Earticle

A cloud computing is a new paradigm that enables users to access applications, infrastructures and service resources by using thin clients at anytime and anywhere. Through this paradigm users can set up the required resources and they have to pay only for the required resources. Due to rapid growth of the demand for computational power by web, scientific and business applications which led to the creation of large-scale data centers that consuming enormous amounts of electrical power. Thus, in the future providing a mechanism for efficient resource management will be an important objective of Cloud computing. In this we propose a method that allocate resources based on the load of Virtual Machines (VMs). To solving the problem of VM resource scheduling in a cloud computing environment, a load predictor is used to predict the load on each node, according to the this, it allocate the resources.

Currently, the combined push-pull model is usually applied in the resource monitoring under cloud computing environment. However, the quantity of data gathered by the producers is much less than those received by the monitor so that a lot of resource is wasted. In order to maintain the data consistency of real time system, an improved adaptive algorithm based on combined push-pull model is proposed. The adaptive strategy is added on the producers that aims to predict when refreshes data came from the monitor using the history data. Experimental result shows that the algorithm can decrease the resource consumption while exchanging data effectively.

The paper target at string similarity search in cloud systems. Existing works focus on query processing within a single server, and it incurs main memory overflow and external memory overflow while dealing with big data. For the above problems, the paper proposes a distributed index to support string similarity search in cloud environments. To provide efficient searching in a single node, an external memory index is designed, which adopts multiple filtering techniques and optimizing strategies. The external memory resident index supports length filter, positional filter in disks. This paper proposes the index construction method. During query processing, asymmetric q-gram is used to reduce the number of inverted lists read from disks. An adaptive algorithm is given to choose inverted lists, and seek the tradeoff between two aspects of query cost. The global index partitions the entire string dataset according the content of strings, and a char vector space partition method is proposed. In char vector space partition method, similar strings are partitioned into the same computing nodes, thus the number of computing nodes involved in a single query is reduced. The partition method is also adopted to determine necessary computing node set for a query to access. Simulation results validate the efficiency and effectiveness of our proposed index.

Data warehousing has proved its importance as a source of decision support in competing market strategies and thus a lot of work is carried out in various aspects of it like conceptual enterprise perspective to ensure cost benefit is achieved by gaining competitive advantage which requires physical organization of data to improve the quality of data and quality of service. This paper gives insight how this is practically achieved, and where researchers are confronted with several challenges like privacy of data, access performance, efficient storage and cost benefit achievement.

Currently, the efficiency of the existing focused crawlers is not high because of their unsatisfactory precision. In this article, we analyze the URL analysis methods of the existing focused crawlers, and propose a URL analysis algorithm based on the semantic content and link clustering in cloud environment. In this algorithm, the download URLs are clustered with the philosophy of clustering on the basis of VSM to improve the precision of the focused crawler according to the correlation between download URLs and new URLs. The algorithm is evaluated on Heritrix3.10 compared with Best First Search algorithm and Shark Search algorithm. The experiment results demonstrate that the algorithm proposed can collect web pages related to the given topic accurately and effectively.Moreover, the algorithm has a good ability of learning which proves the possibility of this algorithm.

In the context of cloud computing, no other aspect looms larger over all others than billing in Infrastructure-as-a-Service or IaaS as it is also known. This paper proposes a checklist option on IaaS Online Billing System that is described as being transparent in its computation in incorporating information on cloud resources availed and consumed, the period under consideration, and the charges accrued, thereon, for utilizing services against payment on-demand. We have discussed here, our proposed Billboard Manager with the checklist option which helps the cloud user in ascertaining how much resources have been consumed and then pay for the services enjoyed. At preset intervals of customers’ choosing, a cloud service provider would be able to inform the users the actual usage. A user is required to thoroughly examine the information and approve the same, for final billing to take place. In case of discrepancy, a user would have the option to raise issues in pre-billing charge statement with the cloud service provider for mutual verification; and to arrive at the final bill. With the aid of this system, customer satisfaction could be significantly enhanced.

Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according to the tree structure formed by clustering. Simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.

