Wenhua Zeng

Cloud computing survey

Since the concept of cloud computing was proposed in 2006, cloud computing has been considered as the technology that probably drives the next-generation Internet revolution and rapidly becomes the hottest topic in the field of IT. The paper synthetically introduces cloud computing techniques, including the currently non-uniform definition and the characteristics of cloud computing; The paper also introduces the core techniques of cloud computing, such as data management techniques, data storage techniques, programming model and virtualization techniques. Then the 4-tie overall technique framework of general cloud computing is talked about. Finally, the paper talks about the obstacles and opportunities.

Multi-objective optimization model of virtual resources scheduling under cloud computing and it's solution

Its an basic requirement in cloud computing that scheduling virtual resources to physical resources with balance load, however, the simple scheduling methods can not meet this requirement. This paper proposed a virtual resources scheduling model and solved it by advanced Non-dominated Sorting Genetic Algorithm II (NSGA II). This model was evaluated by balance load, virtual resources and physical resources were abstracted a lot of nodes with attributes based on analyzing the flow of virtual resources scheduling. NSGA II was employed to address this model and a new tree sorting algorithms was adopted to improve the efficiency of NSGA II. In experiment, verified the correctness of this model. Comparing with Random algorithm, Static algorithm and Rank algorithm by a lot of experiments, at least 1.06 and at most 40.25 speed-up of balance degree can be obtained by NSGA II.

An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have a priority to be selected entering the next generation. Moreover, it incorporates an efficient diversity maintenance approach, which is used to prune the obtained nondominated solutions for a good distributed Pareto front. The feasibility of the proposed algorithm is investigated on a set of five biobjective and two triobjective optimization problems and the results are compared with three other algorithms. The experimental results illustrate the effectiveness of the proposed algorithm.

Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems

In this paper, we propose a modified differential evolution (DE) based algorithm for solving multi-objective optimization problems (MOPs). The proposed algorithm, called multi-objective DE with dynamic selection mechanism (DSM), i.e., MODE-DSM, modifies the general DE mutation operation to produce a population at each generation. To determine and evaluate a better spread of the non-dominated solution, a DSM with a new cluster degree measure is developed. The DSM is also used to select diverse non-dominated solutions. The performance of the proposed algorithm is evaluated against seventeen bi-objective and two tri-objective benchmark test problems. The experimental results show that the proposed algorithm achieves better convergence to the Pareto-optimal front as well as better diversity on the final non-dominated solutions than the other five multi-objective evolutionary algorithms (MOEAs). It suggests that the proposed algorithm is promising in dealing with MOPs. The ability of MODE-DSM with small population and the sensitivity of MODE-DSM have also been experimentally investigated in this paper.

A MOEA/D-based multi-objective optimization algorithm for remote medical

Abstract Remote medical resources configuration and management involves complex combinatorial Multi-Objective Optimization problem, whose computational complexity is a typical NP problem. Based on the MOEA/D framework, this paper applies the two-way local search strategy and the new selection strategy based on domination amount and proposes the IMOEA/D framework, following which each individual produces two individuals in mutation. In this paper, by using a new selection strategy, the parent individual is compared with two mutated offspring individuals, and the more excellent one is selected for the next generation of evolution. The proposed algorithm IMOEA/D is compared with eMOEA, MOEA/D and NSGA-II, and experimental results show that for most test functions, IMOEA/D proposed is superior to the other three algorithms in terms of convergence rate and distribution.

A model for fine-grained vehicle classification based on deep learning

Abstract A model for fine-grained vehicle classification based on deep learning is proposed to handle complicated transportation scene. This model comprises of two parts, vehicle detection model and vehicle fine-grained detection and classification model. Faster R-CNN method is adopted in vehicle detection model to extract single vehicle images from an image with clutter background which may contains serval vehicles. This step provides data for the next classification model. In vehicle fine-grained classification model, an image contains only one vehicle is fed into a CNN model to produce a feature, then a joint bayesian network is used to implement the fine-grained classification process. Experiments show that vehicle’s make and model can be recognized from transportation images effectively by using our method. Furthermore,in order to build a large scale database easier, this paper comes up with a novel network collaborative annotation mechanism.

Cloud computing system risk estimation and service selection approach based on cloud focus theory

Abstract The main cloud computing service providers usually provide cross-regional and services of Crossing Multi-Internet Data Centers that supported with selection strategy of service level agreement risk constraint. But the traditional quality of service (QoS)-aware Web service selection approach cannot ensure the real-time and the reliability of services selection. We proposed a cloud computing system risk assessment method based on cloud theory, and generated the five property clouds by collecting the risk value and four risk indicators from each virtual machine. The cloud backward generator integrated these five clouds into one cloud, according to the weight matrix. So the risk prediction value is transferred to the risk level quantification. Then we tested the Web service selection experiments by using risk assessment level as QoS mainly constraint and comparing with LRU and MAIS methods. The result showed that the success rate and efficiency of risk assessment with cloud focus theory Web services selection approaches are more quickly and efficient.

Several public commercial clouds and open source cloud computing software

Cloud computing is usually used to describe the large-scale distributed infrastructure, platform and software services provided by third party, which is a hot topic in IT area in recent year. However, it is disputable in industrial and academic area that what is cloud computing and whether it is a novel technology. This paper attempts to draw a clear picture by the introduction of several public commercial clouds and open source cloud computing software, not to discuss the exactly concept of cloud computing. The commercial cloud computing Amazon EC2 IBM smart cloud Google App Engine and Windows Azure were introduced and the implementation of several open source cloud computing software such as Hadoop Eucalyptus open Nebula and Nimbus were detailed. At last, several representative definitions were showed. The purpose of this paper is that a basic knowledge about cloud computing can be obtained by reading this paper.

Master-Slave Parallel Genetic Algorithm Based on MapReduce Using Cloud Computing

The implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper’s number.

Multi-datacenter cloud storage service selection strategy based on AHP and backward cloud generator model

Abstract This paper proposed the Cloud Storage Service Selection Strategy under the cross-datacenter environment. Due to the dynamic network environment and the independence between the data centers, this paper presented Cloud Storage Service Selection Strategy across the data center based on AHP–backward cloud generator algorithm. The strategy combines the theory of analytic hierarchy process (AHP) analysis and uncertainty reasoning of cloud method by means of collecting cloud storage providers’ quantitative performance data and inferring qualitative classification of service capability, to select Cloud Storage Service Selection Strategy across the data center. Simulation results show that the strategy has a great advantage in system load balance, replica access rate, and data reliability.