Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy

The processed surface contour shape is extracted with the finite element simulation software.The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain.The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology.The surface roughness prediction model is established and optimized.Finally,the surface roughness finite element simulation prediction model is verified by experiments.The research results show that,compared with the experiment results,the error range of the finite element simulation model is 27.5%–30.9%,and the error range of the empirical model obtained by the response surface method is between 4.4%and 12.3%.So,the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration.

MULTI-HOP RELAYING IN 5G： FROM RESEARCH TO SYSTEMS, STANDARDS, AND APPLICATIONS

In recent years,the demands of high traffic transmission motivate the rapid development of wireless access techniques,and it becomes promising to design the fifth generation(5G)wireless networks.Essential requirements for 5G involve higher traffic volume,indoor or hotspot traffic,and spectrum,energy,and cost efficien-

Towards Yo-Yo attack mitigation in cloud auto-scaling mechanism

Cloud platforms could automatically scale underlying network resources up and down in response to changes in the traffic load.Such an auto-scaling mechanism can largely enhance the elasticity and scalability of cloud platforms.However,it may introduce new security threats.For example,the Yo-Yo attack is a newly disclosed attack against the cloud auto-scaling mechanism.Attackers periodically send bursts of traffic to cause the autoscaling mechanism to oscillate between the scale-up process and the scale-down process,which may result in significant performance degradation and economic loss.None of the prior work addressed the problem of mitigating such an attack.In this paper,we propose a Trust-based Adversarial Scanner Delaying(TASD)approach to effectively and proactively mitigate the Yo-Yo attack on the cloud auto-scaling mechanism.In TASD,we first propose to use the trust-based scheme to establish trust values for users,which is leveraged to identify adversarial requests.Trust values are updated by jointly considering the request mode and the auto-scaling status.Then,we aim to disable the condition under which the Yo-Yo attack takes effect by injecting certain delay,under the QoS constraints,to manipulate the response time of suspicious requests and deceive the attackers.Our extensive evaluation demonstrates that our approach achieves promising results,e.g.,it can detect at least 80%Yo-Yo adversarial users and reduce more than 41%malicious scale-ups.

Virtual network embedding for power savings of servers and switches in elastic data center networks

Currently, the elastic interconnection has realized the high-rate data transmission among data centers(DCs). Thus, the elastic data center network(EDCN) emerged. In EDCNs, it is essential to achieve the virtual network(VN) embedding, which includes two main components: VM(virtual machine) mapping and VL(virtual link) mapping. In VM mapping, we allocate appropriate servers to hold VMs. While for VL mapping,an optimal substrate path is determined for each virtual lightpath. For the VN embedding in EDCNs, the power efficiency is a significant concern, and some solutions were proposed through sleeping light-duty servers.However, the increasing communication traffic between VMs leads to a serious energy dissipation problem, since it also consumes a great amount of energy on switches even utilizing the energy-efficient optical transmission technique. In this paper, considering load balancing and power-efficient VN embedding, we formulate the problem and design a novel heuristic for EDCNs, with the objective to achieve the power savings of servers and switches. In our solution, VMs are mapped into a single DC or multiple DCs with the short distance between each other, and the servers in the same cluster or adjacent clusters are preferred to hold VMs. Such that, a large amount of servers and switches will become vacant and can go into sleep mode. Simulation results demonstrate that our method performs well in terms of power savings and load balancing. Compared with benchmarks, the improvement ratio of power efficiency is 5%–13%.