scholarly journals User-space process virtualization in the context of checkpoint-restart and virtual machines

2014 ◽  
Author(s):  
Kapil Arya
Keyword(s):  
Virtual Machines ◽  
Process Virtualization ◽  
User Space

scholarly journals A Multiserver User-space Unikernel for a Distributed Virtualization System

2019 ◽  
Author(s):  
Pablo Pessolani
Keyword(s):  
Service Oriented Architecture ◽  
Virtual Machines ◽  
Management Tools ◽  
Resource Requirements ◽  
Service Oriented ◽  
Small Footprint ◽  
Memory Network ◽  
User Space ◽  
Attack Surface ◽  
And Performance

Nowadays, most Cloud applications are developed using Service Oriented Architecture (SOA) or MicroService Architecture (MSA). The scalability and performance of them is achieved by executing multiple instances of its components in different nodes of a virtualization cluster. Initially, they were deployed in Virtual Machines (VMs) but, they required enough computational, memory, network and storage resources to hold an Operating System (OS), a set of utilities, libraries, and the application component. By deploying hundreds of these application components, the resource requirements increase a lot. To minimize them, usually small footprint OS are used. Later, as management tools were improved, the application components began to be deployed in Containers which require even less resources than VMs. Another way to reduce the resource requirements is integrating the application components in a Unikernel. This article proposes a Unikernel called MUK, based on a multiserver OS, to be used as a tool to integrate Cloud application components. MUK was built to run in user-space inside a Container of a Distributed Virtualization System. Both technologies facilitate the scattering of application components in a virtualization cluster keeping the isolation properties and minimal attack surface of a Unikernel.

SPDK Vhost-NVMe: Accelerating I/Os in Virtual Machines on NVMe SSDs via User Space Vhost Target

2018 ◽  
Author(s):  
Ziye Yang ◽  
Changpeng Liu ◽  
Yanbo Zhou ◽  
Xiaodong Liu ◽  
Gang Cao
Keyword(s):  
Virtual Machines ◽  
User Space

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

A Method of Shared File Cache for File Clone Function to Improve I/O Performance for Virtual Machines

2016 ◽  
Vol 136 (6) ◽  
pp. 858-867
Author(s):  
Hitoshi Kamei ◽  
Osamu Yashiro ◽  
Takaki Nakamura
Keyword(s):  
Virtual Machines ◽  
Shared File ◽  
File Cache

An Adaptive Traffic-Aware Migration Algorithm Selection Framework in Live Migration of Multiple Virtual Machines

2020 ◽  
Vol 16 (2) ◽  
pp. 314
Author(s):  
Cui Yong ◽  
Zhu Liang ◽  
Cai Zengyu ◽  
Hu Ying
Keyword(s):  
Virtual Machines ◽  
Live Migration ◽  
Algorithm Selection ◽  
Selection Framework

A Methodology for Redistributing the Operating Virtual Machines among the Servers in a Data Center

Vestnik MEI ◽  
2018 ◽  
pp. 98-105
Author(s):  
Alexander A. Larin ◽  
◽  
Leonid I. Abrosimov ◽  
Keyword(s):  
Data Center ◽  
Virtual Machines

Allocation of Virtual Machines to the Cloud Network Using the Barking-Deer Approach

2020 ◽  
Author(s):  
Himadri Biswas ◽  
Sudipta Sahana ◽  
Priyajit Sen ◽  
Debabrata Sarddar
Keyword(s):  
Virtual Machines ◽  
Cloud Network

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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