The research work in this paper is in the field, the moving target detection spatiotemporal correlation and difference contour tracking algorithm based on a fixed background. The algorithm in the background under the condition of fixed to pay a smaller time complexity, the target detection and tracking has a good effect, so it has higher application value. This paper mainly focuses on the study of motion estimation and compensation algorithm to eliminate the temporal redundancy. Algorithm of detection and tracking of video moving object is a core subject in computer vision field, but also the key technology of intelligent video surveillance system. It combines the research achievements of artificial intelligence and other fields of pattern recognition, image processing, has been widely used in every field of security monitoring, intelligent weapons, video conference, video retrieval. Therefore, detection and tracking algorithm research has the extremely important theory significance and practical value. The starting point of this article is the subjective quality of image reconstruction of how to improve the accuracy of motion estimation and compensation after, to reduce the computation complexity of motion estimation algorithms, to improve the efficiency of motion estimation. This paper makes some studies on the redundant wavelet domain block matching motion estimation and compensation, then the video image for non-translational motion, the DT triangular mesh motion estimation and compensation in the redundant wavelet domain to do related research.

Geographic routing exerts geographic information to select the next-hop along the route to the destination. Greedy forwarding is a geographic routing mechanism that uses geographical location information of nodes and the distance of each node to the destination, to select next-hop node that achieve the most positive movement to the destination. Where greedy forwarding is not possible, it can be said a void occurred. In this case, if there is no an alternative mechanism, packets are easily removed. Hence a void-handling technique is need to continue routing and recovery greedy forwarding. There is a possibility of void in all types of wireless networks including vehicular ad hoc network (VANET). In this article we introduce void problem and present a survey on varied void-handling techniques which are suitable for VANET networks. At the end, we provide a comparative survey of these techniques based on some important features that are effective on the network performance.

Distributed storage of cloud data imposes a great challenge for assuring data availability and reliability in the face of stochastic failures and attacks. Although redundancy scheme such as erasure codes or network coding schemes can be used to improve storage efficiency, they do not consider the scenario that the network may be split up into several regions due to correlated failures of storage components, which may not successfully reconstruct the original data. Furthermore, the stored data on the failed node need to be recovered to keep the same redundancy of network data while introducing low overhead. In view of this, we first devise an efficient algorithm that guarantees residual network has enough data segments to recreate the original data under correlated failures. Second, an efficient data recovery scheme is presented to repair the data stored on the failed nodes in case of Byzantine failures or pollution attacks. The proposed protocol benefits from several key features such as less storage cost, competitive communication cost, and much better data robustness comparing to the state of the art distributed storage systems.

With an increase in the demands, the Cloud computing has become one of the ongoing scalable approaches to fulfill the cloud based services. The biggest advantage of the cloud computing is the ability to overcome the infrastructural challenges those are earlier faced by other technologies. Since the technology is new, therefore the development of the whole structure is not so efficient. It does have a lot of issues on which various scientists and others are working on. Scheduling, load balancing, fault tolerance, etc. are various challenges faced by cloud computing. For this purpose various techniques and algorithms have been proposed. In this paper, we will discuss the issue of load balancing of cloud computing and we will study the different types of load balancing techniques used in in biomedical services and make a comparative analysis among all the existing techniques.

With the rapid development of cloud computing and service computing, Web services are combined to form the composite service with a large granularity is an important research direction at present. The current service composition methods have the problems of low composition efficiency and accuracy, this paper proposes a semantic Web service composition method based on the binary tree. This method uses the ontology semantic reasoning relationship and binary tree theory to composite Web services. The composition relations between Web service interfaces are mainly considered. The approach can enhance the efficiency and accuracy of service composition, and the experiments are used to validate and analyze the proposed methods.

Recently, Cloud computing is most powerful research area because of its flexibility and cost efficiency. The environment provides dynamic, easily scalable services. Although cloud computing promises to the organization as well as consumers to make life much easier, still it has considerable level of security issues. When data is transferred between the client and server, then security is a great concern. Efficient model should be implemented in the cloud environment to make it secure from illegitimate users. So that cloud security is becoming a important recent discussion in the IT sector. This paper describes a survey on different security issues of cloud computing and the possible solution for remediation.

