Qibo Sun

Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

This paper provides a description of an hybrid system merging the advantages of a GNSS such as the GPS and a wireless system such as Wi-Fi. Once a general mathematical presentation concerning all the multilateration based positioning systems, GPS, GSM and Wi-Fi is done, the paper analyses the accuracy of multilateration techniques processing absolute and/or relative distance measurements between the mobile terminal and multiple base stations such as BTS or Access Points. Based on those formulas, a simulations are done and then analysed. An interpretation of the results obtained over simulations concerning the location estimation from a geometric point of view are also givenEigenvalue decomposition (EVD) is a widely-used factorization tool to perform principal component analysis, and has been employed for dimensionality reduction and pattern recognition in many scientific and engineering applications, such as image processing, text mining and wireless communications. EVD is considered computationally expensive, and as software implementations have not been able to meet the performance requirements of many real-time applications, the use of reconfigurable computing technology has shown promise in accelerating this type of computation. In this paper, we present an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices. Our experimental results using an FPGA-based hybrid acceleration system indicate the efficiency of our novel array architecture, with dimension-dependent speedups over an optimized software implementation that range from 1.5x to 15.45x in terms of computation time.A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.With the rapid development of Internet of Things and Big Data analysis, the computing mode of the 21st century is undergoing profound reform. But these technologies bring great challenges such as more multiple-dimensional and more numerous information with wide-area and heterogeneous sensor networks to classical context-aware frameworks at the same time. The IOV (Internet of Vehicles) applications is one kind of the typical IOT (Internet of Things) applications and the data involved in them are more and more big which need more complex querying or analyzing methods. Therefore, we have researched the big data problems in IOV applications and put forward the clouding based big data space-time analytics methods for contexts storing and contexts querying to improve the analysis efficiency of the systems. By these methods, we can improve the capability of the complex IOV applications for dealing the numerous contexts.

Evaluating Feedback Ratings for Measuring Reputation of Web Services

In the field of service computing, reputation of a Web service is usually calculated using feedback ratings provided by service users. However, the existing of malicious ratings and different preferences of different service users often lead to a bias towards positive or negative ratings. In this paper, we propose a novel reputation measure method for Web services. The proposed method employs two phases (i.e., malicious rating detection and rating adjustment) to enhance the reputation measure accuracy. We first detect malicious feedback ratings by the Cumulative Sum Method, and then reduce the affect of different user feedback preferences by using Pearson Correlation Coefficient. Extensive experiments are conducted. Experimental results show that our proposed method is effective and can enhance the reliability of service selection.

QoS Uncertainty Filtering for Fast and Reliable Web Service Selection

How to select the optimal composited service from a set of functionally equivalent services but different QoS attributes has become a hot research in service computing. However existing approaches are inefficient as they search all solution spaces. More importantly, they neglect the QoS inherently uncertainty due to the dynamic network environment. In this paper, we propose a fast and reliable Web service selection approach that attempts to select the best reliable composited service on the basis of filtering low reliable Web services according to the uncertainty of QoS. The approach first employs information theory and variance theory to abandon high QoS uncertainty services and downsize the solution spaces. A reliability fitness function is then designed to select the best reliable service for composited services. We experimented with real-world and synthetic datasets and compared our approach with other approaches. Our results show that our approach is not only fast, but also find more reliable composited services.

Machine Status Prediction for Dynamic and Heterogenous Cloud Environment

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

Web Service QoS Prediction Approach in Mobile Internet Environments

Existing many Web service QoS prediction approaches are very accurate in Internet environments, however they cannot provide accurate prediction values in Mobile Internet environments since QoS values of Web services have great volatility. In this paper, we propose an accurate Web service QoS prediction approach by weakening the volatility of QoS data from Web services in Mobile Internet environments. This approach contains three process, i.e., QoS preprocessing, user similarity computing, and QoS predicting. We have implemented our proposed approach with experiment based on real world and synthetic datasets. The results show that our approach outperforms other approaches in Mobile Internet environments.

Dynamic Virtual Resource Renting Method for Maximizing the Profits of a Cloud Service Provider in a Dynamic Pricing Model

