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The dynamic optimization of virtual machine (VM) placement is to dynamically adjust the placement of VMs on physical machines (PMs) to accomplish some objectives with certain constraints. On one hand, the number of possible combinations of PMs and VMs can be extremely large, which make the optimal solution very hard to get. On the other hand, the optimized solution needs be reachable from the old solution. To solve the problem from both sides, a partitioned optimization framework is proposed. First, four different states of PM, i.e. off, sleeping, ready and running, are introduced with different energy consumption. Running pool, sleeping pool and off pool are set up which partition PMs based on their different states. The classification helps us build the energy consumption model which is needed to evaluate mapping solutions. To decide if a new solution is better than the old one, only three parts of energy need be considered, i.e. energy changes for PMs in different states, energy consumed for changing the states of PMs, and extra energy consumption for migrating VMs. An energy model composed of these three parts is built as the optimization objective. A method is presented to decide the most suitable range to conduct the energy optimization through excluding some PMs in the sleep or off pool if the best solution achieved with those PMs included cannot be better than old solution. A memetic algorithm combining the partheno-genetic algorithm with the multiplayer random evolutionary game theory is proposed to achieve the global optimal solution and generate the executable live migration sequence from old mapping to the target one at the same time. According to our experimental results, our method can decrease the optimization range remarkably. Within the optimized scales, the proposed algorithm performed very well to approach the global optimal solution and guarantee the solution’s feasibility from old solution at the same time.

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.

The computational model of energy consumption is built to serve as an evaluation function.An algorithm based on evolutionary game theory is proposed to solve the problem of dynamic VMs placement.It is analyzed that the algorithm can theoretically reach the optimal solution of the dynamic VMs placement problem.The algorithm can take the initial mapping into account and generate an executable list of VMs live migrations from the initial state to the target state. Power saving of data centers has become an urgent problem in recent years. For a virtualized data center, optimizing the placement of virtual machines (VMs) dynamically is one of the most effective methods for power savings. Based on a deep study on VMs placement, a solution is proposed and described in this paper to solve the problem of dynamic placement of VMs toward optimization of their energy consumptions. A computational model of energy consumption is proposed and built. A novel algorithm based on evolutionary game theory is also presented, which successfully addresses the challenges faced by dynamic placement of VMs. It is proved that the proposed algorithm can reach the optimal solutions theoretically. Experimental results also demonstrate that, by adjusting VMs placement dynamically, the energy consumption can be reduced correspondingly. In comparison with the existing state of the arts, our proposed method outperforms other five algorithms tested and achieves savings of 30-40% on energy consumption.

The objective of this paper is to develop an effective photographic transfer method while preserving the semantic correspondence between the style and content images. A semantic correspondence guided deep photo style transfer algorithm is developed, which is to ensure that the semantic structure of the content image has not been changed while the color of the style images is being migrated. The semantic correspondence is constructed in large scale regions based on image segmentation and also in local scale patches using deep image analogy. Based on the semantic correspondence, a matting optimization is utilized to optimize the style transfer result to ensure the semantic accuracy and transfer faithfulness. The proposed style transfer method is further extended to automatically retrieve the style images from a database to make style transfer more-friendly. The experimental results show that our method could successfully conduct the style transfer while preserving semantic correspondence between diversity of scenes. A user study also shows that our method outperforms state-of-the-art photographic style transfer methods.

Using effective modeling ways can improve the quality of workflow models. A new role-oriented modeling way is presented to set up workflow process models based on object Petri net. The details of each role are encapsulated in a role object net. The interfaces are provided for it to communicate with other roles. The cooperative relationships of roles are described in control object nets. An example is given to show the process of the presented modeling method.

Accurate performance metric models are the key to Web capacity planning related problems. Due to the complexity of Web systems, analytical modeling without integrating the performance testing process is not enough to get accurate metric models. To integrate performance testing and analytical modeling in a systematic way, a Web performance modeling process is presented based on the methodology of learning from data. The process divides the modeling activity into several phases: constructing models and hypothetical conditions, deriving test cases, estimating parameters and validating models, etc. The scalability of a real Web community system (www.igroot.com) is studied by using the proposed process. The error of estimated saturation point is within 1 percent, the error of estimated lower bound of buckle point is within 5 percent. At last, a HTTP processing bottleneck at the architecture level is identified by correlating the model with the threads data of the Web server.

Workflow resource model describes all kinds of resources that support the execution of workflows. The meta-model for workflow resource model presents the constituents of workflow resource model. It is one of the three correlative sub-meta-models for workflow model. Based on the analysis of existed studies and real cases, an extended meta-model for workflow resource model was introduced by extending and modifying the meta-model for organizational model proposed by WfMC. The detail of entities and their relationships were described. The relationships between workflow resource model and process model were discussed. XML was used to describe the meta-model. In the end, a conclusion and proposals for future research directions were presented.

The arm of this work is to provide a formal model and an effective way for information retrieving from a big related data set. Based upon fuzzy logic operation, a fuzzy mathematical model of 0-1 mixture programming is addressed. Meanwhile, a density function indicating the overall possessive status of the effective mined out data is introduced. Then, a soft computing (SC) approach which is a genetic algorithm (GA) embedded fuzzy deduction is presented. During the SC process, fuzzy logic decision is taken into the uses of determining the genes' length, calculating fitness function and choosing feasible solution. Stimulated experiments and comparison tests show that the methods can match the user's most desired information from magnanimity data exactly and efficiently. The approaches can be extended in practical application in solving general web mining problem.