Showing papers in "Cluster Computing in 2016"

Big forensic data reduction: digital forensic images and electronic evidence

Abstract: An issue that continues to impact digital forensics is the increasing volume of data and the growing number of devices. One proposed method to deal with the problem of "big digital forensic data": the volume, variety, and velocity of digital forensic data, is to reduce the volume of data at either the collection stage or the processing stage. We have developed a novel approach which significantly improves on current practice, and in this paper we outline our data volume reduction process which focuses on imaging a selection of key files and data such as: registry, documents, spreadsheets, email, internet history, communications, logs, pictures, videos, and other relevant file types. When applied to test cases, a hundredfold reduction of original media volume was observed. When applied to real world cases of an Australian Law Enforcement Agency, the data volume further reduced to a small percentage of the original media volume, whilst retaining key evidential files and data. The reduction process was applied to a range of real world cases reviewed by experienced investigators and detectives and highlighted that evidential data was present in the data reduced forensic subset files. A data reduction approach is applicable in a range of areas, including: digital forensic triage, analysis, review, intelligence analysis, presentation, and archiving. In addition, the data reduction process outlined can be applied using common digital forensic hardware and software solutions available in appropriately equipped digital forensic labs without requiring additional purchase of software or hardware. The process can be applied to a wide variety of cases, such as terrorism and organised crime investigations, and the proposed data reduction process is intended to provide a capability to rapidly process data and gain an understanding of the information and/or locate key evidence or intelligence in a timely manner.

A feature selection approach to find optimal feature subsets for the network intrusion detection system

Abstract: The performance of network intrusion detection systems based on machine learning techniques in terms of accuracy and efficiency largely depends on the selected features. However, choosing the optimal subset of features from a number of commonly used features to detect network intrusion requires extensive computing resources. The number of possible feature subsets from given n features is 2$$^{n}-1$$n-1. In this paper, to tackle this problem we propose an optimal feature selection algorithm. Proposed algorithm is based on a local search algorithm, one of the representative meta-heuristic algorithms for solving computationally hard optimization problems. Particularly, the accuracy of clustering obtained by applying k-means clustering algorithm to the training data set is exploited to measure the goodness of a feature subset as a cost function. In order to evaluate the performance of our proposed algorithm, comparisons with a feature set composed of all 41 features are carried out over the NSL-KDD data set using a multi-layer perceptron.

Geographical information system parallelization for spatial big data processing: a review

Abstract: With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.

Link the remote sensing big data to the image features via wavelet transformation

Abstract: With the development of remote sensing technologies, especially the improvement of spatial, time and spectrum resolution, the volume of remote sensing data is bigger. Meanwhile, the remote sensing textures of the same ground object present different features in various temporal and spatial scales. Therefore, it is difficult to describe overall features of remote sensing big data with different time and spatial resolution. To represent big data features conveniently and intuitively compared with classical methods, we propose some texture descriptors from different sides based on wavelet transforms. These descriptors include a statistical descriptor based on statistical mean, variance, skewness, and kurtosis; a directional descriptor based on a gradient histogram; a periodical descriptor based on auto-correlation; and a low-frequency statistical descriptor based on the Gaussian mixture model. We analyze three different types of remote sensing textures and contrast the results similarities and differences in three different analysis domains to demonstrate the validity of the texture descriptors. Moreover, we select three factors representing texture distributions in the wavelet transform domain to verify that the texture descriptors could be better to classify texture types. Consequently, the texture descriptors appropriate for describe remote sensing big data overall features with simple calculation and intuitive meaning.

Energy aware resource allocation of cloud data center: review and open issues

Abstract: The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.

The big data analytics and applications of the surveillance system using video structured description technology

Abstract: Recently, the video data has very huge volume, taking one city for example, thousands of cameras are built of which each collects high-definition video over 24---48 GB every day with the rapidly growth; secondly, data collected includes variety of formats involving multimedia, images and other unstructured data; furthermore the valuable information contains in only a few frames called key frames of massive video data; and the last problem caused is how to improve the processing velocity of a large amount of original video with computers, so as to enhance the crime prediction and detection effectiveness of police and users. In this paper, we conclude a novel architecture for next generation public security system, and the "front + back" pattern is adopted to address the problems brought by the redundant construction of current public security information systems which realizes the resource consolidation of multiple IT resources, and provides unified computing and storage environment for more complex data analysis and applications such as data mining and semantic reasoning. Under the architecture, we introduce cloud computing technologies such as distributed storage and computing, data retrieval of huge and heterogeneous data, provide multiple optimized strategies to enhance the utilization of resources and efficiency of tasks. This paper also presents a novel strategy to generate a super-resolution image via multi-stage dictionaries which are trained by a cascade training process. Extensive experiments on image super-resolution validate that the proposed solution can get much better results than some state-of-the-arts ones.

