A new model of self-organizing neural networks and its applicationin data projection

In this paper a new model of self-organizing neural networks is proposed. An algorithm called "double self-organizing feature map" (DSOM) algorithm is developed to train the novel model. By the DSOM algorithm the network will adaptively adjust its network structure during the learning phase so as to make neurons responding to similar stimulus have similar weight vectors and spatially move nearer to each other at the same time. The final network structure allows us to visualize high-dimensional data as a two dimensional scatter plot. The resulting representations allow a straightforward analysis of the inherent structure of clusters within the input data. One high-dimensional data set is used to test the effectiveness of the proposed neural networks.     

A hybrid list-based task scheduling scheme for heterogeneous computing

The Journal of Supercomputing - Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task...     

L-overlay: A layered data management scheme for peer-to-peer computing

Efficient storage and handling of data stored in a peer-to-peer (P2P) network, proves vital for various applications such as query processing and data mining. This paper presents a distributed, scalable and robust layered overlay (L-overlay) to index and manage multidimensional data in a dynamic P2P network. The proposed method distinguishes between the data and peer layers, with efficient mapping between the two. The data is organized such that semantically similar data objects are accessed hastily. Grid and tree structures are proposed for the peer layer. As application examples of L-overlay in query processing and data mining, k-nearest neighbors query processing and distributed Naïve Bayes classification algorithms, are proposed. We show the effectiveness of our scheme in static and dynamic environments using simulation. L-overlay is shown to be more efficient than SSW, an available semantic overly, in terms of maintenance and query processing costs.     

A heterogeneous computing system for data mining workflows in multi-agent environments

Abstract: The computing-intensive data mining (DM) process calls for the support of a heterogeneous computing system, which consists of multiple computers with different configurations connected by a high-speed large-area network for increased computational power and resources. The DM process can be described as a multi-phase pipeline process, and in each phase there could be many optional methods. This makes the workflow for DM very complex and it can be modeled only by a directed acyclic graph (DAG). A heterogeneous computing system needs an effective and efficient scheduling framework, which orchestrates all the computing hardware to perform multiple competitive DM workflows. Motivated by the need for a practical solution of the scheduling problem for the DM workflow, this paper proposes a dynamic DAG scheduling algorithm according to the characteristics of an execution time estimation model for DM jobs. Based on an approximate estimation of job execution time, this algorithm first maps DM jobs to machines in a decentralized and diligent (defined in this paper) manner. Then the performance of this initial mapping can be improved through job migrations when necessary. The scheduling heuristic used considers the factors of both the minimal completion time criterion and the critical path in a DAG. We implement this system in an established multi-agent system environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. The system evaluation and its usage in oil well logging analysis are also discussed.     

A verifiable data integrity scheme for distributed data sharing in fog computing architecture

Fog computing is a promising computing paradigm that brings computing resources close to end users at the edge of the network. Hence, it handles large-scale, geographically distributed, and latency-sensitive services. However, there are several security challenges that must be addressed due to the unreliable nature of this architecture. One can cite the verification of data integrity among the most critical issues in the context of fog computing. In fact, since data is often stored dynamically in a fully distributed manner, traditional solutions based on a centralized third-party auditor for integrity verification become unsuitable for such highly dynamic and distributed contexts. Indeed, the constant transfer of data to and from the central auditor results in high network latency and potential bottlenecks.Therefore, in this paper, we propose a new efficient public verification protocol that ensures the integrity of the data in fog computing architecture. Our solution protects data integrity and authenticity using the short integer solution problem (SIS) and identity-based signatures. Moreover, in order to legitimately modify the data, our protocol allows to distributively identify the data owners and to delegate their signatures to other entities in the architecture. Furthermore, it enables effective data integrity verification even when data is separately shared across several servers. This verification can be performed by any legitimate end user connected to the architecture, and without relying on any trusted third party. Finally, we prove that our protocol is highly efficient and outperforms existing solutions, as demonstrated by our extensive simulations and thorough security analysis that confirmed its security.     

A parametric model for fusing heterogeneous fuzzy data

Presented is a model that integrates three data types (numbers, intervals, and linguistic assessments). Data of these three types come from a variety of sensors. One objective of sensor-fusion models is to provide a common framework for data integration, processing, and interpretation. That is what our model does. We use a small set of artificial data to illustrate how problems as diverse as feature analysis, clustering, cluster validity, and prototype classifier design can all be formulated and attacked with standard methods once the data are converted to the generalized coordinates of our model. The effects of reparameterization on computational outputs are discussed. Numerical examples illustrate that the proposed model affords a natural way to approach problems which involve mixed data types     

