Earticle

Based on the delocalized entanglement correlation and transience of transferring quantum information of quantum teleportation, an improved quantum stop-wait communication protocol for data link layer is presented. In the previous quantum stop-wait protocols, they only send the quantum acknowledgement frame (QACK) without sending quantum negative acknowledgement frame (QNACK). In this paper, we present an improved quantum stop-wait communication protocol that sends the negative frame QNACK as soon as possible when Bob detects the error of received data frame, instead of waiting for the retransmission time tout. As a result, the minimum time span between two successfully delivered data frames can be significantly reduced, and the communication time is shortened. It is shown that our protocol enhances the maximum throughput effectively and improves the communication efficiency in the data link layer.

Cloud computing, the model for providing on-demand access to a pool of shared resources with minimum provider interference, is emerging as a substitute to common IT infrastructure. As increasing numbers of cloud consumers dispatch their workloads to cloud providers, Service Level Agreement (SLA) between consumers and providers becomes of paramount importance to guarantee that service quality is preserved at satisfactory levels regardless of the dynamic nature of the cloud environment. SLA contains an explanation of the agreed service, parameters of the level of service, the guarantees regarding the Quality of Service, arrangements and cures for all cases of violations. In this paper, we provide a study about the general structure of SLA, its components, the management processes (in particular SLA monitoring), SLA lifecycle, and pricing. Then, we explore the importance of SLA for cloud computing services related to both the cloud user and the service provider including the differences between SLA for cloud services and other Web services. Finally, we present a comparison between the current major cloud computing service providers in terms of SLA’s.

The big data set in Energy Acquisition System needs to acquire massive electric energy data and dynamic information online, and finishes processing in scheduled time. This requires a higher demand on massive data storage and data processing. In order to achieve these massive electric energy data efficiently, this article based on the data gathering system and storage structure of Hadoop technique, and tested electric energy mensuration log data set of a city as an example, the result shows that the bigger of the sets group, the better effect would be achieved, which effectively avoid the latency problem of big data set information processing respond.

Mobile agent technology is emanating as a new paradigm in the area of distributed and mobile computing, and has been engaged in many areas from network management tasks to information management. It makes considerably simpler to model, implement, and maintain distributed systems, including sensor networks and cloud computing. It provides an infrastructure not only for executing autonomous agents but also migrating them between different computers. In the recent years, mobile agent paradigm has emerged as a viable approach for the evolution of autonomic systems in the healthcare domain. This paper reviews the applicability and usefulness of mobile agents in a wide variety of healthcare applications such as medical data management, medical information retrieval, health data integration, decision-making support, telemedicine, securing medical information and coordination of distinct medical activities.

In the orthogonal direction (OD) algorithm, the iteration direction is inconsistent with the direction that leads to the iteration error, thereby, degrading the convergence performance of adaptive filtering. In this paper, we propose a new self-adaptive orthogonal direction algorithm (called OD-DE) by introducing iteration direction error. In the OD-DE algorithm, the iteration direction is changed to be consistent with the direction that causes the iteration error, both equal to the direction vector established for input signal. Besides, we also estimate the optimal step-size for the OD-DE algorithm, to further improve the convergence performance of adaptive filtering. Last, the simulation experiments demonstrate the effectiveness of our proposed algorithm.

DE\BBO is the hybridization of Differential Evolution optimization and Biogeography Based Optimization. DE is considered to have good exploration ability and BBO is considered to have good exploitation ability, to achieve goodness of both techniques, these techniques have been combined. Hybridization of Differential evaluation and biogeography based optimization dominates the performance of biogeography based optimization. The influencing factors of performance of DE are scaling factor and cross-over rate which are used to track proper optimization. In this paper, it has been shown how DE/BBO outperforms BBO. The change in the performance of DE/BBO due to variation in some pre-considered variables has also been highlighted. It has been observed that DE/BBO performs best when scaling factor is 0.11 and cross over rate is 0.2.

Aiming at the problem of resource scheduling in cloud computing, we constructed a new scheduling method based on trust model and LB algorithm. Firstly, on the basis of matching tasks and resources, we established three calculation modes on security requirements and reliable demand benefits. Secondly, based on the benefit function of security requirements and reliable demand, we built the trust benefit function of total. Finally, we got a cloud resource scheduling algorithm which combined with load balancing algorithm. The experimental results show that proposed method has obvious advantages comparing with the maximum and minimum trust driving method in the execution time span, load balancing performance and average waiting time, and this method can carry out fast and efficient resources scheduling for cloud computing.