Research on heterogeneous data integration model of group enterprise based on cluster computing

Abstract: Cluster, consisting of a group of computers, is to act as a whole system to provide users with computer resources. Each computer is a node of this cluster. Cluster computer refers to a system consisting of a complete set of computers connected to each other. With the rapid development of computer technology, cluster computing technique with high performance---cost ratio has been widely applied in distributed parallel computing. For the large-scale close data in group enterprise, a heterogeneous data integration model was built under cluster environment based on cluster computing, XML technology and ontology theory. Such model could provide users unified and transparent access interfaces. Based on cluster computing, the work has solved the heterogeneous data integration problems by means of Ontology and XML technology. Furthermore, good application effect has been achieved compared with traditional data integration model. Furthermore, it was proved that this model improved the computing capacity of system, with high performance---cost ratio. Thus, it is hoped to provide support for decision-making of enterprise managers.

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Abstract: Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.

An autonomic approach for resource provisioning of cloud services

Abstract: Recently, there has been a significant increase in the use of cloud-based services that are offered in software as a service (SaaS) models by SaaS providers, and irregular access of different users to these cloud services leads to fluctuations in the demand workload. It is difficult to determine the suitable amount of resources required to run cloud services in response to the varying workloads, and this may lead to undesirable states of over-provisioning and under-provisioning. In this paper, we address improvements to resource provisioning for cloud services by proposing an autonomic resource provisioning approach that is based on the concept of the control monitor-analyze-plan-execute (MAPE) loop, and we design a resource provisioning framework for cloud environments. The experimental results show that the proposed approach reduces the total cost by up to 35 %, the number of service level agreement (SLA) violations by up to 40 %, and increases the resource utilization by up to 25 % compared with the other approaches.

An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks

Abstract: The role of load balancing in wireless sensor networks is to provide a constant and reliable service. Applications with periodic data generation for wireless sensor networks require the maximum lifetime of the network. Most research imposes mainly on how to maximize the lifetime of the sensor nodes for the load balancing in order to performance and effeteness in the wireless sensor networks. Because the energy consumption is related to lifetime of the sensor nodes and the energy is a strictly limited resource in wireless sensor networks. Also, energy consumption optimization is required to synchronize the lifetime of the nodes with the whole network lifetime. For this reason, we address the lifetime maximization problem then we improves a novel load balancing scheme that balance the energy consumption of the sensor nodes and maximum network lifetime by load balancing applying the sub-network management in wireless sensor networks. Then, we propose a scheme using analytical models and compare the results with the previous researches. Our simulation result shows that the sensor nodes operate together for full network lifetime and it indicates maximum utilization of the usable energy of the wireless sensor network.

Automatic facial emotion recognition using weber local descriptor for e-Healthcare system

Abstract: For an e-Healthcare system, detecting the emotion of a patient is vital for initial assessment of the patient. This paper proposes an emotion recognition system from face using for an e-Healthcare system. For features, Weber local descriptors (WLD) are utilized. In the proposed system, a static facial image is subdivided into many blocks. A multi-scale WLD is applied to each of the blocks to obtain a WLD histogram for the image. The significant bins of the histogram are found by using Fisher discrimination ratio. These bin values represent the descriptors of the face. The face descriptors are then input to a support vector machine based classifier to recognition the emotion. Two publicly available databases are used in the experiments. Experimental results demonstrate that the proposed WLD based system achieves a very high recognition rate, where the highest recognition accuracy reaches up to 99.28 % in case of Cohn---Kanade database.

