A Systematic Literature Review on Virtual Machine Consolidation

Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.

A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

Virtual Machine Consolidation using Roulette wheel selection Strategy

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. Virtual Machine [VM] consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/under load detection, VM selection and VM placement. In Our Proposed Model We are going to use Roulette-Wheel Selection Strategy, Where the VM selects the Instance type and Physical Machine [PM] using Roulette-Wheel Selection Mechanism Keywords—searchable encryption, dynamic update, cloud computing

Study on the mud swimming motion of Paramisgurnus dabryanus

In the context of the rapid development of bionic technology, inspired by the swimming behavior of fish, a variety of robotic fish have been designed and applied to different underwater works and even military applications. However, in some operations, such as detection and salvage, vehicles need to travel under mud, a medium that is different from fluids. This complicating factor put higher requirements on robotic fish design. In this study, Paramisgurnus dabryanus, a fish species adept at swimming into the mud, was taken as a research object to investigate its profile and mud swimming behavior. First, a three-dimensional (3D) image scanner is used for profile scanning to acquire the point cloud data of the profile features of the loach. After modification, data coordinate points are extracted and used to fit the profile curve of loach and build geometric and mathematical models by means of Fourier function fitting. The next step includes the analysis of the motion of loach, determination of main parameters of the wave equation, and establishment of the fish body wave curve of a loach in the swimming using MATLAB software. Saturated mud having a water content of 37% is adopted as an environmental medium to numerically simulate the swimming behavior in mud, identifying the distribution of vortex path, and velocity field of loach’s motion. The rationality of simulation results is verified by the loach mud swimming test, and the simulating results agree well with the experimental data. This study lays a preliminary foundation for the outer contour design of the robotic fish operating under mud and aims to carry out the drag reduction and accelerating design of the robotic fish. The robotic loach may be applied in fishery breeding, shipwreck salvage operations, and so on.

Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.