Analysis of Virtual Machine Instances and Cost Computation on Microsoft Azure Cloud Service Provider

2020 ◽  
Vol 12 (3) ◽  
pp. 268-278
Author(s):  
Gagandeep Kaur
Keyword(s):  
Virtual Machine ◽  
Service Provider ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Microsoft Azure

scholarly journals Energy Efficient Resource Allocation in Cloud Computing

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2071-2074
Keyword(s):  
Virtual Machine ◽  
Service Provider ◽  
Physical System ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Host System ◽  
Physical Systems ◽  
Single Host

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].

scholarly journals Optimized memory model for hadoop map reduce framework

2019 ◽  
Vol 9 (5) ◽  
pp. 4396
Author(s):  
Archana Bhaskar ◽  
Rajeev Ranjan
Keyword(s):  
Service Provider ◽  
Service Level Agreement ◽  
Computation Time ◽  
Large Data ◽  
Cloud Service ◽  
Map Reduce ◽  
Memory Optimization ◽  
Process Data ◽  
Cloud Service Provider ◽  
Microsoft Azure

Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.

scholarly journals Securing SAAS service under cloud computing based multi-tenancy systems

2019 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Trinathbasu Miriyala ◽  
JKR Sastry
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Efficient Method ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Physical Machine ◽  
Multiple Users ◽  
Business Requirements

<p><span lang="EN-US">Cloud computing technologies are being used by many who need computing resources such as software, platform and infrastructure as per their business requirements in terms of provisioning and pay for the usage as per actual consumption of the services based on the SLA signed by the user and cloud service provider. Software running on a physical machine is being provided as services to the end users. For the reasons of cost economies access to software that uses a database is being provided to multiple users. The access to the software is provided either directly or through a virtual machine. The software being provided as service uses the same database for many of the users who have requisitioned for the same. As a result, there could be encroachments by the users into the data of others. There is a need to secure the data belonging to several users while all of them access the data using the same application. In this paper an efficient method is presented for securing the data processed by software which is offered as a service to multiple users either directly or through virtual machines.    </span></p>

Securing Cloud Virtual Machine Image using Ethereum Blockchain

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Service Provider ◽  
Formal Model ◽  
Cloud Service ◽  
Cloud Infrastructure ◽  
Cloud Service Provider ◽  
Service Consumer ◽  
Machine Image ◽  
Virtual Machine Image

Virtual Machine Image (VMI) is the building block of cloud infrastructure. It encapsulates the various applications and data deployed at the Cloud Service Provider (CSP) end. With the leading advances of cloud computing, comes the added concern of its security. Securing the Cloud infrastructure as a whole is based on the security of the underlying Virtual Machine Images (VMI). In this paper an attempt has been made to highlight the various risks faced by the CSP and Cloud Service Consumer (CSC) in the context of VMI related operations. Later, in this article a formal model of the cloud infrastructure has been proposed. Finally, the Ethereum blockchain has been incorporated to secure, track and manage all the vital operations of the VMIs. The immutable and decentralized nature of blockchain not only makes the proposed scheme more reliable but guarantees auditability of the system by maintaining the entire VMI history in the blockchain.

scholarly journals An Accurate and Efficient Scheduler for Hadoop MapReduce Framework

2018 ◽  
Vol 12 (3) ◽  
pp. 1132 ◽  
Author(s):  
D C Vinutha ◽  
G.T Raju
Keyword(s):  
Service Provider ◽  
Service Level Agreement ◽  
Computation Time ◽  
Large Data ◽  
Cloud Service ◽  
Process Data ◽  
Mapreduce Framework ◽  
Cloud Service Provider ◽  
Hadoop Mapreduce ◽  
Microsoft Azure

MapReduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used MapReduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel makespan model for Hadoop MapReduce framework namely OHMR (Optimized Hadoop MapReduce) to process data in real-time and utilize system resource efficiently. The OHMR present accurate model to compute job makespan time and also present a model to provision the amount of cloud resource required to meet task deadline. The OHMR first build a profile for each job and computes makespan time of job using greedy approach. Furthermore, to provision amount of resource required to meet task deadline Lagrange Multipliers technique is applied. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of OHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall good correlation is reported between practical makespan values and theoretical makespan values.

scholarly journals Load Balancing Model using Elastic Technique in Cloud Computing

2021 ◽  
Vol 10 (4) ◽  
pp. 185-188
Author(s):  
Sovban Nisar ◽  
Deepika Arora
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Service Provider ◽  
Structural Design ◽  
Virtual Machines ◽  
Cloud Service ◽  
Virtual Machine Migration ◽  
Cloud Service Provider

A structural design in which virtual machines are implicated and connect to the cloud service provider is called cloud computing. On the behalf of the users, the virtual machines connect to the cloud service provider. The uncertainties overload the virtual machines. The genetic algorithm is implemented for the migration of virtual machine in the earlier study. The genetic algorithm is low depicts latency within the network is high at the time of virtual machine migration. The genetic algorithm is implemented for virtual machine migration in this study. The proposed algorithm is applied in MATLAB in this work. The obtained results are compared with the results of earlier algorithm. Various parameters like latency, bandwidth consumption, and space utilization are used to analyze the achieved results.

scholarly journals Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider

2020 ◽  
Vol 8 (5) ◽  
pp. 4124-4232
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Cloud Service ◽  
Public Cloud ◽  
Cloud Service Provider ◽  
Computing Services ◽  
Cloud Service Providers ◽  
Amazon Web Services ◽  
Cloud Computing Services ◽  
Microsoft Azure

Picking up public cloud service providers is now becoming a harder task in an enterprise organization. This paper will help in reducing more hesitation to choose a public cloud service provider. This paper is highlighting computation, storage, and infrastructure is important to service features that have an impact when choosing cloud service providers. Compare these three (AWS, Microsoft Azure, GCP) CSPs concerning service, price, advantages, and highlight significant service features. Studies discuss the primary reason to choose a CSP that normally enhance features, familiarity with the brand, suitable for organization and security parameters considered when choosing CSP. Amazon Web Services proved its leadership by maintaining about 33% share in the market throughout for several quarters irrespective of the market size increased by a factor of 3. Microsoft has shown prominent performance in SaaS. Since 2008, after introducing PaaS in the form of Google App Engine, Google is continuously enhancing its cloud computing services of Google Cloud Platform.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

Sign in / Sign up

Export Citation Format

Share Document