Ontology algorithm using singular value decomposition and applied in multidisciplinary

Abstract: As a popular data management and computation tool, ontology is widely used in material science, biological genetics, chemistry, biology and pharmaceuticals. It can be regarded as a dictionary or a database, and the key of various ontology applications is similarity measuring between concepts in ontology. In this paper, we propose a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of singular value decomposition method and deterministic sampling iteration. Then, the new ontology learning is applied in plant science, gene biology, bionics and physics ontologies. The data results show the high efficiency of our singular value decomposition based ontology learning algorithm for ontology similarity measuring and ontology mapping.

Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

Abstract: Ant colony optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to "real world" problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.

Continuous and transparent multimodal authentication: reviewing the state of the art

Abstract: Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner.

Game modeling and policy research on the system dynamics-based tripartite evolution for government environmental regulation

Abstract: Government regulation and policy strategies play very important roles in environmental pollution control. In this study on the evolutionary game theories and the relationship between the government, businesses, and the overall interests of society, we build two system dynamics-based tripartite evolutionary game models: a government environmental regulation-static punishment model and a dynamic punishment model. By factoring various policy strategies in the two models, including adjustments to the "Budget of pollution inspection", "Reward for no pollution discharge", "Enterprise production gain", and "Punishment coefficient" and additional combinations of the adjustment schemes; this study observes the changes in the action and the data outputs of the two models. Finally, the operation of the two models under the same policy strategy is compared and analyzed. The results show that loss of integrated social benefit and the type of punishment mechanism will significantly impact the selection of the environmental regulation strategies. However, compared with a single strategy, a combination of policy strategies could work better in promoting the environmental regulatory model to achieve an "ideal state".

Meta-operation conflict resolution for human---human interaction in collaborative feature-based CAD systems

Abstract: Conflicts resolution is one of the key issues in maintaining consistency and in supporting smooth human---human interaction for real-time collaborative systems. This paper presents a novel approach of meta-operation conflict resolution for feature-based collaborative CAD system. Although commutative replicated data type (CRDT) is an emerging technique for conflict resolution, it is not capable of resolving conflicts among meta operations for 3D CAD systems. By defining 3 types of meta operations, this work extends CRDT capability to meta operation conflict resolution from 1D to 3D applications. The paper defines the dependency, casuality, conflict and compatible relations specific for 3D collaborative CAD systems. The conflicts of feature-based operations are automatically detected by tracking topological entity changes with the assistance of a persistent data structure, topological entity structure tree ($$TES\_Tree$$TES_Tree). An efficient commutativity-based confliction combination method is proposed to preserve the design intention of each user in a transparent way and maintains the eventual consistent state of the system. The proposed methods are tested in a prototype system with case studies, time complexity analysis and correctness proof.

PHR open platform based smart health service using distributed object group framework

Abstract: As an interest in health and disease has increased, medical service has changed to prevention of disease and health care from treatment oriented service. Medical service industry is creating various services and added value for promotion of health. Aging, extension of life expectancy, increase in lifestyle and income growth have brought about a change in paradigm of medical service which led smart health to become an important issue. Smart health caused medical service for promotion of health to change into remote medical treatment that uses personal health record from medical service which has been provided by mainly large hospitals. Medical service for promotion of health has developed into u-Healthcare which monitors condition of health in everyday life. This enabled problems of time and space constraints that occur in medical service for promotion of health that requires a medical doctor to examine bio-signal related information of a patient while facing a patient to be solved. It is difficult for a remote medical treatment to care for chronic patients who require a care of lifestyle because it focuses on treating specific diseases. As a remote medical treatment does not provide innovative medical service and it only delivers general bio information on a patient to a medical doctor remotely, remote medical open platform is needed. Thus, in this paper, we proposed a PHR open platform based smart health services using the distributed object group framework. A PHR open platform based smart health system is distributed object group framework based smart health service for managing chronic diseases. When Medical WBAN sensor uses multi-channel in transmitting data, emergency data is very important in patient's life, smart health environment is built using distributed network considering importance according to data. As WBAN sensor is very different from other networks in terms of application, architecture and density of development, it is important for WBAN sensor to be combined with external network. High quality of service of integrated network as well as link connectivity should be maintained. Since automatic diagnosis function should be reinforced in order for remote diagnosis service to be provided, integration of each small unit system and model design are important. Therefore, smart health network environment that makes the most of performance of distributed network based on automation technique and distributed agent for optimum design of system is built.

