Edge computing oriented virtual optical network mapping scheme based on fragmentation prediction

As edge computing services soar,the problem of resource fragmentation situation is greatly worsened in elastic optical networks(EON).Aimed to solve this problem,this article proposes the fragmentation prediction model that makes full use of the gate recurrent unit(GRU)algorithm.Based on the fragmentation prediction model,one virtual optical network mapping scheme is presented for edge computing driven EON.With the minimum of fragmentation degree all over the whole EON,the virtual network mapping can be successively conducted.Test results show that the proposed approach can reduce blocking rate,and the supporting ability for virtual optical network services is greatly improved.

FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.

Naive Echo-State-Network Based Services Awareness Algorithm of Software Defined Optical Networks

As internet services newly emerge with diversity and complexity, great challenges and demands are presented to the Open Flow controlled software defined optical networks(SDON) to achieve better match between services and SDON. With this aim, this paper proposes a naive Echo-State-Network(Naive-ESN) based services awareness algorithm of the software defined optical network, where the naive ESN model adopts the ring topology structure and generates the probability output result to determine the Qo S policy of SDON. Moreover, the Naive-ESN engine is also designed in controller node of SDON to perform services awareness by obtaining service traffic features from data plan, together with some necessary extension of the Open Flow protocol. Test results show that the proposed approach is able to improved services-oriented supporting ability of SDON.

Naive-LSTM based services awareness of edge computing elastic optical networks

Great challenges and demands are presented by increasing edge computing services for current elastic optical networks(EONs)to deal with serious diversity and complexity of these services.To improve the match degree between edge computing and optical network,the services awareness function is necessary for EON.This article proposes a Naive long short-term memory(Naive-LSTM)based services awareness strategy of the EON,where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the LSTM model.Moreover,the proposed algorithm can generate the probability output result to determine the quality of service(Qo S)policy of EONs.After well learning operation,these Naive-LSTM classification agents in edge nodes of EONs are able to perform services awareness by obtaining data traffic characteristics from services traffics.Test results show that the proposed approach is feasible and efficient to improve edge computing ability of EONs.

Applications of atomic force microscopy in immunology

Cellular mechanics,a major regulating factor of cellular architecture and biological functions,responds to intrinsic stresses and extrinsic forces exerted by other cells and the extracellular matrix in the microenvironment.Cellular mechanics also acts as a fundamental mediator in complicated immune responses,such as cell migration,immune cell activation,and pathogen clearance.The principle of atomic force microscopy(AFM)and its three running modes are introduced for the mechanical characterization of living cells.The peak force tapping mode provides the most delicate and desirable virtues to collect high-resolution images of morphology and force curves.For a concrete description of AFM capabilities,three AFM applications are discussed.These applications include the dynamic progress of a neutrophil-extracellular-trap release by neutrophils,the immunological functions of macrophages,and the membrane pore formation mediated by perforin,streptolysin O,gasdermin D,or membrane attack complex.

Dynamic fragments awareness based virtual network mapping strategy of elastic optical networks

As virtual networks services emerge increasingly with higher diversification, the issue of spectrum fragments presents great challenge to the elastic optical networks(EON), especially under heaven services burdens. Aimed to solve this problem, this article proposes a dynamic fragments awareness based virtual network mapping(DFA-VNM) strategy of elastic optical network. In this proposed approach, the dynamic fragments awareness model of it is established, which takes available bandwidth demand and spectrum fragment degree into consideration. Moreover, the dynamic fragments awareness based virtual network mapping strategy makes full advantage of real-time fragments awareness result to conduct virtual network service mapping operation with less fragments and lower blocking rate. Testing results show that the proposed approach is able to improved services supporting ability of EON.

Evaluating and Improving Linear Regression Based Profiling:On the Selection of Its Regularization

Side-channel attacks(SCAs)play an important role in the security evaluation of cryptographic devices.As a form of SCAs,profiled differential power analysis(DPA)is among the most powerful and efficient by taking advantage of a profiling phase that learns features from a controlled device.Linear regression(LR)based profiling,a special profiling method proposed by Schindler et al.,could be extended to generic-emulating DPA(differential power analysis)by on-the-fly profiling.The formal extension was proposed by Whitnall et al.named SLR-based method.Later,to improve SLR-based method,Wang et al.introduced a method based on ridge regression.However,the constant format of L-2 penalty still limits the performance of profiling.In this paper,we generalize the ridge-based method and propose a new strategy of using variable regularization.We then analyze from a theoretical point of view why we should not use constant penalty format for all cases.Roughly speaking,our work reveals the underlying mechanism of how different formats affect the profiling process in the context of side channel.Therefore,by selecting a proper regularization,we could push the limits of LR-based profiling.Finally,we conduct simulation-based and practical experiments to confirm our analysis.Specifically,the results of our practical experiments show that the proper formats of regularization are different among real devices.

Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous manner.Therefore,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different contexts.Possible solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are received.However,these mechanisms are characterized by both inefficient data transfer and increased area cost.Good performance of the dataflow architecture depends on the efficiency of data transfer.In order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)mechanism.LAA accelerates the execution flow by speculatively acknowledging ahead without penalties.Our simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

Physical impairments awareness based virtual network mapping strategy of elastic optical networks

As virtual networks services emerge increasingly with higher requirement of flexibility and robust, great complex challenges caused by physical-layer impairments are presented to the elastic optical networks(EON). Aimed to solve this problem, this paper proposes a physical impairment awareness based virtual network mapping stragegy of EON. The physical impairments awareness model is established, including both of linear factors and nonlinear ones. On this basis, this paper proposes a virtual network mapping strategy with detailed procedures, combined with node importance factors during the virtual network mapping procedure. Test results show that the proposed approach is able to reduce blocking rate and enhance services supporting ability of EON.

Hierarchy Bayesian model based services awareness of high-speed optical access networks

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.