A survey on virtualization techniques in Mobile edge computing

2021 ◽  
pp. 455-468
Author(s):  
Durga Chouhan ◽  
Nilima Gautam ◽  
Gaurav Purohit ◽  
Rajesh Bhdada

In the present scenario, the field of Information Technology(IT) is moving from physical storage to cloud storage as "cloud" providers deliver on-demand resources over the Internet. MEC's key idea is to provide an IT infrastructure system and cloud computing services at the mobile network's edge, within the RAN and close to mobile users. MEC expands the idea of cloud computing by taking the benefits of the cloud closer to consumers in the form of a network edge, resulting in less latency from end to end. It is a decentralized computing infrastructure where some applications use signal processing, storage, control and computing are distributed between the data source and the cloud in the most effective and logical way. Virtualization is the main cloud infrastructure technology used in MEC. Virtualization is accomplished by virtualizing the software or hardware resource layer. Virtualization in MEC can be done by the hypervisor, Virtual machine, Docker Container or by Kubernetes. Hypervisors and VMs are the technologies used earlier. Docker is the technology we use nowadays, and Kubernetes is the future of Virtualization. In the face of large-scale and highly scalable needs, the cloud computing infrastructure is hard to fulfil in a short time, and the conventional virtual machine-based cloud host absorbs a lot of device resources on its own hence in this paper, we will address Docker as new container technology and introduce you to how this technology has solved previous problems in Virtualization, including the creation and deployment of large applications. The purpose of this paper is to provide a detailed survey of related MEC research and technological developments where specifically relevant research and future directions are illustrated.

2019 ◽  
Vol 9 (20) ◽  
pp. 4327
Author(s):  
Jueun Jeon ◽  
Jong Hyuk Park ◽  
Young-Sik Jeong

Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study.


Author(s):  
Ramandeep Kaur

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.


Author(s):  
Olexander Melnikov ◽  
◽  
Konstantin Petrov ◽  
Igor Kobzev ◽  
Viktor Kosenko ◽  
...  

The article considers the development and implementation of cloud services in the work of government agencies. The classification of the choice of cloud service providers is offered, which can serve as a basis for decision making. The basics of cloud computing technology are analyzed. The COVID-19 pandemic has identified the benefits of cloud services in remote work Government agencies at all levels need to move to cloud infrastructure. Analyze the prospects of cloud computing in Ukraine as the basis of e-governance in development. This is necessary for the rapid provision of quality services, flexible, large-scale and economical technological base. The transfer of electronic information interaction in the cloud makes it possible to attract a wide range of users with relatively low material costs. Automation of processes and their transfer to the cloud environment make it possible to speed up the process of providing services, as well as provide citizens with minimal time to obtain certain information. The article also lists the risks that exist in the transition to cloud services and the shortcomings that may arise in the process of using them.


2021 ◽  
Author(s):  
◽  
Kyle Chard

<p>The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains.</p>


Author(s):  
Adrian Jackson ◽  
Michèle Weiland

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.


Author(s):  
Marcus Tanque ◽  
Harry J Foxwell

Big data and cloud computing are transforming information technology. These comparable technologies are the result of dramatic developments in computational power, virtualization, network bandwidth, availability, storage capability, and cyber-physical systems. The crossroads of these two areas, involves the use of cloud computing services and infrastructure, to support large-scale data analytics research, providing relevant solutions or future possibilities for supply chain management. This chapter broadens the current posture of cloud computing and big data, as associate with the supply chain solutions. This chapter focuses on areas of significant technology and scientific advancements, which are likely to enhance supply chain systems. This evaluation emphasizes the security challenges and mega-trends affecting cloud computing and big data analytics pertaining to supply chain management.


Author(s):  
Salvatore Distefano ◽  
Antonio Puliafito

Cloud computing is the new consolidated trend in ICT, often considered as the panacea to all the problems of existing large-scale distributed paradigms such as Grid and hierarchical clustering. The Cloud breakthrough is the service oriented perspective of providing everything “as a service”. Different from the others large-scale distributed paradigms, it was born from commercial contexts, with the aim of selling the temporarily unexploited computing resources of huge datacenters in order to reduce the costs. Since this business model is really attractive and convenient for both providers and consumers, the Cloud paradigm is quickly growing and widely spreading, even in non commercial context. In fact, several activities on the Cloud, such as Nimbus, Eucalyptus, OpenNEbula, and Reservoir, etc., have been undertaken, aiming at specifying open Cloud infrastructure middleware.


Author(s):  
Michael Davis ◽  
Alice Sedsman

Cloud computing has been heralded as a new era in the evolution of information and communications technologies. ICT giants have invested heavily in developing technologies and mega server facilities, which allow end users to access web-based software applications and store their data off-site. Businesses using cloud computing services will benefit from reduced operating costs as they cut back on ICT infrastructure and personnel. Individuals will no longer need to buy and install software and will have universal access to their data through any internet-ready device. Yet, hidden amongst the host of benefits are inherent legal risks. The global nature of cloud computing raises questions about privacy, security, confidentiality and access to data. Current terms of use do not adequately address the multitude of legal issues unique to cloud computing. In the face of this legal uncertainty, end users should be educated about the risks involved in entering the cloud.


Author(s):  
Rashmi Rai ◽  
G. Sahoo

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.


Sign in / Sign up

Export Citation Format

Share Document