Fast implementation of block ciphers and PRNGs in Maxwell GPU architecture

Abstract: GPU is widely used in various applications that require huge computational power. In this paper, we contribute to the cryptography and high performance computing research community by presenting techniques to accelerate symmetric block ciphers (AES-128, CAST-128, Camellia, SEED, IDEA, Blowfish and Threefish) in NVIDIA GTX 980 with Maxwell architecture. The proposed techniques consider various aspects of block cipher implementation in GPU, including the placement of encryption keys and T-box in memory, thread block size, cipher operating mode, parallel granularity and data copy between CPU and GPU. We proposed a new method to store the encryption keys in registers with high access speed and exchange it with other threads by using the warp shuffle operation in GPU. The block ciphers implemented in this paper operate in CTR mode, and able to achieve high encryption speed with 149 Gbps (AES-128), 143 Gbps (CAST-128), 124 Gbps (Camelia), 112 Gbps (SEED), 149 Gbps (IDEA), 111 Gbps (Blowfish) and 197 Gbps (Threefish). To the best of our knowledge, this is the first implementation of block ciphers that exploits warp shuffle, an advanced feature in NVIDIA GPU. On the other hand, block ciphers can be used as pseudorandom number generator (PRNG) when it is operating under counter mode (CTR), but the speed is usually slower compare to other PRNG using lighter operations. Hence, we attempt to modify IDEA and Blowfish in order to achieve faster PRNG generation. The modified IDEA and Blowfish manage to pass all NIST Statistical Test and TestU01 SmallCrush except the more stringent tests in TestU01 (Crush and BigCrush).

Multi-label classification algorithm research based on swarm intelligence

Abstract: Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm.

Energy aware scheduling of deadline-constrained tasks in cloud computing

Abstract: Energy efficiency is the predominant issue which troubles the modern ICT industry. The ever-increasing ICT innovations and services have exponentially added to the energy demands and this proliferated the urgency of fostering the awareness for development of energy efficiency mechanisms. But for a successful and effective accomplishment of such mechanisms, the support of underlying ICT platform is significant. Eventually, Cloud computing has gained attention and has emerged as a panacea to beat the energy consumption issues. This paper scrutinizes the importance of multicore processors, virtualization and consolidation techniques for achieving energy efficiency in Cloud computing. It proposes Green Cloud Scheduling Model (GCSM) that exploits the heterogeneity of tasks and resources with the help of a scheduler unit which allocates and schedules deadline-constrained tasks delimited to only energy conscious nodes. GCSM makes energy-aware task allocation decisions dynamically and aims to prevent performance degradation and achieves desired QoS. The evaluation and comparative analysis of the proposed model with two other techniques is done by setting up a Cloud environment. The results indicate that GCSM achieves 71 % of energy savings and high performance in terms of deadline fulfillment.

OptiSpot: minimizing application deployment cost using spot cloud resources

Abstract: The spot instance model is a virtual machine pricing scheme in which some resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose OptiSpot, a heuristic to automate application deployment decisions on cloud providers that offer the spot pricing model. In particular, with our approach it is possible to determine: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use to minimize the total cost while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.

Secure outsourcing of modular exponentiations in cloud and cluster computing

Abstract: Cloud computing and cluster computing are user-centric computing services. The shared software and hardware resources and information can be provided to the computers and other equipments according to the demands of users. A majority of services are deployed through outsourcing. Outsourcing computation allows resource-constrained clients to outsource their complex computation workloads to a powerful server which is rich of computation resources. Modular exponentiation is one of the most complex computations in public key based cryptographic schemes. It is useful to reduce the computation cost of the clients by using outsourcing computation. In this paper, we propose a novel outsourcing algorithm for modular exponentiation based on the new mathematical division under the setting of two non-colluding cloud servers. The base and the power of the outsourced data can be kept private and the efficiency is improved compared with former works.

