Earticle

In the most of case In-building communication lines have a Optical fiber Cable. with this fiber, residers are consume so much data therefore make traffic increase every year for example CCTV, Security Lines, CableTV etc. Sometimes the residers are moving-in or moving-out, the other time they are change service provider. for this purpose We develop a ineuron system which automatically check the fiber cable and Facility Management. and this system software drives on Virtual Machine Server.

Multi-access Edge Computing (MEC) has emerged as a novel and efficient technology to enable a new breed of time sensitive applications in the 5G era. By installing small computing infrastructures at the network edges, it solves the current centralized structure problem of the cloud infrastructure (i.e., high end-to-end communication latency between a user equipment and the cloud). As users move across different sites over time, continuous seamless service support to the users is also required. A service migration in 5G MEC is a promising approach for continuous seamless service support by migrating active services to a new MEC host near the current user location. In this paper, a comparison result for the service migration implemented either in a virtual machine or container is presented. The motivation behind this paper is to understand the fundamental performance differences between the virtual machine and container for the service migration in 5G MEC.

클라우드 컴퓨팅 환경에서 단일 서버는 자원 효율화를 위해 다수의 가상 머신을 수용한다. 에너지와 자원 효율화 정 책에 따라서 서버에 가상 머신을 배치해야 한다. 본 논문은 체온 손실을 최소화하려는 황제 펭귄의 행동에 기반한 가상 머신 배치 알고리즘을 제안한다. 가장 큰 열손실을 겪은 펭귄은 강한 외부의 바람을 피하기 위해서 지속적으로 집단 내에서 이동한다. 제안한 알고리즘은 먼저 전력 소비를 절감하기 위해서 가상 머신을 배치하되, 열섬이 발생할 것으로 예상되는 서버에 있는 가상 머신은 온도가 낮은 다른 서버로 이주시킨다. 본 논문은 서버와 랙 단위의 가상 머신 배치 알고리즘을 제안한다. 시뮬레이션 결과 제안한 두 알고리즘은 기존 알고리즘과 유사한 수준으로 전력을 소비하면서 가상 머신이 더 분산되어 배치되었다. 랙 단위의 가상머신 배치 알고리즘을 적용한 경우, 서버별 전력 소비의 편차가 가장 적어 열섬 방지에 더 우수한 것으로 확인되었다.

In cloud computing environment, a single server accommodates multiple virtual machines for resource efficiency. It is necessary to place virtual machines on servers according to the energy and resource efficient policy. This paper proposes a virtual machine placement (VMP) algorithm based on the emperor penguins’ behavior, which attempts to minimize loss of penguins’ body temperature. Penguins with the greatest heat loss continuously move in their group against strong external winds. The proposed algorithm attempts to place virtual machines on servers for minimizing power consumption, and periodically migrate virtual machines on the server, which possibly occurs a heat island, to a server with low temperature. This paper proposes server-based and rack-based VMP algorithms. Simulation results show that the two proposed algorithms consumed server power similar to the existing algorithm while virtual machines were placed in a mostly distributed way. In case of applying the rack-based VMP, the difference in power consumption among servers is the smallest so that this algorithm is considered to be the most suitable for preventing the heat island.

Virtual Desktop Infrastructure (VDI) based on virtual computing laboratories (VCL) model has been implemented in a small or medium scale on cloud computing in universities. Conventional physical computing laboratories with thousands of PCs are still widely used and co-exist with the VCL, however, the conventional PCs are not fully utilized in VCL. The demand of VDI is growing but the numbers of VDIs are bounded by the capacities of host servers. To have scale-up VDI, system feasibility in terms of functionality and performance and scalability of virtual machines should be tested. We propose an extended model of VCL that can scale-up VDIs by running on an integrated virtual and physical lab environment. End-users can build their own workspaces on a host server and migrate to PCs in physical lab. The paper concludes with discussions on the benefits, problems, and performances of the extended model of the VCL.

Internet-based virtual computing environments (iVCE) are open, anonymous and dynamic in nature. Such characteristics bring about threats and vulnerabilities in providing trusted services and improving resource utilization. Therefore, a dynamic trust model for distinguishing service and recommendation is proposed. In this paper, we analyze multidimensional decision factors related to the evaluation of autonomous node, such as user satisfaction, reward function, punishment function and time decay function. According to the network connection degree of node, our model assigns a new trust weight that specifically describes the relationship between network and trust in iVCE. We then propose a dynamic quantitative model for measuring different kinds of trust. Simulation results indicate that our model can effectively cope with malicious behavior and exhibits evident advantages in resource utilization compared with existing models.