异构云环境下基于分簇的云资源感知任务调度方案

针对提高异构云平台中资源调度的效率,提出了一种基于任务和资源分簇的异构云计算平台任务调度方案。利用K-means算法,根据任务的CPU和I/O处理时间对任务分簇,根据资源的计算能力对资源分簇;然后,将任务簇对应到合适的资源簇,并利用最早截止时间优先（EDF）算法对任务簇中的独立任务进行调度,利用提出的改进型最小关键路径（MCP）算法对依赖性任务进行调度。实验结果表明,在资源异构的云计算环境中,该方案执行任务时间短、能耗低。     

移动云计算环境中基于代理的可验证数据存储方案

现有云计算可验证数据存储协议无法直接应用于终端存储和计算能力有限的移动计算环境。针对该问题,提出移动计算环境下基于代理的可验证云存储协议,在终端和云服务器之间引入一个半可信的安全计算代理,利用代理来帮助移动终端用户完成计算密集的操作,从而使得可验证数据存储方案可用于移动计算环境。提出一个具体的可验证数据存储协议,形式化证明了所提协议满足随机预言机模型下的选择明文攻击(CPA)安全,量化分析结果表明协议设计适用于移动计算环境,符合设计目标。     

异构数据集成交换方案及其在ASP平台中的应用

ASP模式对中小型企业来说是促进信息化管理的理想方式,而ASP模式的采用往往要对异构数据库中的数据进行交互访问.首先给出了相关概念的定义,并针对企业实际应用中的异构数据问题,提出了一种基于XML的异构数据集成交换方案.然后结合应用对关系数据库与XML文档之间的映射、转化和存储等相关技术进行了详细介绍.最后介绍了该方案在“面向服装纺织行业的应用服务托管平台”中的应用.     

An efficient scheduling scheme using estimated execution time for heterogeneous computing systems

Computing systems should be designed to exploit parallelism in order to improve performance. In general, a GPU (Graphics Processing Unit) can provide more parallelism than a CPU (Central Processing Unit), resulting in the wide usage of heterogeneous computing systems that utilize both the CPU and the GPU together. In the heterogeneous computing systems, the efficiency of the scheduling scheme, which selects the device to execute the application between the CPU and the GPU, is one of the most critical factors in determining the performance. This paper proposes a dynamic scheduling scheme for the selection of the device between the CPU and the GPU to execute the application based on the estimated-execution-time information. The proposed scheduling scheme enables the selection between the CPU and the GPU to minimize the completion time, resulting in a better system performance, even though it requires the training period to collect the execution history. According to our simulations, the proposed estimated-execution-time scheduling can improve the utilization of the CPU and the GPU compared to existing scheduling schemes, resulting in reduced execution time and enhanced energy efficiency of heterogeneous computing systems.     

A high-level energy consumption model for heterogeneous data centers

Data centers consume anywhere between 1.7% and 2.2% of the United States’ power. A handful of studies focused on ways of predicting power consumption of computing platforms based on performance events counters. Most of existing power-consumption models retrieve performance counters from hardware, which offer accurate measurement of energy dissipation. Although these models were verified on several machines with specific CPU chips, it is difficult to deploy these models into data centers equipped by heterogeneous computing platforms. While models based on resource utilization via OS monitoring tools can be used in heterogeneous data centers, most of these models were linear model. In this paper, we analyze the accuracy of linear models with the SPECpower benchmark results, which is a widely adopted benchmark to evaluate the power and performance characteristics of servers. There are 392 published results until October 2012; these servers represent most servers in heterogeneous data centers. We use R-squared, RMSE (Root Mean Square Error) and average error to validate the accuracy of the linear model. The results show that not all servers fit the linear model very well. 6.5% of R-squared values are less than 0.95, which means linear regression does not fit the data very well. 12.5% of RMSE values are greater than 20, which means there is still big difference between modeled and real power consumption. We extend the linear model to high degree polynomial models. We found the cubic polynomial model can get better results than the linear model. We also apply the linear model and the cubic model to estimate real-time energy consumption on two different servers. The results show that linear model can get accurate prediction value when server energy consumption swing in a small range. The cubic model can get better results for servers with small and wide range.     

A stochastic neighborhood conditional autoregressive model for spatial data

A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.     