Associative context mining for ontology-driven hidden knowledge discovery

Abstract: The modern society has been developing new paradigms in diverse fields through IT convergence based on information technique development. In the field of construction/transportation, such IT convergence has been attracting attention as a new generation technology for disaster prevention and management. Researches on disaster prevention and management are continuously being performed. However, the development of safety technology and simulation for prediction and prevention is comparatively slow. For the new generation IT convergence to efficiently secure safety and manage disaster prevention, it is more important than anything else to construct systematic disaster prevention system and information technology. In this study, we suggested the associative context mining for ontology-driven hidden knowledge discovery. Such method reasons potential new knowledge information through the association rule mining in the ontology-driven context modeling, a preexisting research, and uses the semantic reasoning engine to create and apply rules to the context simulation. The ontology knowledge base consists of internal, external, and service context information such as user profile, weather index, industry index, location information, environment information, and comprehensive disaster situation. Apriori mining algorithm of the association rule is applied to reason the potential relationship among internal, external, and service context information and discovers and applies hidden knowledge to the semantic reasoning engine. The accuracy and validity are verified through evaluating the performance of the developed ontology-driven associative context simulation. Such developed simulation is expected contribute to enhancing public safety and quality of life through determining potential risk involved in disaster prevention and quick response.

MrHeter: improving MapReduce performance in heterogeneous environments

Abstract: As GPUs, ARM CPUs and even FPGAs are widely used in modern computing, a data center gradually develops towards the heterogeneous clusters. However, many well-known programming models such as MapReduce are designed for homogeneous clusters and have poor performance in heterogeneous environments. In this paper, we reconsider the problem and make four contributions: (1) We analyse the causes of MapReduce poor performance in heterogeneous clusters, and the most important one is unreasonable task allocation between nodes with different computing ability. (2) Based on this, we propose MrHeter, which separates MapReduce process into map-shuffle stage and reduce stage, then constructs optimization model separately for them and gets different task allocation $$ml_{ij}, mr_{ij}, r_{ij}$$mlij,mrij,rij for heterogeneous nodes based on computing ability.(3) In order to make it suitable for dynamic execution, we propose D-MrHeter, which includes monitor and feedback mechanism. (4) Finally, we prove that MrHeter and D-MrHeter can greatly decrease total execution time of MapReduce from 30 to 70 % in heterogeneous cluster comparing with original Hadoop, having better performance especially in the condition of heavy-workload and large-difference between nodes computing ability.

Elastic resource provisioning for scientific workflow scheduling in cloud under budget and deadline constraints

Abstract: With the popularization and development of cloud computing, lots of scientific computing applications are conducted in cloud environments. However, current application scenario of scientific computing is also becoming increasingly dynamic and complicated, such as unpredictable submission times of jobs, different priorities of jobs, deadlines and budget constraints of executing jobs. Thus, how to perform scientific computing efficiently in cloud has become an urgent problem. To address this problem, we design an elastic resource provisioning and task scheduling mechanism to perform scientific workflow jobs in cloud. The goal of this mechanism is to complete as many high-priority workflow jobs as possible under budget and deadline constraints. This mechanism consists of four steps: job preprocessing, job admission control, elastic resource provisioning and task scheduling. We perform the evaluation with four kinds of real scientific workflow jobs under different budget constraints. We also consider the uncertainties of task runtime estimations, provisioning delays, and failures in evaluation. The results show that in most cases our mechanism achieves a better performance than other mechanisms. In addition, the uncertainties of task runtime estimations, VM provisioning delays, and task failures do not have major impact on the mechanism's performance.

Distributed denial-of-service attacks against HTTP/2 services

Abstract: HTTP/2 is the second major version of the HTTP protocol published by the internet engineering steering group. The protocol is designed to improve reliability and performance Such enhancements have thus delineated the protocol as being more vulnerable to distributed denial-of-service (DDoS) attacks when compared to its predecessor. Recent phenomenon showed that legitimate traffic or flash crowds could have high-traffic flow characteristics as seen in DDoS attacks. In this paper, we demonstrate that legitimate HTTP/2 flash crowd traffic can be launched to cause denial of service. To the best of our knowledge, no previous study has been conducted to analyse the effect of both DDoS as well as flash crowd traffic against HTTP/2 services. Results obtained prove the effect of such attacks when tested under four varying protocol-dependant attack scenarios.