High Performance Computing (HPC) needs enormous amounts of computing power for short periods of time. While High Throughput Computing (HTC) is suitable for huge number of jobs with considerable execution time. Many academic as well as industry researchers use HTC based computing resource for scientific or computing applications to simulate and calculate their results. In order to reduce power consumption and increase the utilization of computing resources, virtualization is one of the options. While general performance on virtual environment is not better than physical machine environment because of hypervisor overhead. In this paper, we present the performance difference of several configurations of CPU and Job running time between physical based HTCondor cluster and Virtualized HTCondor cluster environment.

Cloud computing is the delivers the computing services over the internet. Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. Virtual Machine (VM) is an emulation of a particular computer system. In cloud computing, Virtual machine migration is a useful tool for migrating Operating System instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. There are various techniques and parameters available for VM migration. This paper presents the various virtual machine migration techniques.

In cloud computing with host virtualization, it is necessary to understand the queue of storage for disk requests due to the competition for the storage requirements of Virtual Machine (VM). In this paper, we address schemes, sequence and dynamic balance allocation, for handling the VM queue to access the virtual storage device. SimPy, python-based simulation tool, is adopted to build the simulation models to get analytical data. The SimPy simulator incorporates the resource analyzing.

Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. Data center of this cloud computing has huge number of resources and list of applications (with different architecture, configuration and requirement for deployment) want to use those resource. Cloud computing environment uses virtualization concept and provides resources to application by creating and allocating virtual machine to specific application. There for resource allocation policies and load balance policies play very vital role in allocating and managing the resources among various application in clod computing life cycle. CloudSim is an extensible simulation toolkit that enables modeling and simulation of Cloud computing environments. The model proposed by this paper for dynamic load balance policy with considering different attributes and different service level agreements in cloud computing environment helps this environment to utilize their resources and improves performance. The proposed model uses Hungarian algorithm and the result is verified by simulating this model using CloudSim.

Load balancing of virtual machines is one of the most significant issues in cloud computing research. A common approach is to employ intelligent algorithms such as Ant Colony Optimization (ACO). However, there are two main issues with traditional ACO. First, ACO is very dependent on the initial conditions, which might affect the final optimal solution and the convergence speed. To solve this problem, we propose to employ Genetic Algorithm (GA) for ACO initialization. Second, ACO could arrive at local optimal point, and the convergence speed is typically low. Along this line, we introduce the idea of Simulated Annealing (SA) to avoid local optimal and accelerate the convergence. Lastly, our experiments show that our improved ACO achieves good performance in load balancing.

The energy consumption is one of the most important factors in the virtual machines deployment in the current data centres. Various studies proved that the energy aware management of the virtual machines can reduce the total energy consumption about tens of percents. We developed the new approach, based on the distributed algorithm, which is able to consolidate the virtual machines between various virtualization nodes without the central coordinator. The input data for this algorithm is collected online from the electronic wattmeters, which are placed before the energy input of each virtualization node.

In social media services and social network services, it is necessary to collect, analyze and process their big data with low maintenance cost. Therefore, distributed-parallel data processing on cloud platform is getting spotlight as useful solution for them. In this paper, we present a new architecture of DIVE-C: DIstributed-parallel Virtual Environment on Cloud computing platform for distributed parallel data processing applications which offers a transparent virtual computing environment in order to provide a way easy to launch user’s distributed parallel applications. It hides the complexity of the cloud, and helps users to focus on their new applications and core services. DIVE-C uses agent-based resource management scheme to configure VM resources and application deployment for offering various distributed-parallel application models. VM resources are automatically provided by unified cloud management layer. Furthermore, an easy-to-use web interface of DIVE-C offers convenience to users. We implemented a prototype of DIVE-C, and its experiment results show the competitive performance of DIVE-C for dynamic resource and virtual computing environment provisioning for various data processing models.

Cloud resource managers face many problems, such as the dynamic changes of incoming load and demand elasticity of resources. From the aspect of elastic configuration management technology of virtual resources, this paper focus on how to provide quick and reliable cloud resources for users. Virtual machine resources automatic configuration management technology is proposed in this paper, the reverse reinforcement learning technique is introduced into cloud virtual resource management, configuration management process of the virtual machine is modeled as a Markov decision model. According to the running state of the application system and the dynamic changes of the input load, this technology can make an automatic decision to add or remove a number of virtual machines. Experimental results show that this technology can complete the tasks of automating configuration of virtual resource management according to the changing load, respond to end user's in a timely manner, and ensure the SLA requirements of cloud users.

