DYNAMIC CHARACTERISTICS OF SOILS SUBJECT TO IMPACT LOADINGS

The dynamic properties of soil under impact loads are studied experimentally and numerically. By analyzing the microstructural photos of soils with and without impact, it is shown that impact loads can destroy the original structures in the compact area, where the soil grains are rearranged regularly and form the compact whirlpool structure. Simultaneously, the dynamic impact process of soil is simulated by using the software of Ls-dyna. The time-dependent distribution of the dynamic stress and density is obtained in the soil. Furthermore, the simulation results are consistent with the experimental results. The reinforcement mechanism and the rule of dynamic compaction of soils due to impact load are also elucidated.

Virtual machine selection and placement for dynamic consolidation in Cloud computing environment

Dynamic consolidation of virtual machines （VMs） in a data center is an effective way to reduce the en- ergy consumption and improve physical resource utilization. Determining which VMs should be migrated from an over- loaded host directly influences the VM migration time and increases energy consumption for the whole data center, and can cause the service level of agreement （SLA）, delivered by providers and users, to be violated. So when designing a VM selection policy, we not only consider CPU utilization, but also define a variable that represents the degree of resource satisfaction to select the VMs. In addition, we propose a novel VM placement policy that prefers placing a migratable VM on a host that has the minimum correlation coefficient. The bigger correlation coefficient a host has, the greater the in- fluence will be on VMs located on that host after the migra- tion. Using CloudSim, we run simulations whose results let draw us to conclude that the policies we propose in this pa- per perform better than existing policies in terms of energy consumption, VM migration time, and SLA violation per- centage.

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)-intensive workload and some for web application workload.Therefore,an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms.Neat is an open-source dynamic VM consolidation framework,which is well integrated into OpenStack.However,it cannot conduct dynamic algorithm scheduling,and VM consolidation algorithms in Neat are few and basic,which results in low performance for energy saving and Service-Level Agreement(SLA)avoidance.In this paper,an Intelligent Neat framework(I-Neat)is proposed,which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system.The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms.The algorithm library is designed based on a template,and in addition to the algorithms of Neat,I-Neat adds six new algorithms to the algorithm library.Furthermore,the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code.Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.