Life style improvement mobile service for high risk chronic disease based on PHR platform

Abstract: As IT convergence technique develops, medical technology and apparatus are being modernized opening the era that we can obtain variable information easily anywhere, anytime thanks to wireless communication developed, further. These social changes enabled us to obtain information related to health more efficiently. Modern society is rapidly aging and more people experience chronic diseases because of their wrong eating habit, obesity and insufficient exercise. Thus a demand for health improvement and management at a certain term is increasing rather than complete therapy. Previously, major medical institutions managed personal medical history regarding patients mainly in health management but it is not changing its method to self-utilization and management by individual patient as of now along with medical institutions as fusion technology develops, and individual health record information can easily be checked anywhere, anytime through personal health record (PHR) platform. Unlike developing speed of related technology, however, there is a limitation in expansion, development of individual health record service, personal information security currently. In this paper, we propose mobile service regarding life style improvement targeting high risk chronic diseases based on PHR platform. PHR platform determines high blood pressure, diabetes, hyperlipidemia diseases which are three main chronic diseases using users' data and can monitor chronic diseases in portable mobile device. Also, the service provides by organically, mutually connected form through feedback towards input from health states of users in mobile device. By proposing contents about service based on efficient individual health record through mobile device that maximized transportability based on PHR platform, proposed method will contribute to industry development and activation of application service development of individual health record. Increase in consistency and reliability through standardization of afterwards health management service is expected to contribute to reduction in social cost and improvement of national health being the basis to realize communication activation of health record between medical institutions, efficient management and education of patients, reduction in dual examinations.

Pareto-based multi-objective optimization for classification in data mining

Abstract: This paper explores the possibility of classification based on Pareto multi-objective optimization. The efforts on solving optimization problems using the Pareto-based MOO methodology have gained increasing impetus on comparison of selected constraints. Moreover we have different types of classification problem based on optimization model like single objective optimization, MOO, Pareto optimization and convex optimization. All above techniques fail to generate distinguished class/subclass from existing class based on sensitive data. However, in this regard Pareto-based MOO approach is more powerful and effective in addressing various data mining tasks such as clustering, feature selection, classification, and knowledge extraction. The primary contribution of this paper is to solve such noble classification problem. Our work provides an overview of the existing research on MOO and contribution of Pareto based MOO focusing on classification. Particularly, the entire work deals with association of sub-features for noble classification. Moreover potentially interesting sub-features in MOO for classification are used to strengthen the concept of Pareto based MOO. Experiment has been carried out to validate the theory with different real world data sets which are more sensitive in nature. Finally, experimental results provide effectiveness of the proposed method using sensitive data.

Consistency maintenance of Do and Undo/Redo operations in real-time collaborative bitmap editing systems

Abstract: In real-time collaborative graphical editing systems, bitmap-based graphical editing systems are particularly special and practically useful ones, and Do and Undo/Redo operations are intricate problems in this field. However, existing researches on graphical editing systems are quite scanty. In this paper, based on Multi-version strategy, we propose a new approach to solve the Do and Undo/Redo consistency maintenance problems with due consideration of three possible cases: all-causal, all-independent and causal-independent-mixed operations. Compared with previous collaborative algorithms, the algorithms proposed in this paper support Do and Undo/Redo operations without requiring additional space. In addition, two example analyses are also given to prove the algorithms' effectiveness separately. Furthermore, the time complexity of the two algorithms is both O(n). Finally, a system prototype called bitmap-based Co-Graphical Editor is implemented to verify them realistically.

Teachers' views on the use of robots and cloud services in education for sustainable development

Abstract: Various studies have shown the educational use of robots to be effective in science and mathematics education. However, such studies have not considered the psychological factors affecting users of the new technology, only external factors, such as the range of affordable robotic platforms and ready-for-lesson materials for a robot-assisted learning environment. It is necessary to extend the use of robots and cloud platforms to support education for sustainable development. To that end, this study first assessed the possibility of using robots in education for sustainable development by providing them to children from low-income families, since they often show abnormal behaviors and have few opportunities to access robots in education. The long-term changes in their behavior resulting from this outreach program were examined. Qualitative as well as quantitative methods were used to evaluate and discuss the changes in self-efficacy and learning attitudes of students during the year. Second, we proposed a technology acceptance model, termed RSAM, for teachers in robot-assisted learning environments with a cloud service platform. Acceptance factors were estimated using a weighted average method based on teacher focus group interviews. The challenges associated with robot-assisted learning considering cloud services are discussed.