The acoustic emission (AE) technology can be used to assess the security condition of oil storage tank without opening pot. Signal recognition is a foundation to analyze the corrosion status for oil storage tanks. Because of inadequateness of the analysis method of parameters, a new acoustic emission signal recognition method is proposed based on wavelet transform and RBF neural network. AE signal was decomposed to 6 layers by db2 wavelet and the space energy of 6-layer detail features is regarded as the vector of the AE signal characteristics. RBF neural network is designed by considering the characteristics of AE signal. The RBF neural network is trained by using the pattern known of acoustic emission signal. RBF network is used to classify experiments to corrosion, crack and condensation acoustic emission signal. The experimental results show that the recognition rate of RBF neural network reaches 93.3%, which reveals the advantage of the acoustic emission signal of neural network recognition. It has some significance of the quantitative analysis to the safety situation of oil storage tanks.

How to satisfy the users’ QoS requirements while improving the resource utilization is one of the key technologies in cloud computing environment. In our work, a virtual cloud resource allocation model VCRAM-U (Utility-based Virtual Cloud Resource Allocation Model) is proposed. In our model, the problem of virtual cloud resources allocation is abstracted as a utility-maximization problem, taking tradeoffs between the utility of the data center and the performance of the applications into account, and maximizing the utility on the premise of meet user’s performance. We design a local decision algorithm and a global decision algorithm to solve our model. Experimental results show that the virtual cloud resources can be managed and allocated efficiently by our model and algorithms. In addition, our model can get a higher utility of the data center compared with other models.

IaaS model in the Cloud Computing provides infrastructure services to users. However, the provider of such centralized Cloud requires notable investments to maintain the infrastructures. P2P Cloud, whose infrastructures are provided by multiple volunteer nodes in the P2P network, gives a low cost option to the provision of Cloud Computing. P2PCloud workflow is used to handle tasks with control and data dependencies, and plays an important role in P2PCloud environment. It is essential to design an appropriate model to satisfy the distributed and heterogeneous workflow systems that must be flexible, reusable, dynamic feature in higher level. In this paper, An architecture of a P2PCloud system firstly is proposed, and then a P2PCloud-W(P2PCloud workflow system) framework is proposed, which is a novel hybrid decentralized workflow management system facilitating both Cloud and P2P technologies. Finally, a P2PCloud-W case is analyzed using Petri Net.

Due to lacking valid prediction for resource requirement, the existing application approaches for cloud computing resource could hardly achieve a high efficiency. According to this point, we propose a mixed-prediction based resource allocation approach in this paper, which is abbreviated as MPRA. This proposed MPRA employs FFT (Fast Fourier Transform) theory to determine the cyclical attribution. If there is no such attribution existed, the Markov chain is alternatively used to predict the tendency of resource requirement. The experimental results show that the proposed MPRA could predict the future resource requirement more precisely. Moreover, based on the prediction result, it could also allocate the virtual machine resource adaptively, decrease the number of occupied physical machines, and reduce the probability of violating the SLA (Service-level Agreement).

Although in past few decades, lots of research has been done on searching documents in textual data and there have been many commercial systems for retrieving data on the Web. Fast retrieving desired documents from massive textual corpora is still challenging. Indexing is the most commonly used way to improve the performance of retrieving; particularly an effective index structure can help retrieving data more quickly and effectively. This work proposes a novel index structure, named as L−INDEX, to conceptually index massive set of textual data. L-INDEX employs Form Concept Analysis to discover the relationships between documents and expresses the discovered relationships with lattice. To make our approach more applicable to process massive set of data, the structure is implemented in MapReduce environment which can fulfill computing tasks in an efficient distributed way. A set of algorithms is developed for creating, maintaining, storing L-INDEX and searching documents through it as well. A serial of experiments is conducted to verify the performance of our approach by comparing it with some other popular existing indexing structures. The experimental results demonstrate that the proposed approach can index massive set of textual data with an effective structure which supports querying tasks in an efficient way.