The information that caused by the complex network is massive, but because of a large amount of information, so the use of traditional data analysis has been unable to meet the search and mining complex network data, so the distributed computing mode, i.e., cloud computing has become the main method of calculation method of complex network, according to the calculation of cloud computing, it needs to consider computing topological partition method and the computational performance. This paper presents a data mining model matrix, according to this model can integrate different information, optimization of data mining, so as to improve the efficiency of complex network distributed computing.

Satellites today are prime source of information capture and decision making for research organizations, corporate houses and government departments like weather forecasting, agricultural monitoring etc. Accurate and timely retrieval of data is key challenge for satellites today. The major issue with information capture is mixing of unwanted signals called noise with required vital data which leads to malfunctioning of decision making process. In order to overcome the issue, it becomes a mandate today to design and develop specific filters to different satellites which are problem specific and capable of tuning the captured information in various formats in robust manner. In this work, a review of Satellite image filter for RISAT is presented. These filters work on visible, infrared and water vapor images which are taken from satellites.

In order to compensate for the current lack of assessing cloud resource allocation and business operation from the economical perspective, this paper introduced the Data Envelopment Analysis (DEA) method which is one of the most famous methods in economics into this field. Based on it, it proposed several new methods to evaluate the efficiency of cloud resource allocation and cloud providers’ business operation status through the way of case study.

One of the advantages of web services is development of the software through composition of web services, which use existing web services as a component to create software on demand, for composition to be useful and valid in development of the software, web services composition need to be validated for correctness. In this paper, we have formally represented web services using Pi-Calculus. It is a kind of process algebra which can be used to model dynamic concurrent systems and behavior of web services composition is very close to it. For verification the tool MWB (Mobility Workbench) has been used.

Building applications on on-demand infrastructures instead of building applications on fixed and rigid infrastructures was provided by cloud computing providers. By simply positing into the cloud, it gains fast access to business applications or infrastructure resources with reduced Capital Expenditures (CAPEX). Dramatic increase in amount of information’s are placed into the cloud by individuals and industries, security issues are vital concern in mobile computing (MCC) and impends fast deployment of applications on the cloud. This work discus the different security issues that arises about how safe the mobile cloud computing environment is and provides a unified reliable security mechanism. There are two different types of the cloud users are: On-demand and Optimistic. On-demand is a non-preemptible for flexible leases given a user accessing to the resources within an interactive time of making the request and makes the resources available for an agreed-upon period of time, user can deploy any virtual machine (VM) compatible with the system. Optimistic is preemptible and pre-set contract gives a user access to resources at an indeterminate time and make resources available to the user for an insufficient amount of time. After that, this resources are initially (pre)-defined for the user by the cloud admin, that is the user cannot provide his or her own virtual machine (VM) based on defined access control for security.

Analyzing user opinion and building features model of entities is very important for sentiment analysis and opinion mining, which is the fundamental work for social network analysis. Opinions of entity can be classified into two types: explicit aspect expressions and implicit aspect impressions, and implicit aspect expressions might have some relationships which can be denoted by domain specific knowledge, existing previous works have not addressed in much detail. This paper defines an opinion mining model for implicit aspect expression based on semantic ontology, and proposes semantic similarity estimation approaches of entity by the domain ontology.

In a cognitive radio network (CRN), a preempted secondary user (SU) is placed in a call level queue to wait for accessing another free channel. If the availability of channels is transparent to SUs, packets will be generated during their waiting time and the performance of the CRN will be influenced by which way to handle these packets. This paper mainly analyzes the performance of a CRN adopting discarding or buffering policy, Here, an analytical model is developed to derive the formulas for both call level performance measures (i.e., call blocking probability) and packet level performance measures (i.e., packet delay, packet loss ratio and throughput) for SUs. Numerical results show that theoretical models are consistent with simulation results. The major observations include (i) the performances of a CRN degrade as the primary user (PU) or SU call arrival rate increases. (ii) Under discarding policy, the call blocking probability and packet delay are smaller than adopting buffering policy, while the packet delay and throughput are greater. (iii) Adopting different policies cause a smaller effect on call blocking probability and throughput than on packet loss ratio and packet delay.

