Performance Evaluation of Energy-Aware Virtual Machine Placement Techniques for Cloud Environment

2018 ◽  
pp. 45-72
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Aware ◽  
Physical Machine ◽  
Overall Performance ◽  
And Performance ◽  
The One ◽  
Vm Consolidation

The most dominant service of cloud computing is infrastructure as a service (IaaS). Virtualization is the most important feature of IaaS and it is very important for the improvement of resource utilization; but along with this, it also degrades the system's performance and makes them overutilized. Therefore, to solve the problem of overutilization or underutilization of machines and performance improvement of machine, the VMs present inside the physical machine needs to be migrated to another physical machine using the process of VM consolidation, and the reduced set of physical machines after placement needs a lesser amount of power or energy consumption, which is the main aim of energy-aware VM placement. This chapter presents a decision-making VM placement system and compares it with other predefined VM placement techniques. This analysis contributes to a better understanding of the effects of the placement strategies over the overall performance of cloud environment and also shows how the one algorithm delivers better results for VM placement than another.

Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

2017 ◽  
Vol 13 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Consolidation Process ◽  
Physical Machine ◽  
Overall Performance ◽  
Vm Consolidation ◽  
New Algorithms

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

2021 ◽  
pp. 1456-1470
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Consolidation Process ◽  
Physical Machine ◽  
Overall Performance ◽  
Vm Consolidation ◽  
New Algorithms

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

A privacy protection approach for XML-based archives management in a cloud environment

2019 ◽  
Vol 37 (6) ◽  
pp. 970-983 ◽  
Author(s):  
Zongda Wu ◽  
Jian Xie ◽  
Xinze Lian ◽  
Jun Pan
Keyword(s):  
Cloud Computing ◽  
Privacy Protection ◽  
Design Methodology ◽  
Digital Archive ◽  
Cloud Environment ◽  
System Availability ◽  
Encrypted Data ◽  
Content Type ◽  
Main Obstacle ◽  
Overall Performance

Purpose The security of archival privacy data in the cloud has become the main obstacle to the application of cloud computing in archives management. To this end, aiming at XML archives, this paper aims to present a privacy protection approach that can ensure the security of privacy data in the untrusted cloud, without compromising the system availability. Design/methodology/approach The basic idea of the approach is as follows. First, the privacy data before being submitted to the cloud should be strictly encrypted on a trusted client to ensure the security. Then, to query the encrypted data efficiently, the approach constructs some key feature data for the encrypted data, so that each XML query defined on the privacy data can be executed correctly in the cloud. Findings Finally, both theoretical analysis and experimental evaluation demonstrate the overall performance of the approach in terms of security, efficiency and accuracy. Originality/value This paper presents a valuable study attempting to protect privacy for the management of XML archives in a cloud environment, so it has a positive significance to promote the application of cloud computing in a digital archive system.

scholarly journals PEMILIHAN DAN MIGRASI VM MENGGUNAKAN MCDM UNTUK PENINGKATAN KINERJA LAYANAN PADA CLOUD COMPUTING

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Abdullah Fadil ◽  
Waskitho Wibisono
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Virtual Machine ◽  
Data Center ◽  
Response Times ◽  
Multi Criteria Decision Making ◽  
Live Migration ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Cloud Data

Komputasi awan atau cloud computing merupakan lingkungan yang heterogen dan terdistribusi, tersusun atas gugusan jaringan server dengan berbagai kapasitas sumber daya komputasi yang berbeda-beda guna menopang model layanan yang ada di atasnya. Virtual machine (VM) dijadikan sebagai representasi dari ketersediaan sumber daya komputasi dinamis yang dapat dialokasikan dan direalokasikan sesuai dengan permintaan. Mekanisme live migration VM di antara server fisik yang terdapat di dalam data center cloud digunakan untuk mencapai konsolidasi dan memaksimalkan utilisasi VM. Pada prosedur konsoidasi vm, pemilihan dan penempatan VM sering kali menggunakan kriteria tunggal dan statis. Dalam penelitian ini diusulkan pemilihan dan penempatan VM menggunakan multi-criteria decision making (MCDM) pada prosedur konsolidasi VM dinamis di lingkungan cloud data center guna meningkatkan layanan cloud computing. Pendekatan praktis digunakan dalam mengembangkan lingkungan cloud computing berbasis OpenStack Cloud dengan mengintegrasikan VM selection dan VM Placement pada prosedur konsolidasi VM menggunakan OpenStack-Neat. Hasil penelitian menunjukkan bahwa metode pemilihan dan penempatan VM melalui live migration mampu menggantikan kerugian yang disebabkan oleh down-times sebesar 11,994 detik dari waktu responnya. Peningkatan response times terjadi sebesar 6 ms ketika terjadi proses live migration VM dari host asal ke host tujuan. Response times rata-rata setiap vm yang tersebar pada compute node setelah terjadi proses live migration sebesar 67 ms yang menunjukkan keseimbangan beban pada sistem cloud computing.

scholarly journals Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE

2019 ◽  
Vol 9 (1) ◽  
pp. 279-291 ◽  
Author(s):  
Proshikshya Mukherjee ◽  
Prasant Kumar Pattnaik ◽  
Tanmaya Swain ◽  
Amlan Datta
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Multi Criteria Decision Making ◽  
Cloud Environment ◽  
Computing Environment ◽  
Queuing Model ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm

AbstractThis Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model in a cloud environment and also deals with minimization of the waiting time of the task. The simulated results of the algorithm give better outcomes as compared to other existing algorithms by 15 percent.

Role of Agents to Enhance the Security and Scalability in Cloud Environment

2019 ◽  
pp. 552-573 ◽  
Author(s):  
Manisha Malhotra ◽  
Aarti Singh
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Mobile Agents ◽  
The Other ◽  
Main Concern ◽  
Hybrid Architecture ◽  
Cloud Environment ◽  
Dynamic Nature ◽  
The One

Cloud computing is a novel paradigm that changes the industry viewpoint of inventing, developing, deploying, scaling, updating, maintaining, and paying for applications and the infrastructure on which they are deployed. Due to dynamic nature of cloud computing it is quite easy to increase the capacity of hardware or software, even without investing on purchases of it. This feature of cloud computing is named as scalability which is one of the main concern in cloud environment. This chapter presents the architecture of scalability by using mobile agents. It also highlights the other main issues prevailing in cloud paradigm. Further it presents the hybrid architecture for data security which is also the one of major concern of it. This chapter mainly highlights the solution for scalability and security.

scholarly journals Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiong Fu ◽  
Qing Zhao ◽  
Junchang Wang ◽  
Lin Zhang ◽  
Lei Qiao
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Energy ◽  
Binary Particle Swarm Optimization ◽  
Vm Placement ◽  
Energy Aware ◽  
Low Energy Consumption ◽  
Initial Placement ◽  
And Storage ◽  
High Energy Consumption

In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

scholarly journals Benchmarking and Performance Evaluations on Various Configurations of Virtual Machine and Containers for Cloud-Based Scientific Workloads

2021 ◽  
Vol 11 (3) ◽  
pp. 993
Author(s):  
Syed Asif Raza Shah ◽  
Ahmad Waqas ◽  
Moon-Hyun Kim ◽  
Tae-Hyung Kim ◽  
Heejun Yoon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Scientific Community ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Performance Evaluations ◽  
Cloud Environment ◽  
Computational Power ◽  
Efficient Resource ◽  
Benchmark Suite ◽  
And Performance

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.

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