GPU Virtualization using PCI Direct Pass-Through

2013 ◽  
Vol 311 ◽  
pp. 15-19 ◽  
Author(s):  
Hee Seung Jo ◽  
Myung Ho Lee ◽  
Dong Hoon Choi
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Gpu Programming ◽  
Processing Unit ◽  
Computing Environment ◽  
Virtual Machine Monitor ◽  
Cloud Computing Environment ◽  
Pass Through ◽  
Gpu Virtualization

Machine virtualization and cloud computing environment have highlighted for last several years. This trend is based on the endeavor to enhance the utilization and reduce the ownership cost of machines. On the other hand, in aspect of high performance computing, graphics processing unit (GPU) has proved its capability for general purpose computing in many research areas. Evolving from traditional APIs such as the OpenGL and the Direct3D to program GPU as a graphics device, the CUDA of NIVDIA and the OpenCL provide more general programming environment for users. By supporting memory access model, interfaces to access GPUs directly and programming toolkits, users can perform parallel computation using the hundreds of GPU cores. In this paper, we propose a GPU virtualization mechanism to exploit GPU on virtualized cloud computing environment. Differently from the previous work which mostly reimplemented GPU programming APIs and virtual device drivers, our proposed mechanism uses the direct pass-through of PCI-E channel having GPU. The main limitation of previous approaches is virtualization overhead. Since they were focused on the sharing of GPU among virtual machines, they reimplemented GPU programming APIs at virtual machine monitor (VMM) level, and it incurred significant performance overhead. Moreover, if APIs are changed, they need to reengineer the most of APIs. In our approach, bypassing virtual machine monitor layer with negligible overhead, the mechanism can achieve similar computation performance to bare-metal system and is transparent to the GPU programming APIs.

scholarly journals A Hybrid Technique for Server Consolidation in Cloud Computing Environment

2020 ◽  
Vol 20 (1) ◽  
pp. 36-52
Author(s):  
C. Vijaya ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Hybrid Technique ◽  
Computing Environment ◽  
Placement Problem ◽  
Cloud Computing Environment ◽  
Server Consolidation ◽  
Memory Utilization

AbstractThe goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

scholarly journals Study of Immune-Based Intrusion Detection Technology in Virtual Machines for Cloud Computing Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Virtual Machine ◽  
Virtual Machines ◽  
The State ◽  
Signal Acquisition ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Model ◽  
Detection Technology

Cloud computing platforms are usually based on virtual machines as the underlying architecture; the security of virtual machine systems is the core of cloud computing security. This paper presents an immune-based intrusion detection model in virtual machines of cloud computing environment, denoted as IB-IDS, to ensure the safety of user-level applications in client virtual machines. In the model, system call sequences and their parameters of processes are used, and environment information in the client virtual machines is extracted. Then the model simulates immune responses to ensure the state of user-level programs, which can detect attacks on the dynamic runtime of applications and has high real-time performance. There are five modules in the model: antigen presenting module, signal acquisition module, immune response module, signal measurement module, and information monitoring module, which are distributed into different levels of virtual machine environment. Performance analysis and experimental results show that the model brings a small performance overhead for the virtual machine system and has a good detection performance. It is applicable to judge the state of user-level application in guest virtual machine, and it is feasible to use it to increase the user-level security in software services of cloud computing platform.

scholarly journals Bin packing algorithms for virtual machine placement in cloud computing: a review

2019 ◽  
Vol 9 (1) ◽  
pp. 512 ◽  
Author(s):  
Kumaraswamy S ◽  
Mydhili K Nair
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Future Research ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

<p>Cloud computing has become more commercial and familiar. The Cloud data centers havhuge challenges to maintain QoS and keep the Cloud performance high. The placing of virtual machines among physical machines in Cloud is significant in optimizing Cloud performance. Bin packing based algorithms are most used concept to achieve virtual machine placement(VMP). This paper presents a rigorous survey and comparisons of the bin packing based VMP methods for the Cloud computing environment. Various methods are discussed and the VM placement factors in each methods are analyzed to understand the advantages and drawbacks of each method. The scope of future research and studies are also highlighted.</p>

scholarly journals Privacy Preserving Auction Based Virtual Machine Instances Allocation Scheme for Cloud Computing Environment