Multicast benefits data center group communication in both saving network traffic and improving application throughput. The SLA (Service Level Agreement) of cloud service requires the computation correctness of distributed applications, translating to the requirement of reliable Multicast delivery. In this paper we present INICE, a high stable Multicast approach for data center network. The key idea of INICE is to minimize the impact of end host instability on the Multicast performance, by computing the link weight with node stability. A Multicast tree-aware backup overlay is purposely built on hierarchical clustering of stable nodes. INICE proposes feasible and highly efficient node clustering algorithm, and guarantees the out-degree of node being a constant. The backup overlay multicast tree is organized in such a way that it causes little multicast tree rebuilding. We extensively evaluate the INICE algorithm by theoretical analysis and experiments.With an increasing number of cloud service providers (CSP) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing methods are difficult to solve the problem because they do not make full use of temporal price differences. This paper introduces a dynamic virtual resource renting method that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. We first pretreat the historical price series and adopt the outlier detection technique to filter the extreme price. Then, considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We provide an extensive evaluation of our method using Amazon EC2 spot price dataset and normally distributed price dataset. The results demonstrate the effectiveness of our method.This paper presents a voice activity detection (VAD) algorithm based on the Wavelet Packet Transform and the Teager Energy Operation (TEO) processing. The signal is decomposed into subband signals. We used the multi-resolution analysis property of the Wavelet Transform to extract and analyse time-frequency components corresponding to speech. In order to obtain a parameter called Voice Activity Shape (VAS), we used TEO processing to better distinguish subband signals corresponding to speech. The subband variance values of each TEO signal are summed to obtain the VAS, which is higher in speech regions than in non speech regions. Experimental results show that our VAD perform better than the G729B, particularly in difficult noise conditions and also in the case when the speech sound is passed in a nonlinear communication channel. Experimental results are shown in the case of real speech communications from a spaceship to terrestrial 3G cellular network assuming nonlinear interferences.A rapid post-map insertion of an embedded logic analyzer is discussed. The proposed technique makes use of otherwise unused resources in an already-mapped circuit and does not disturb the original placement and routing of the circuit. Using this technique, designers can add debugging circuitry to existing circuits and quickly modify the set of observed signals in just a few minutes instead of waiting for a recompile of their circuit. All tests were performed on a Xilinx Virtex-5 FPGA.

Web Services QoS Measure Based on Subjective and Objective Weight

The previous Quality of Service (QoS) measure method for Web services are not accurate as they often focused on the ambiguity of user preferences but neglected its one-sidedness (i.e., user preferences cannot realize the data distribution characteristics of services set). This paper first points out that user preferences are not only ambiguous but also one-sided. QoS measure should be a co-action of both user preferences and services set, and therefore neglecting the data distribution characteristics of services set have weakened the accuracy of measure results. We present in this paper a novel QoS measure algorithm for Web services employing the subjective and objective weight. The subjective weight is used to quantify ambiguous user preferences and the objective weight is used to correct the one-sidedness of user preference. The two used weights guarantee the measure results can both conform to user preference and reflect the overall performance precisely. The validation of theoretical analysis and experiments based on the QWS real data sets confirmed the efficiency of proposed algorithm.

A Measure Approach for Trustworthy QoS of Web Service

In order to obtain trustworthy QoS (Quality of Service) in situations which have little or no information regarding a services QoS, we propose a QoS measure approach, namely Bayesian Approach with Maximum-Entropy Principle (BA-MEP). BA-MEP firstly extracts the QoS prior distribution from the objective data (such as historical statistics data) and subjective data (such as the service providers and QoS experts) by Maximum Entropy Principle, and then Bayesian Approach is used to infer the QoS posterior distribution, finally, trustworthy QoS can be obtained from the QoS model. In addition, we also propose a trustworthy expert algorithm (TEA) and analysis three reasons that the QoS data of Web services are not always true. Some experiments are illustrated to show the effectiveness of BA-MEP.

Temporal Influences-Aware Collaborative Filtering for QoS-Based Service Recommendation

As service computing becomes increasingly prevalent, the number of web services grows rapidly. It becomes very important to recommend suitable, personalized web services to users. Collaborative Filtering based on Quality of Service (QoS) has been widely used for service recommendation, and variety of factors such as location, environment are taken into account to improve the accuracy of recommendation. However, temporal influences, which is one of key factors affecting the QoS, are not fully considered by the investigators. In this paper, we propose a novel temporal influences-aware collaborative filtering method which designs an enhanced temporal influences-aware similarity measurement to predict QoS values. Finally, we conduct a series of experiments to evaluate the effectiveness of our method, and results show that our method outperforms other state-of-the-art methods.

Minimizing Data Transmission Latency by Bipartite Graph in MapReduce

Many factors affect the time cost of Cloud computing tasks. One of the most serious factors is data transmission latency, which reduces the efficiency of Cloud computing. Existing notable schemes ignore the communication cost among virtual machines (VMs) in the MapReduce environment. In this paper, we propose a VM placement approach to reduce data transmission latency with the communication cost among VMs. We first construct bipartite graph and classify VMs as two groups according to their transmission latency with data nodes. Then we propose two VM placement optimization algorithms to minimize the total data transmission latency (TDTL) and the maximum data transmission latency (MDTL) in the MapReduce environment. Finally, we place VMs for Reduce phase. The evaluation results show that our approach reduces the average data transmission latency by 26.3% compared with other approaches.