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 Enhance the Security of Data Storage and Retrieval in Cloud Computing Through Authentication Based Encryption

2019 ◽  
Vol 8 (4) ◽  
pp. 11927-11931
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Virtual Machines ◽  
Public Key ◽  
Sensitive Data ◽  
Cloud Data ◽  
Finger Print ◽  
Additional Level ◽  
Cloud Users ◽  
Cloud User

The digital computing infrastructure is rapidly moving towards cloud based architecture. The protection of data is becoming a difficult task in the current scenario as more and more confidential and sensitive data is stored in cloud environment and transmitted between cloud users. In a cloud computing environment, the entire data reside over a set of networked resources of remote servers and locations. These data has been accessed by unauthorized cloud users through virtual machines. To provide additional level of cloud data security, Biometric based authentication with encryption using public key cryptography is proposed in this paper. The proposed model Authentication Based Encryption (ABE) helps to enhance the security of data as well as the authentication of cloud user. The sensitive data is initially encrypted and then stored secretly with biometric finger print image. The resultant image is transmitted through the in-secured channel. However to avoid unauthorized access, the image is decomposed and stored in cloud separately as encrypted message and finger print. Before beginning the decryption process, the finger print of the cloud user is being compared with the stored image for authentication. If the match is found, the encrypted data is decrypted by the authenticated cloud user. Otherwise access to the data is denied to ensure security. Thus, the proposed framework provides an additional level of protection to public key algorithm with authentication

A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

2022 ◽  
pp. 1-22
Author(s):  
Vhatkar Kapil Netaji ◽  
G.P. Bhole
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
State Of The Art ◽  
Research Work ◽  
Research Papers ◽  
Virtualization Technology ◽  
Comprehensive Survey ◽  
Novel Algorithms ◽  
Allocation Algorithms

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

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.

scholarly journals Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

2020 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Wu-Chun Chung ◽  
Tsung-Lin Wu ◽  
Yi-Hsuan Lee ◽  
Kuo-Chan Huang ◽  
Hung-Chang Hsiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Stream ◽  
Limited Attention ◽  
Heterogeneous Servers ◽  
Resource Allocation Algorithm ◽  
System Utilization ◽  
Allocation Algorithms ◽  
Considerable Resource ◽  
Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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 LAYANAN CLOUD COMPUTING BERBASIS INFRASTRUCTURE AS A SERVICE MENGGUNAKAN ANDROID

Compiler ◽  
2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Hero Wintolo ◽  
Lalu Septian Dwi Paradita
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Wireless Lan ◽  
Information Technologies ◽  
Computer Software ◽  
Computer Hardware ◽  
Local Network ◽  
Infrastructure As A Service ◽  
Platform As A Service ◽  
Service Application

Cloud computing, one form of information technologies are widely used in the field of computer networks or the Internet. Cloud computing consists of computer hardware, computer networking devices, and computer software, the cloud computing there are three services provided include (SaaS) Software as a Service (PaaS) Platform as a Service, and (IaaS) Infrastructure as a Service. Application cloud computing services in the wake of this system is a service-based data storage infrastructure as a service by using android smartphone as a storage medium, which utilizes FTP Server which is already available on the smartphone. This certainly supports the easy storage of data that utilize various types of internal and external storage on smartphones that serves as a storage server. In addition to the functions of storage available, this service can accommodate streaming function .mp3 file type. Implementation result of the system can be implemented on a local network using a wireless LAN. In addition, the results of user testing using Likert method shows the application can run and function properly

Improving the Energy Efficiency in Cloud Computing Data Centres Through Resource Allocation Techniques

2017 ◽  
pp. 211-236 ◽  
Author(s):  
Belén Bermejo ◽  
Sonja Filiposka ◽  
Carlos Juiz ◽  
Beatriz Gómez ◽  
Carlos Guerrero
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Cloud Computing ◽  
Data Centres

Smart XSS Attack Surveillance System for OSN in Virtualized Intelligence Network of Nodes of Fog Computing

2017 ◽  
Vol 14 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Shashank Gupta ◽  
B. B. Gupta
Keyword(s):  
Web Applications ◽  
Virtual Machines ◽  
Online Social Network ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Data ◽  
Distributed Intelligence ◽  
Injection Process ◽  
Development Framework ◽  
Data Centres

This article introduces a distributed intelligence network of Fog computing nodes and Cloud data centres for smart devices against XSS vulnerabilities in Online Social Network (OSN). The cloud data centres compute the features of JavaScript, injects them in the form of comments and saved them in the script nodes of Document Object Model (DOM) tree. The network of Fog devices re-executes the feature computation and comment injection process in the HTTP response message and compares such comments with those calculated in the cloud data centres. Any divergence observed will simply alarm the signal of injection of XSS worms on the nodes of fog located at the edge of the network. The mitigation of such worms is done by executing the nested context-sensitive sanitization on the malicious variables of JavaScript code embedded in such worms. The prototype of the authors' work was developed in Java development framework and installed on the virtual machines of Cloud data centres (typically located at the core of network) and the nodes of Fog devices (exclusively positioned at the edge of network). Vulnerable OSN-based web applications were utilized for evaluating the XSS worm detection capability of the authors' framework and evaluation results revealed that their work detects the injection of XSS worms with high precision rate and less rate of false positives and false negatives.

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