scholarly journals Virtual machines migration game approach for multi-tier application in infrastructure as a service cloud computing

IET Networks ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 326-337
Author(s):  
Khiet Thanh Bui ◽  
Hung Dac Ho ◽  
Tran Vu Pham ◽  
Hung Cong Tran
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Infrastructure As A Service ◽  
Service Cloud

scholarly journals Cloud-based Network Virtualization in IoT with OpenStack

2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Author(s):  
Zakaria Benomar ◽  
Francesco Longo ◽  
Giovanni Merlino ◽  
Antonio Puliafito
Keyword(s):  
Cloud Computing ◽  
Overlay Networks ◽  
Virtual Machines ◽  
Cloud Model ◽  
Network Virtualization ◽  
Bare Metal ◽  
Infrastructure As A Service ◽  
Model Network ◽  
The Core ◽  
Geographically Distributed

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

scholarly journals Taxonomy of resource allocation algorithms for inner IaaS cloud data centres

2013 ◽  
Author(s):  
Dang Minh Quan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Computer Hardware ◽  
Infrastructure As A Service ◽  
Cloud Data ◽  
Cloud Infrastructures ◽  
Allocation Algorithms ◽  
Data Centres ◽  
Cloud User

Cloud computing has become more and more popular  with  the  widely  deployment  of  several  cloud infrastructures.  Infrastructure-as-a-service  (IaaS) Cloud  computing  replaces  bare  computer hardware. The cloud user  will use the virtual  machines (VMs)  to  fullfil  their  computing  requirements.  Among the  components  of  IaaS  cloud  software  stack,  the resource  allocation  module  is  very  important  as  it selects suitable VMs and the place to execute VMs. This paper  focuses  on  studying  and  classifying  algorithms used  in  the  resource  allocation  module.  The  issues  of how to apply those algorithms are also discussed.

scholarly journals Analisis Perbandingan Kinerja Layanan Infrastructure As A Service Cloud Computing Pada Proxmox dan Xenserver

2019 ◽  
Vol 19 (1) ◽  
pp. 9-16
Author(s):  
Surahmat Surahmat ◽  
Alfred Tenggono
Keyword(s):  
Cloud Computing ◽  
Network Traffic ◽  
Computing System ◽  
Memory Usage ◽  
Infrastructure As A Service ◽  
Benchmark Testing ◽  
Computing Services ◽  
Cloud Computing System ◽  
Cloud Computing Services ◽  
Service Cloud

Cloud computing is a technology that utilizes the internet as a service so that it does not use too many physical computers because the cloud computing system uses a virtual system where by using a computer alone it can provide services to many computers at the same time with a virtualization system. Research conducted by researchers focuses on comparing the performance of Infrastructure as a Service Cloud Computing services with the aim to finding out the strengths and weaknesses of the two types of software with type 1 hypervisor technology, namely Proxmox and Xenserver. The method used in this study is to use action research. The testing scheme conducted in this study is testing CPU load and memory usage, benchmark testing, network traffic testing, and retrieval of service user responsses. The results of testing on Xenserver and Proxmox get good responsse results for CPU load and memory usage testing, benchmark testing, network traffic testing then for responsse results from users Proxmox get a total value of 3848 and Xenserver 3739.

Mission Operations as a Service: Cloud Computing for Space Missions beyond Infrastructure-as-a-Service

2014 ◽  
Author(s):  
Mehran Sarkarati ◽  
Mario Merri ◽  
Mariella Spada ◽  
Vicente Navarro ◽  
Jiri Marak ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Space Missions ◽  
Infrastructure As A Service ◽  
Mission Operations ◽  
Service Cloud

scholarly journals The enhancement of logging system accuracy for infrastructure as a service cloud

2020 ◽  
Vol 9 (4) ◽  
pp. 1558-1568
Author(s):  
Surapong Wiriya ◽  
Winai Wongthai ◽  
Thanathorn Phoka
Keyword(s):  
Virtual Machines ◽  
Main Memory ◽  
The Novel ◽  
File Size ◽  
Infrastructure As A Service ◽  
Energy Investment ◽  
Design And Implementation ◽  
Service Cloud ◽  
Better Than

We introduce the novel technical results of the enhanced logging system for customer virtual machines (VMs) in an Infrastructure as a Service (IaaS) cloud. The main contribution is that the enhanced system can work with a better system's accuracy and speed, with the simplicity of the design and implementation. We measure the accuracy of the unenhanced logging system, then find a quick solution to enhance the system based on the results of the measurement. To measure and enhance the unenhanced system, we increase the main memory and CPU cores of the VMs then collect the accuracy results from each increment configuration. We analyze the results and propose to use the taskset tool to enhance the accuracy of the system. Found three main findings include: firstly, the accuracy of the enhanced system is about 20% on maximum better than the unenhanced one;  the enhanced system accuracy becomes 100%; lastly, the enhanced system can detect a file with the smaller file size as almost 12% smaller. The findings can be a basis to design the logging systems in an IaaS cloud, to decrease hardware and energy investment. To the best of our knowledge, the contribution and findings are not in the literature.

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