Secure Allocation for Graph-Based Virtual Machines in Cloud Environments

2021 ◽  
Author(s):  
Mansour Aldawood ◽  
Arshad Jhumka
Keyword(s):  
Virtual Machines ◽  
Cloud Environments

scholarly journals Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing

2017 ◽  
Vol 71 ◽  
pp. 129-144 ◽  
Author(s):  
José Luis Díaz ◽  
Joaquín Entrialgo ◽  
Manuel García ◽  
Javier García ◽  
Daniel Fernando García
Keyword(s):  
Optimal Allocation ◽  
Virtual Machines ◽  
On Demand ◽  
Cloud Environments ◽  
Multi Cloud

Update Checker: Increasing Virtual Machine Security in Cloud Environments

2014 ◽  
Vol 513-517 ◽  
pp. 1268-1273
Author(s):  
R. Raghavendran ◽  
B. Ragupathi
Keyword(s):  
Grid Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Infrastructure As A Service ◽  
Security Vulnerabilities ◽  
Software Vulnerabilities ◽  
Security Issues ◽  
Software Packages ◽  
Cloud Environments ◽  
Remote Security

A common approach in Infrastructure-as-a-Service Clouds or virtualized Grid computing is to provide virtual machines to customers to execute their software on remote resources. Giving full superuser permissions to customers eases the installation and use of user software, but it may lead to security issues. The providers usually delegate the task of keeping virtual machines up to date to the customers, while the customers expect the providers to perform this task. Consequently, a large number of virtual machines (either running or dormant) are not patched against the latest software vulnerabilities. The approach presented in this article deals with these problems by helping users as well as providers to keep virtual machines up to date. Prior to the update step, it is crucial to know which software is actually outdated or affected by remote security vulnerabilities. While these tasks seem to be straight forward, developing a solution that handles multiple software repositories from different vendors and identifies the correct packages is a challenging task. The Update Checker presented in this article identifies outdated software packages in virtual machines, regardless if the virtual machine is running or dormant on disk. The proposed Online Penetration Suite performs pre-rollout scans of virtual machines for security vulnerabilities using established techniques and prevents execution of flawed virtual machines.

Virtual machine placement mechanisms in the cloud environments: a systematic review

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haiyan Zhuang ◽  
Babak Esmaeilpour Ghouchani
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Cloud Services ◽  
Future Research ◽  
Cloud Environment ◽  
Content Type ◽  
Cloud Data ◽  
Refining Performance ◽  
Cloud Data Centers ◽  
Cloud Environments

Purpose Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future. Design/methodology/approach Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms. Findings The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance. Research limitations/implications This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP. Practical implications The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area. Originality/value The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

An Improved Genetic Algorithm for Document Clustering on the Cloud

2018 ◽  
Vol 8 (4) ◽  
pp. 20-28
Author(s):  
Ruksana Akter ◽  
Yoojin Chung
Keyword(s):  
Genetic Algorithm ◽  
Virtual Machines ◽  
Document Clustering ◽  
Local Minima ◽  
Improved Genetic Algorithm ◽  
Crossover Point ◽  
Text Document ◽  
Modified Genetic Algorithm ◽  
Cloud Environments ◽  
Traditional Approaches

This article presents a modified genetic algorithm for text document clustering on the cloud. Traditional approaches of genetic algorithms in document clustering represents chromosomes based on cluster centroids, and does not divide cluster centroids during crossover operations. This limits the possibility of the algorithm to introduce different variations to the population, leading it to be trapped in local minima. In this approach, a crossover point may be selected even at a position inside a cluster centroid, which allows modifying some cluster centroids. This also guides the algorithm to get rid of the local minima, and find better solutions than the traditional approaches. Moreover, instead of running only one genetic algorithm as done in the traditional approaches, this article partitions the population and runs a genetic algorithm on each of them. This gives an opportunity to simultaneously run different parts of the algorithm on different virtual machines in cloud environments. Experimental results also demonstrate that the accuracy of the proposed approach is at least 4% higher than the other approaches.

A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 423-438
Author(s):  
B. Lakhani ◽  
A. Agrawal
Keyword(s):  
Evolutionary Algorithms ◽  
Response Time ◽  
Task Scheduling ◽  
Gradient Descent ◽  
Virtual Machines ◽  
Sufficient Information ◽  
Swarm Optimization ◽  
Cloud Environments ◽  
Time Critical ◽  
Cloud Resources

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system.  The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

scholarly journals Elastic Technique for Load Balancing in Cloud Computing

2020 ◽  
Vol 8 (6) ◽  
pp. 1123-1127
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Advanced Technology ◽  
Service Provisioning ◽  
Web Traffic ◽  
Cloud Environments ◽  
Cloud Users

The cloud computing is the architecture that is decentralized in nature due to which various issues in the network get raised which reduces its efficiency. The exchange of data over the network is also continuously increasing. New advanced technology, cloud computing is becoming popular because of providing the above services beneficially. Other vital technologies like virtualization and scalability by designing virtual machines in cloud computing. In cloud computing, web traffic and service provisioning are increasing day by day, so load balancing is becoming a big research issue in cloud computing. Cloud Computing is a new propensity emerging in the IT environment within huge requirements of infrastructure and resources. The load Balancing technique for cloud computing is a vital aspect of the cloud computing environment. Peerless Load balancing scheme ensures splendid resource utilization by provisioning resources to cloud users on-demand services basis in a pay-as-you-use manner. The technique of Load Balancing may further support prioritizing requests of users/clients by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environments based on requirements specified in the Service Level Agreement (SLA).

An Improved Genetic Algorithm for Document Clustering on the Cloud

2021 ◽  
pp. 181-189
Author(s):  
Ruksana Akter ◽  
Yoojin Chung
Keyword(s):  
Genetic Algorithm ◽  
Virtual Machines ◽  
Document Clustering ◽  
Local Minima ◽  
Improved Genetic Algorithm ◽  
Crossover Point ◽  
Text Document ◽  
Cloud Environments ◽  
Traditional Approaches ◽  
Different Parts

This article presents a modified genetic algorithm for text document clustering on the cloud. Traditional approaches of genetic algorithms in document clustering represents chromosomes based on cluster centroids, and does not divide cluster centroids during crossover operations. This limits the possibility of the algorithm to introduce different variations to the population, leading it to be trapped in local minima. In this approach, a crossover point may be selected even at a position inside a cluster centroid, which allows modifying some cluster centroids. This also guides the algorithm to get rid of the local minima, and find better solutions than the traditional approaches. Moreover, instead of running only one genetic algorithm as done in the traditional approaches, this article partitions the population and runs a genetic algorithm on each of them. This gives an opportunity to simultaneously run different parts of the algorithm on different virtual machines in cloud environments. Experimental results also demonstrate that the accuracy of the proposed approach is at least 4% higher than the other approaches.

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