences in the correlation among components. Two-point crossover operator is introduced to improve the exploration ability of the algorithm. The mutation operator is added to enhance the local search ability, and the mutation probability is set to the dynamic value which varies with the number of iterations to speed up the convergence of the algorithm. The experimental results show that the OABC is preferable to the artificial bee colony algorithm (ABC) and the greedy algorithm as to the cluster load balancing and service response time aspects.
Keywords: Cloud Computing; Microservice; Container; Load Balancing; Artificial Bee Colony; Quality of Service.
Dynamic Bayesian Network Threat Assessment for Warship Formation: a Data Analysis Method
by Haiwen SUN
Abstract: In the target threat assessment of maritime formation air defense, the observation data are easy to be missing, and existing data analysis methods are difficult to carry out dynamic assessment in time series. In order to solve these problems, a data analysis method about threat assessment is proposed, which is based on Discrete Dynamic Bayesian Networks (DDBN) and the utility theory. Firstly, the data characteristics of the target threat assessment are analyzed, and a two-stage dynamic Bayesian network structure evaluation system is constructed. Secondly, the continuous variable in the network structure is transformed into a discrete variable, which can avoid the repeated calculation caused by the continuous change of the node threat attribute value in a small range. Then, the prior probability of the credibility of the uncertainty node to make the Bayesian network parameters more realistic, and the utility theory is introduced to carry out the threat ranking. Finally, the simulation results show that the data analysis method is in good agreement with the artificial judgment. This proposed method has a certain practical significance, which realizes the data processing of dynamic threat assessment.
Keywords: DDBN; Data analysis; Discrete variable; Credibility; Utility theory; Threat assessment;.
Functional Encryption with Efficient Verifiable Outsourced Decryption for Secure Data Acess Control in Social Network
by Li Cong, Yang Xiaoyuan, Liu Yudong, Cao Yunfei
Abstract: For social network, the uer's data streams needs to be securely shared. Attribute-based functional encryption (ABFE) implements fine-grained access control for sensitive data and implements different functional encryption systems with multiple access policies. Attribute-based functional encryption, as a new type of encryption scheme, whose user private key and ciphertext are associated with attributes, is very suitable for data security sharing and fine-grained access control in social network environments. However, the main disadvantages of the attribute-based functional encryption are that the size and decryption time of the ciphertext increase in the complexity of the access formula. In order to reduce the burden of decryption for users, this paper proposed to outsource the computation of functional encryption to cloud server, by providing a transformation key, we show how a user can provide a single transformation key to the cloud, which enables the cloud to convert any ABE ciphertext into a (constant-size) ElGamal-style ciphertext, at the same time through the efficient verification to ensure the correctness of the outsourcing computing. It saves a lot of bandwidth and decryption time for users without increasing the number of transfers data streams.
Keywords: Functional Encryption; Outsourced Decription; Verifiability; ABE; Cloud Computing; Social Network.