Efficient VM Placement Policy for Data Centre in Cloud Environment

Author(s):  
Kalka Dubey ◽  
Aida A. Nasr ◽  
S. C. Sharma ◽  
Nirmeen El-Bahnasawy ◽  
Gamal Attiya ◽  
...  
2019 ◽  
Vol 23 (2) ◽  
pp. 797-836 ◽  
Author(s):  
Seyedeh Yasaman Rashida ◽  
Masoud Sabaei ◽  
Mohammad Mehdi Ebadzadeh ◽  
Amir Masoud Rahmani

Author(s):  
Mohammed Radi ◽  
Ali Alwan ◽  
Abedallah Abualkishik ◽  
Adam Marks ◽  
Yonis Gulzar

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities


Author(s):  
Oshin Sharma ◽  
Hemraj Saini

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.


Author(s):  
Oshin Sharma ◽  
Hemraj Saini

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.


Author(s):  
Ajay Rawat ◽  
Rama Sushil ◽  
Amit Agarwal

Fault tolerance is the most imperious issue in the cloud to provide reliable services. Inherent vulnerability to failure hampers the performance and reliability of cloud services. Hence, to achieve reliability, fault tolerance becomes a mandatory feature which is hard to implement due to the dynamic infrastructure and complex interdependencies. Numerous fault tolerance techniques have been developed in the literature to address the challenges of cloud reliability. A recent research survey presented in this paper attempts to integrate the different fault tolerance architecture. This study presents a critical research review on various existing fault tolerance techniques to improve services reliability, availability, and applications execution in the cloud. A comparative analysis, based on different critical metrics like failure prediction, detection strategy, failure history, VM placement, and limitations, of the reviewed framework systems is also included in the paper. This review intends to facilitate the development of the new fault tolerance technique for the cloud environment.


Author(s):  
Xiaodong Zhang ◽  
Ying Zhang ◽  
Xing Chen ◽  
Kai Liu ◽  
Gang Huang ◽  
...  

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