Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario,cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients.Resources are in self-administration;consequently,clients can adjust their usage according to their requirements.Resource usage is estimated and clients can pay according to their utilization.In literature,the existing method describes the usage of various hardware assets.Quality of Service(QoS)needs to be considered for ascertaining the schedule and the access of resources.Adhering with the security arrangement,any additional code is forbidden to ensure the usage of resources complying with QoS.Thus,all monitoring must be done from the hypervisor.To overcome the issues,Robust Resource Allocation and Utilization(RRAU)approach is developed for optimizing the management of its cloud resources.The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS.The asset assignment calculation is heuristic,which is based on experimental evaluations,RRAU approach with J48 prediction model reduces Job Completion Time(JCT)by 4.75 s,Make Span(MS)6.25,and Monetary Cost(MC)4.25 for 15,25,35 and 45 resources are compared to the conventional methodologies in cloud environment.

Fault Aware Dynamic Resource Manager for Fault Recognition and Avoidance in Cloud

Fault tolerance(FT)schemes are intended to work on a minimized and static amount of physical resources.When a host failure occurs,the conventional FT frequently proceeds with the execution on the accessible working hosts.This methodology saves the execution state and applications to complete without disruption.However,the dynamicity of open cloud assets is not seen when taking scheduling choices.Existing optimization techniques are intended in dealing with resource scheduling.This method will be utilized for distributing the approaching tasks to the VMs.However,the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure.The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe,but it suffers with higher makespan;poor resource usage and unbalance load concerns.To overcome the above mentioned issue,Fault Aware Dynamic Resource Manager(FADRM)is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement.Proposed FADRM method gives FT a Multi-stage Resilience Manager(MRM)in the client and application layers,and simultaneously decreases the over-head and degradations.It additionally provides safety to the application execution considering the clients,application and framework necessities.Based on experimental evaluations,Proposed Fault Aware Dynamic Resource Manager(FADRM)method 157.5 MakeSpan(MS)time,0.38 Fault Rate(FR),0.25 Failure Delay(FD)and improves 5.5 Performance Improvement Ratio(PIR)for 25,50,75 and 100 tasks and 475 MakeSpan(MS)time,0.40 Fault Rate(FR),1.30 Failure Delay(FD)and improves 6.75 improves Performance Improvement Ratio(PER)for 100,200,300 and 500 Tasks compare than existing methodologies.

Secure android IoT mobile and collaborative machine learning for controlling the management of enterprise

There is a need for increased security measures because of wide variety of android Internet of Thing(IoT)mobile devices that can communicate with each other via networks for controlling the management of enterprise.Elliptic Curve Deffie Hellman(ECDH)and Rivest Shamir Adleman(RSA)are used to secure data in android IoT phones in efficient manner.Android mobile can store a lot of data,including sensitive data.Protecting data saved on mobile has become a critical problem.In android IoT,Collaborative Machine Learning describes a method for collaboratively mining data,which makes it easier to manage and lowers cost of maintenance.To increase security in IoT phones,suggested system uses ECDH,RSA,and CML algorithms,which have been considered novelty of this method.RSA and ECDH are computed using time of decryption,encryption,and key generation.Conclusions show ECDH beats other alternatives like RSA.Finally,all users of the network have been tested.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Residual Convolutional Graph Neural Network with Subgraph Attention Pooling

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational complexity.However,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification features.In this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features losing.Particularly,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph classification.Our strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual connection.By feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level representation.The messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for classification.Our experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

Power-aware transceiver design for half-duplex bidirectional chip-to-chip optical interconnects

A power-aware transceiver for half-duplex bidirectional chip-to-chip optical interconnects has been designed and fabricated in a 0.13 μm complementary metal-oxide-semiconductor （CMOS） technology. The transceiver can detect the presence and absence of received signals and saves 55% power in Rx enabled mode and 45% in Tx enabled mode. The chip occupies an area of 1.034 mm2 and achieves a 3-dB bandwidth of 6 GHz and 7 GHz in Tx and Rx modes, respectively. The disabled outputs for the Tx and Rx modes are isolated with 180 dB and 139 dB, respectively, from the enabled outputs. Clear eye diagrams are obtained at 4.25 Gbps for both the Tx and Rx modes.