QoS-Aware Virtual Machine Scheduling for Video Streaming Services in Multi-Cloud

Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of Service (QoS) and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.     

Performance Improvement of Distributed Systems by Autotuning of the Configuration Parameters

The performance of distributed computing systems is partially dependent on configuration parameters recorded in configuration files. Evolutionary strategies, with their ability to have a global view of the structural information, have been shown to effectively improve performance. However, most of these methods consume too much measurement time. This paper introduces an ordinal optimization based strategy combined with a back propagation neural network for autotuning of the configuration parameters. The strategy was first proposed in the automation community for complex manufacturing system optimization and is customized here for improving distributed system performance. The method is compared with the covariance matrix algorithm. Tests using a real distributed system with three-tier servers show that the strategy reduces the testing time by 40% on average at a reasonable performance cost.     

Compact packaging for multi-wavelength DML TOSA

A compact multi-wavelength hybrid-integrated directly-modulated distributed-feedback laser （DML） transmitter optical sub-assembly （TOSA） has been achieved in our laboratory. The 8-channel distributed feedback （DFB） lasers are monolithically integrated based on the reconstruction-equivalent-chirp （REC） technology. With the high-density and high-speed packaging technique, the laser array and a multi-mode interference （MMI） multiplexer are assembled in the TOSA. The channel spacing of the TOSA is 200 GHz between adjacent lasers. It meets the 8 ; 12.5 Gb/s operation demand and gives rather low channel crosstalk of less than -25 dB. This compact TOSA is of effective cost and shows good sta- bility for mass production, which is expected to improve the performance of devices in access networks, data centers and supercomputing.     

DR-Cloud: Multi-Cloud Based Disaster Recovery Service简

With the rapid popularity of cloud computing paradigm, disaster recovery using cloud resources becomes an attractive approach. This paper presents a practical multi-cloud based disaster recovery service model： DR- Cloud. With DR-Cloud, resources of multiple cloud service providers can be utilized cooperatively by the disaster recovery service provider. A simple and unified interface is exposed to the customers of DR-Cloud to adapt the heterogeneity of cloud service providers involved in the disaster recovery service, and the internal processes between clouds are invisible to the customers. DR-Cloud proposes multiple optimization scheduling strategies to balance the disaster recovery objectives, such as high data reliability, low backup cost, and short recovery time, which are also transparent to the customers. Different data scheduling strategies based on DR-Cloud are suitable for different kinds of data disaster recovery scenarios. Experimental results show that the DR-Cloud model can cooperate with cloud service providers with various parameters effectively, while its data scheduling strategies can achieve their optimization objectives efficiently and are widely applicable.     

Power coal transportation and storage: A programming analysis of road and rail options

In this study,the transportation and storage problems for regional power coal allocation planning are formulated as transportation and storage problems to realize the minimization of the regional transportation and storage cost.An effective optimization model is proposed to solve transportation and storage problems for regional power coal allocation planning,which has interactive effect on multiple participants,such as regional power plants,coal transportation companies,logistics centers,and coal storage centers.A case study illustrates that the model and algorithm are more reasonable compared with the classic transportation model,and the sensitivity analysis improves transportation and storage strategies for regional power coal allocation planning.Results demonstrate that this model can not only satisfy more of the actual requirements of the integral system but also offer more information to the decision-makers(DMs) for reference in favor of exalting decision-making quality.     

Trust Based Pervasive Computing

Pervasive computing environment is a distributed and mobile space. Trust relationship must be established and ensured between devices and the systems in the pervasive computing environment. The trusted computing （TC） technology introduced by trusted computing group is a distributed-system-wide approach to the provisions of integrity protection of resources. The TC＇s notion of trust and security can be described as conformed system behaviors of a platform environment such that the conformation can be attested to a remote challenger. In this paper the trust requirements in a pervasive/ubiquitous environment are analyzed. Then security schemes for the pervasive computing are proposed using primitives offered by TC technology.     

A rough granular computing in discovery of process models from data and domain knowledge

The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored, but also in the burgeoning complexity of the concepts and phenomena pertaining to those data. This issue has been vividly compared by the renowned statistician, prof. Friedman of Stanford University, to the advances in human mobility from the period of walking afoot to the era of jet travel. These essential changes in data have brought new challenges to the development of new data mining methods, especially that the treatment of these data increasingly involves complex processes that elude classic modeling paradigms. “Hot” datasets like biomedical, financial or net user behavior data are just a few examples. Mining such temporal or stream data is on the agenda of many research centers and companies worldwide. In the data mining community, there is a rapidly growing interest in developing methods for process mining, e.g〖DK〗., for discovery of structures of temporal processes from data. Works on process mining have recently been undertaken by many renowned centers worldwide. This research is also related to functional data analysis , cognitive networks , and dynamical system modeling, e.g., in biology. In the lecture, we outline an approach to discovery of processes from data and domain knowledge which is based on the roughgranular computing.     

Distributed Estimation for Sensor Networks with Channel Estimation Errors

This paper describes the effect of channel estimation error (CEE) on the performance of distributed estimations of an unknown parameter in a wireless sensor network. Both the classical and Bayesian estimators are derived to mitigate the adverse effects caused by the CEE. Power scheduling among sensors and the power ratio between the training and data transmission at each individual node are optimized by directly minimizing the final average mean squared error to compensate for the CEE. A closed-form power scheduling policy is given for a homogeneous environment, which shows that more than 50% of the power should be allocated to sensor observation transmissions. For an inhomogeneous environment, a multilevel waterfilling type solution is developed for the power scheduling among sensors for only the sum power constraint with a "cave" waterfilling solution for both the sum and individual power constraints. Simulations show that the proposed power scheduling schemes achieve better performance than the equal power scheduling scheme.     