基于异构网的一种数据安全模型设计

针对局域异构网络中的数据可用性、保密性及完整性的问题,提出一种终端检测-数据存储访问-安全信道建立及传输的3阶段系统模型方案,即首先对终端系统扫描检测,排除因遭受攻击或硬件层清理不完整导致的数据不可用问题;然后对数据加密存储访问并设置自毁机制,实现数据保密功能排除隐私泄露问题;最后利用SSL及VPN技术实现信道安全建立及数据完整安全传输。通过设计原型模型和仿真实验进行测试和分析,结果表明终端检测数据可用精准度达到90%以上,实现保密性功能的加密速度低于0.5MB/s,且自毁保护机制有效,信道数据完整传输速度平均提高约27.5%。综合分析可见该模型对网络数据安全检测防护方面有较高参考价值。     

高效节能的能量异构传感器网络数据聚合协议

介绍了一种有效地用于能量异构传感网络的数据采集协议EDGA。EDGA在能量最小化的网内通信和均衡能量负荷等性能上有很好的表现。它通过基于权重选择概率选取的簇头获得了更好的处理能量异构环境的能力,并且利用一种简单但有效的方法解决了簇内的面积覆盖问题。仿真结果表明,该EDGA方法在能量异构无线传感网络的数据采集应用中比LEACH和HEED等方法有明显的优势。     

MASHED: Security and privacy-aware mutual authentication scheme for heterogeneous and distributed mobile cloud computing services

ABSTRACT

Rapid development in mobile devices and cloud computing technologies has increased the number of mobile services from different vendors on the cloud platform. However, users of these services are facing different security and access control challenges due to the nonexistence of security solutions capable of providing secure access to these services, which are from different vendors, using a single key. An effective security solution for heterogeneous Mobile Cloud Computing (MCC) services should be able to guarantee confidentiality and integrity through single key-based authentication scheme. Meanwhile, a few of the existing authentication schemes for MCC services require different keys to access different services from different vendors on a cloud platform, thus increases complexity and overhead incurred through generation and storage of different keys for different services.

In this paper, an efficient mutual authentication scheme for accessing heterogeneous MCC services is proposed. The proposed scheme combines the user’s voice signature with cryptography operations to evolve efficient mutual authentication scheme devoid of key escrow problem and allows authorized users to use single key to access the heterogeneous MCC services at a reduced cost.     

Resource estimation for heterogeneous computing

Code-profiling is the process of determining the types of codes found in a given heterogeneous task. Once this information is available, it is desirable to know how many processors are needed for each of the code types. In this paper, we propose two methods for estimating the minimum number of processors required for each of these code types. The first method involves making use of task compatibility graphs. We show that a task compatibility graph can be generated by analyzing certain compatible relations between task module pairs of a given task flow graph. We define the resource (processor) minimization problem therefore to be equivalent to finding the minimal number of cliques that cover the task compatibility graph, or to finding the minimal number of colors that color the vertices of its complement graph, called task conflict graph. We solve this problem using a greedy approach in O(¦V¦log¦V¦¦E¦) time, where ¦V¦ and ¦E¦ are the number of vertices and edges of the task compatibility graph. We further show that for three special types of task compatibility graphs, optimal solution can be obtained in polynomial time. The second method studied in this paper uses the Cluster-M methodology for estimating the minimum number of processors. Examples are shown to compare the estimated results obtained using different techniques.     

A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization

A new meta-heuristic method, called Chemical Reaction Optimization (CRO), has been proposed very recently. The method encodes solutions as molecules and mimics the interactions of molecules in chemical reactions to search the optimal solutions. The CRO method has demonstrated its capability in solving NP-hard optimization problems. In this paper, the CRO scheme is used to formulate the scheduling of Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems, and a Double Molecular Structure-based Chemical Reaction Optimization (DMSCRO) method is developed. There are two molecular structures in DMSCRO: one is used to encode the execution order of the tasks in a DAG job, and the other to encode the task-to-computing-node mapping. The DMSCRO method also designs four elementary chemical reaction operations and the fitness function suitable for the scenario of DAG scheduling. In this paper, we have also conducted the simulation experiments to verify the effectiveness and efficiency of DMSCRO over a large set of randomly generated graphs and the graphs for real-world problems.     

A secure re‐encryption scheme for data services in a cloud computing environment

Cloud computing as a promising technology and paradigm can provide various data services, such as data sharing and distribution, which allows users to derive benefits without the need for deep knowledge about them. However, the popular cloud data services also bring forth many new data security and privacy challenges. Cloud service provider untrusted, outsourced data security, hence collusion attacks from cloud service providers and data users become extremely challenging issues. To resolve these issues, we design the basic parts of secure re‐encryption scheme for data services in a cloud computing environment, and further propose an efficient and secure re‐encryption algorithm based on the EIGamal algorithm, to satisfy basic security requirements. The proposed scheme not only makes full use of the powerful processing ability of cloud computing but also can effectively ensure cloud data security. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security model. Copyright © 2015 John Wiley & Sons, Ltd.     

A code protection scheme by process memory relocation for android devices

Multimedia Tools and Applications - Android devices is emerging as a significant force for multimedia big data, which hold an enormous amount of information about the users. The security and...