In 21th centaury energy has become a prime need of the civilization. One of the most important types of energy is the electrical energy, which is very much essential for both domestic & industrial purposes. In present century, more than 60% of the total electric energy is producing from coals, 14-17% of energy is producing from Renewable sources & the remaining part is from natural gases. So, it is clear that maximum sources (more than 80%) of energy are causes pollution. Pollution & Global warming are the main reason of rapid environmental misbalancing. In case of our state (Tripura) this energy generation figures or records are not impressive, approximately 1% of total energy is producing from hydro-power & the rest percentages are from gas-thermal plants. It is observed that the year round insufficiencies of river water & irregular rainfall in hilly river sub-basin areas are the main reasons of decrement in hydro-power generation. So, in present scenario, the concept of Pump-Storage Hydro-Power station is very much appropriate, because this type of plant can recycle fix storage of water. That’s why insufficiency of river water will not affect the generation like conventional hydro power station. In this work Multi Criteria Decision Making Algorithm (MCDMA) is used to find the best alternative with respect to the design consideration to select a best location in this state to set pump storage plant, the suitable location has been selected by both MCDM (AHP) & cost effective analysis. This work tried to give some key point regarding the problems, mitigations & future scope of Gomati Hydro Power Project by integrated studies on Dumbur Lake, river sub-basin, average annual rainfall & annual mean depth of the river. At the end the project implies a probability of more than 11 MW Hydro-power generations by installing Mini-Hydro Pump storage plant in this state Tripura using the water of River Gomati. Finally plant network has been optimized by Flex-Sim software, to check the efficiencies of different component under the assigned/ proposed conditions.

The problem of delay-dependent condition for a new class of Takagi-Sugeno (T-S) fuzzy Lurie control systems with time-delay and time-variant uncertainties is investigated in this paper, which is different from existing ones. We use T-S fuzzy models to describe Lurie system in the form of a weighted sum of some simple linear subsystems, and a new delay-dependent absolute stability criterion for such systems is derived by utilizing Lyapunov-Krasovskii functional (LKF), linear matrix inequality (LMI) approach and novel techniques. Finally, a numerical example and its simulation results have shown that the proposed result is feasible and effective.

Improving the energy efficiency of high performance clusters has become important research issue. We proposed a new algorithm that reduces energy consumption of precedence constrained parallel tasks in power-scalable clusters. To reduce energy consumption without increasing the schedule length, our algorithm reclaims both static and dynamic slack time and employs different frequency adjusting techniques in different slack time. The optimal frequency is obtained through analyzing the precedence constraints of parallel tasks. We conducted extensive experiments to compare the proposed algorithm with two other existing algorithms, simulation results show that the proposed algorithm can get better energy efficiency without increasing the make span.

Cloud Computing is an emerging paradigm which has become today’s hottest research area due to its ability to reduce the costs associated with computing. In today’s era, it is most interesting and enticing technology which is offering the services to its users on demand over the internet. Since Cloud computing stores the data and its disseminated resources in the environment, security has become the main obstacle which is hampering the deployment of cloud environments. There are number of users used cloud to store their personal data, so that data storage security is required on the storage media. The major concern of cloud environment is security during upload the data on cloud server. Data storage at cloud server attracted incredible amount of consideration or spotlight from different communities. For outsourcing the data there is a need of third party. The importance of third party is to prevent and control unauthorized access to data store to the cloud. This research paper discusses the security issues of cloud storage.

Nowadays, Software-Defined Networking (SDN) is utilized to improve network management, but the management standardization of SDN itself is still under development. This paper focuses on the integration problem of management information models for SDN, and in order to understand the principles of unified information modeling for SDN management, it tries to apply the theory of concept lattices to learn concept hierarchy from the YANG language for SDN management. Thus, this paper first obtains formal contexts from YANG statements, and then studies formal concepts from the viewpoint of posets so as to learn concept hierarchy from YANG. Case study shows that, the proposed approach is feasible and promising to promote unified information modeling for SDN management.