The virtual resource scheduling is an important topic in the field of cloud computing. Based on particle swarm scheduling algorithm, this paper introduces cellular automata theory to construct a new cellular particle swarm scheduling algorithm. This approach through mathematical modeling for virtual resource scheduling of cloud computing and complete the final search configuration based on directional optimization objective function. Experimental results show that the proposed method has more excellent scheduling performance, in the case of changes in resources, can be also kept stable scheduling balance.

A variety of faults may cause performance degradation or even downtime of virtual machines (VMs) under Cloud environment, thus lowering the dependability of Cloud platform. Detecting anomalous VMs before real failures occur is an important means to improve the dependability of Cloud platform. Since the performance or state of VMs may be affected by the environmental factors, this article proposes an environment-aware anomaly detection framework (termed EaAD) for VMs under Cloud environment. EaAD partitions all the VMs in Cloud platform into several monitoring domains based on similarity in running environment, which makes the VMs in a same monitoring domain have similar running environment. In each domain, the equipped anomaly detection algorithm detects anomalous VMs based on their performance metrics. In addition, anomaly detection in a certain monitoring domain faces such challenges as multiple anomaly categories, imbalanced training sample sets, increasing number of training samples. To cope with these challenges, several support vector machine (SVM) based anomaly detection algorithms are implemented and equipped in EaAD, including C-SVM, OCSVM, multi-class SVM, imbalanced SVM, online learning SVM. This article conducts experiments on EaAD to test the performance of the adopted detection algorithms and looks into future work.

Infrastructure as a Service (IaaS) is one of the three important fundamental service models provided by cloud computing. It provides users with computing resource and storage resource in terms of virtual machine instances. Because of the rapid development of cloud computing, more and more application systems have been deployed on the IaaS cloud computing platforms. Therefore, once anomalies incur in the IaaS cloud computing platforms, all the application systems cannot work normally. In order to enhance the dependability of IaaS cloud computing platform, a virtual machine instance anomaly detection system is proposed for IaaS cloud computing platform to detect virtual machine instances that exhibit abnormal behaviors. The proposed virtual machine instance system consists of four modules that are the data collection, the data transmission, the data storage, and the anomaly detection. In order to reduce the computing complexity and improve the detection precision, the anomaly detection module introduces the principal components analysis to reprocess the collected data and then adopts the Bayesian decision theory to detect the abnormal data. Experimental results show that the proposed virtual machine instance anomaly detection system is effective.

Existing data centers are each individually owned and operated by a single entity. This situation creates an excessive financial burden upon each entity through the need to over-provision for hardware. While this state of affairs enables a secure and an efficient maintenance scheme for the data center, the financial drawbacks are perhaps excessive. To address the concern, we propose the idea of a cooperative virtual data center among multiple entities that is founded on the principal of fair resource sharing amongst the entities.

A major challenge facing cloud computing is virtual resource allocation with dynamic characteristics. Evaluation of a resource allocation strategy using a single aspect can no longer meet the real world demands. We resolve this issue from the perspectives of users and resource providers using a particle swarm algorithm for resource allocation. With this algorithm, we establish an allocation model using the shortest task completion time and the lowest cost as the constraints. The fast convergence rate of the particle swarm algorithm is then used to find the optimal solution for resource allocation. The velocity weight of each particle is self-adaptively adjusted based on the fitness value of each particle, resulting in an improvement in the global optimization and convergence capabilities. Finally, a simulation with the CloudSim platform shows that this algorithm can take into account the completion time and cost, which ensures the minimum cost in the shortest possible time to complete the task to improve resource utilization.

How to satisfy the users’ QoS requirements while improving the resource utilization is one of the key technologies in cloud computing environment. In our work, a virtual cloud resource allocation model VCRAM-U (Utility-based Virtual Cloud Resource Allocation Model) is proposed. In our model, the problem of virtual cloud resources allocation is abstracted as a utility-maximization problem, taking tradeoffs between the utility of the data center and the performance of the applications into account, and maximizing the utility on the premise of meet user’s performance. We design a local decision algorithm and a global decision algorithm to solve our model. Experimental results show that the virtual cloud resources can be managed and allocated efficiently by our model and algorithms. In addition, our model can get a higher utility of the data center compared with other models.