Neural network approach for modification and fitting of digitized data in reverse engineering

Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.     

I-Neat:An Intelligent Framework for Adaptive Virtual Machine Consolidation

With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine(VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation.However, the performance of different algorithms is related with the characteristics of the workload and system status;some algorithms are suitable for Central Processing Unit(CPU)-i...     

Improved verifiability scheme for data storage in cloud computing

In Cloud computing,data and service requests are responded by remote processes calls on huge data server clusters that are not totally trusted.The new computing pattern may cause many potential security threats.This paper explores how to ensure the integrity and correctness of data storage in cloud computing with user’s key pair.In this paper,we aim mainly at constructing of a quick data chunk verifying scheme to maintain data in data center by implementing a balance strategy of cloud computing costs,removing the heavy computing load of clients,and applying an automatic data integrity maintenance method.In our scheme,third party auditor (TPA) is kept in the scheme,for the sake of the client,to periodically check the integrity of data blocks stored in data center.Our scheme supports quick public data integrity verification and chunk redundancy strategy.Compared with the existing scheme,it takes the advantage of ocean data support and high performance.     

The adaptive distributed learning based on homomorphic encryption and blockchain

The privacy and security of data are recently research hotspots and challenges. For this issue,an adaptive scheme of distributed learning based on homomorphic encryption and blockchain is proposed. Specifically, in the form of homomorphic encryption, the computing party iteratively aggregates the learning models from distributed participants, so that the privacy of both the data and model is ensured. Moreover, the aggregations are recorded and verified by blockchain, which prevents attacks from ...     

Heterogeneous Parallel Algorithm Design and Performance Optimization for WENO on the Sunway TaihuLight Supercomputer

A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system's architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.     

Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing简

A data center is an infrastructure that supports Internet service. Cloud comput the face of the Internet service infrastructure, enabling even small organizations to quickly ng is rapidly changing build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. Network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used n a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet nspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for ntelligence flow processing to protect from possible network attacks inside a data center network     

Trajectory P system

Membrane computing is a branch of natural computing aiming to abstract computing ideas for the structure and the functioning of living cells as well as from the way the cells are organized in tissues or higher-order structures. Trajectories are used as a tool for modeling language operations and other related objects. A trajectory P system consists of a membrane structure in which the object in each membrane is a collection of words and the evolutionary rules are given in terms of trajectories. In this paper, we present some properties of trajectory P systems.     

Lightweight Privacy-Aware Yet Accountable Secure Scheme for SM-SGCC Communications in Smart Grid

Smart grid is envisioned as a critical application of cyber-physical systems and of the internet of things.In the smart grid,smart meters equipped with wireless sensors can upload meter readings (data) to smart grid control and schedule centers via the advanced metering infrastructure to improve power delivery efficiency.However,data gathered in short intervals,such as 15minutes,will expose customers’ detailed daily activities (for example,when they get up and when they use oven) using nonintrusive appliance load monitoring.Thus,data must be hidden to protect customers’ privacy.However,data accountability is still required for emergency responses or to trace back suspected intrusions,even though the data is anonymous.In addition to desired security requirements,this imposes two extra tasks:Sensors in smart meters usually have resource constraints;thus,the desired security protocols have to remain lightweight in terms of computation and storage cost.Furthermore,scalability and flexibility are required since there exist vast meters.This paper presents a lightweight Privacy-aware yet Accountable Secure Scheme called PASS which guarantees privacy-aware accountability yet tackles the above challenges in the smart grid.A formal security analysis justifies that PASS can attain the security goals,while a performance analysis verifies that PASS requires few computations,and is scalable and flexible.     

Joint UAV 3D deployment and sensor power allocation for energy-efficient and secure data collection

Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on...     

Integration of a statistical emulator approach with the SCE-UA method for parameter optimization of a hydrological model

Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.     

Particle Swarm Optimization Embedded in Variable Neighborhood Search for Task Scheduling in Cloud Computing

In cloud computing system,it is a hot and hard issue to find the optimal task scheduling method that makes the processing cost and the running time minimum. In order to deal with the task assignment,a task interaction graph was used to analyze the task scheduling; a modeling for task assignment was formulated and a particle swarm optimization (PSO)algorithm embedded in the variable neighborhood search (VNS) to optimize the task scheduling was proposed. The experimental results show that the method is more effective than the PSO in processing cost,transferring cost, and running time. When the task is more complex,the effect is much better. So,the algorithm can resolve the task scheduling in cloud computing and it is feasible,valid,and efficient.     

Uniform wire segmentation algorithm of distributed interconnects

A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer inser-tion are obtained through computation and derivation, based on a 2-pole approximatian model of distribut-ed RLC interconnect. For typical inductance value and long wires under 180nm technology, experiments show that the uniform wire segmentation technique proposed in the paper can reduce delay by about 27%～56%, while requires 34%～69% less total buffer usage and thus 29% to 58% less power consump-tion. It is suitable for long RLC interconnect performance optimization.