The management of Multi-sensors in information fusion system occupies an important role with the development of modern weapon platform. Therefore, scientific and rational management of limited sensors resources is essential or urgent, and improvement the capability of air defense operations is necessary. According to the management optimization problem in Multi-sensor optimal resource, we analyzed the definition of rough entropy and presented the method of Multi-sensor management based on rough entropy and target threat degree. By calculating rough entropy of the sensors to the target, the maximum information gain access for each sensor on each target, which acts as cost function, taking into account the target threat degree, and using a linear programming optimization assignment multi-sensor to multi-target. In this paper, we adopted maximum information gain optimization criterion in target tracking, then discussed optimization assignment problem about multi-sensor to multi-target. In addition, we are validate the optimized allocation algorithm with the experimental simulations, and case analysis shows that the effectiveness of the method.

The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.

As a new computing pattern, client-cloud computing can provide limitless computing and storage capacity according to the need for different types of client devices and avoid the increasingly skeptical cloud security question. For this purpose, a client-cloud computing collaborative architecture was built. On this basis, a collaborative service model based on the cloud and centered on clients was designed, and some crucial steps of collaborative application initiated by client were analyzed. A collaborative system development, maintenance method and process centered on clients were pointed out. The client-cloud computing collaborative model proposed in this paper provided a distributional, effective, independent, unfailing and secure architecture for the collaboration, highlighted the user’s core position in collaborative activities, permitted individual users to start and manage collaborative application fast and easy from local.

Type-2 fuzzy logic to make up for the lack of a type of fuzzy logic in dealing with uncertainty, object contains uncertainty is strong; the application of type-2 fuzzy logic advantage is more obvious. In this paper, type-2s of fuzzy logic for power load time series forecasting, good results were obtained. According to the power load has strong randomness it is difficult to accurately forecast problem, the introduction of the type-2 of fuzzy logic in order to reduce the prediction error. According to the power load is difficult to predict accurately the problem, this paper introduces the interval type-2 fuzzy logic method to reduce the prediction error, presents an interval type-2 fuzzy logic model for the time series of one hour of power load forecasting, and adopted the first modeling process model structure, and then use back propagation algorithm to adjust the model parameters are determined by simulation. At the same time, based on the back-propagation algorithm of a fuzzy model with interval type-2 fuzzy model, singular value decomposition iterative blending algorithm of interval type-2 fuzzy model, the predicted result was the test performance benchmark. The simulation results show that the prediction accuracy of the forecasting model, based on the established with high, can better track the actual load curve, a better performance than the other three models.

Based upon several new technologies that have been developed such as semantic web, SPARQL language and ontology engineering, this paper proposes platform architecture for e-learning. It is an e-learning management system with metadata. This system consists of ontology for the e-learning process, such as teaching methods, learning styles, learning activities and course syllabus. It helps students, administrative staff and teachers to set up and maintain the course data and go through the learning content. This system architecture will be capable of gaining user adaptability, performance scalability and concept reusability. It has ability to act in an intelligent manner by evaluating the academics initially and then provide personalized suggestions to the academics indicating their weaknesses and strengths.

Aiming at the provincial level within the grid wide-area distributed substation equipment on-line monitoring various types, processing of large amount of data and communication standard is not unified. Optimization of the structure of communication between the various levels, researched open communication format specification, established data interaction integration model. The paper studied the wide-area distributed on-line monitoring comprehensive processing unit of end stations with provincial level network of on-line monitoring system for the main platform of communication technology. Application shows that systems runs stability and meet online monitoring data collection, processing, transmission, and other functions, and has good maintainability and extensibility, adapt to various on-line monitoring in province level the development trend of the main platform for centralized management.

Effective allocation of shared resources limited is a key problem for chip multiprocessors. As the processor core growth in the scale, multi thread for the shared resource limited system competition will become more intense, the performance of the system will also be more significant. In order to alleviate this problem, a fair and effective multi thread shared resources allocation scheduling algorithm is important. In all kinds of shared resources, the largest effect on the system performance is the shared cache and DRAM system. There are essential differences between the last level cache and a cache. The goal of a cache design is to provide fast data processor which requires high access speed. However, the object of the last level cache is to save data in the chip as much as possible, and the access speed requirements are not too high, it is more subject to the plate number of available transistors. Management level cache LRU strategy and its approximate algorithm are not applicable to the large capacity last level cache for traditional. It may cause destructive interference between threads, cache thrashing of stream media program lead, which will lead to a decline in the performance of processor. This paper focuses on the analysis of some hot problems of the last level cache management in the process of the large capacity of multi-core platform sharing, and puts forward the corresponding costs less.