In response to the influence of calibration parameter on measurement accuracy of characteristic impedance tester, the paper comes up with a perfect calibration method of two points based on waveform average and the optimal measuring region. The method is based on calibration method of two points. First, waveform average can reduce random errors in the testing process of characteristic impedance tester. Second, automatically selection for the optimal measuring region can reach a high precision. Last, each tube has a separate calibration parameter, increasing its consistency and Independence, which implement highly calibration for the tester. Making an integrated comparison of testing results among three algorithms, including Tektronix instrument and original method of two-point calibration, validates the accuracy of new algorithm. The method was put into the design of characteristic impedance tester using advanced measurement accuracy.

The negotiation process of the Service Level Agreement (SLA) in cloud environment is an interaction process of access control rules and credentials. Due to the mass of credentials in cloud environment, the negotiation efficiency is not high. To address the problem, this paper proposes an Automated Trust Negotiation (ATN) model based on Colored Petri Net (CPN), which exists a legitimate occurrence sequence and a reachable state space graph. A Minimal Credential Path Searching (MCPS) algorithm is proposed to find a minimum credential disclosure set in the reachable marking graph. The results show that the algorithm can effectively improve the negotiation efficiency.

The development of distance education put forward higher requirements for WEB courseware, it should update teaching content based on the degree of students. This article proposes a resource sharing model of courseware based on semantic web so as to solve the problem of courseware in the teaching process. It takes Resource sharing based on SCORM courses object model as a framework and use XML to describe each learning content objects which makes teachers organize course content and create courseware more easy as well as makes excellent courseware resources can be shared in greatest degree. System defines two agents, one is the teacher interface agent for receiving input information from teachers and curriculum recommendation agent and another agent is communicate agent. Course recommendation agents mainly complete the selection of appropriate curriculum content recommended according to teachers in advance and adjust the course object order in accordance with the repository attribute settings.

Based on the study about the basic idea of PageRank algorithm, combining with the MapReduce distributed programming concepts, the paper first proposed a parallel PageRank algorithm based on adjacency list which is suitable for massive data processing. Then, after examining the essential characteristics of iteration hidden behind the PageRank, it provided an iteration acceleration model based on vector computing. Following, using such acceleration model, the paper again brought forward a parallel PageRank algorithm with power iteration acceleration with MapReduce. Finally, after abundant experimental analyses, it has been proved that the both the two proposed algorithm can be suitable for massive data processing and the 2nd one can significantly reduce the numbers of iteration and improve the efficiency of PageRank algorithm.

Convergence and ubiquity are the key characteristics of tomorrow’s service provision infrastructures. Today, Cloud architectures offer cost-efficient backbones that support transmission, storage, and computing of applications contents. These architectures can be used for business, scientific, and other computing purposes. However, the diversity of these services, delivered through Cloud infrastructures, increases its vulnerability to security incidents and attacks. The requirements for cost and complexity reduction make the design and development of protection mechanisms even more challenging. Furthermore, key design features such as confidentiality, privacy, authentication, anonymity, survivability, dependability, and fault-tolerance can be even more difficult to provide. The aim of this paper is to present the state-of-the-art Cloud security and to explore research directions and technology trends to address the protection of Cloud infrastructures. To do so, we are going to explore the fundamental concepts of Cloud computing security, notably, its principles, vulnerabilities, requirements, and mechanisms.

In order to improve the service performance of mobile cloud computing, the paper strategy launched a study about switching strategy of mobile cloud computing, we propose a fused mobile cloud computing switching method based on threshold judgment and network selection .This method divided the switching strategy of mobile cloud computing process into two steps, first by a threshold judgment to determine whether to switch, followed by the use of multi-attribute decision making method to select the best candidate network for the switching .Experimental results show that the proposed switching method has more excellent switching performance than Saw method and TOPSIS method, at the three indicators of switching frequency, completion time, the energy consumption of the mobile terminal have shown outstanding advantages.

In order to meet the requirements for data access by tenants and management by service providers, the multi-tenant data stored in cloud using replica technology must be reasonably placed. For the outweight nodes and the ultra light nodes, according to characteristics of the multi-tenant data and the load of nodes, through adjusting the number and position of the replicas to maintenance and optimization the strategy so that meet the SLA requirements meanwhile minimize the overall cost. Experimental results through comparison with random placement strategy and greedy placement policy demonstrate the feasibility and effectiveness of the proposed strategy.