2017 ◽  
Vol 7 (5) ◽  
pp. 2645
Author(s):  
Suneeta Mohanty ◽  
Prasant Kumar Pattnaik ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Cloud Service ◽  
Computing Environment ◽  
Cloud Service Provider ◽  
Cloud Computing Environment ◽  
The Individual ◽  
Cloud Users ◽  
Yao’S Protocol

<p>Cloud Computing Environment provides computing resources in the form of Virtual Machines (VMs), to the cloud users through Internet. Auction-based VM instances allocation allows different cloud users to participate in an auction for a bundle of Virtual Machine instances where the user with the highest bid value will be selected as the winner by the auctioneer (Cloud Service Provider) to gain more. In this auction mechanism, individual bid values are revealed to the auctioneer in order to select the winner as a result of which privacy of bid values are lost. In this paper, we proposed an auction scheme to select the winner without revealing the individual bid values to the auctioneer to maintain privacy of bid values. The winner will get the access to the bundle of VM instances. This  scheme relies on a set of cryptographic protocols including Oblivious Transfer (OT) protocol and Yao’s protocol to maintain privacy of bid values.</p>

A study on distributed transcoding using dynamic allocation of virtual machines in a cloud computing environment

2015 ◽  
Vol 17 (2) ◽  
pp. 113-120 ◽  
Author(s):  
Seokmo Gu ◽  
Aria Seo ◽  
Yei-chang Kim
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
High Efficiency ◽  
Virtual Machines ◽  
Dynamic Allocation ◽  
Computing Environment ◽  
High Efficiency Video Coding ◽  
Cloud Computing Environment ◽  
Content Type ◽  
Network Bandwidth

Purpose – The purpose of this paper is a transcoding system based on a virtual machine in a cloud computing environment. There are many studies about transmitting realistic media through a network. As the size of realistic media data is very large, it is difficult to transmit them using current network bandwidth. Thus, a method of encoding by compressing the data using a new encoding technique is necessary. The next-generation encoding technique high-efficiency video coding (HEVC) can encode video at a high compressibility rate compared to the existing encoding techniques, MPEG-2 and H.264. Yet, encoding the information takes at least ten times longer than existing encoding techniques. Design/methodology/approach – This paper attempts to solve the tome problem using a virtual machine in a cloud computing environment. Findings – In addition, by calculating the transcoding time of the proposed technique, it found that the time was reduced compared to existing techniques. Originality/value – To this end, this paper proposed transcoding appropriate for the transmission of realistic media by dynamically allocating the resources of the virtual machine.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

scholarly journals Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

2021 ◽  
Vol 9 (10) ◽  
pp. 267-296
Author(s):  
Pritam Patange
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Disk File ◽  
Virtual Machine Monitor ◽  
Image File ◽  
File Formats ◽  
Physical Infrastructure ◽  
Disk Image ◽  
The Impact

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

Virtual Machine Placement Using Statistical Mechanism in Cloud Computing Environment

2018 ◽  
Vol 9 (3) ◽  
pp. 23-31
Author(s):  
Narander Kumar ◽  
Surendra Kumar
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machine ◽  
Minimum Cost ◽  
Virtual Machine Placement ◽  
The Internet ◽  
Computing Environment ◽  
Assignment Problems ◽  
Cloud Computing Environment ◽  
Statistical Mechanism

The internet has become essential and is the basis of cloud computing and will continue to be in the future. Best resource allocation is a process of placing the resources at their minimum cost/time and minimizes the load to a virtual machine. In this article, the authors propose an algorithm to optimize assignment problems and get the best placements in the resources to maintain the load on the virtual machine. Further, they also make comparisons between various optimization mechanisms for assignment problems, which is formulated for the cloud in virtual machine placement.

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