Browsing by Subject "Virtualization"

Now showing 1 - 4 of 4

Open Access
A utilization based genetic algorithm for virtual machine placement in cloud systems
(2024-01-15) Çavdar, Mustafa Can; Körpeoğlu, İbrahim; Ulusoy, Özgür
Due to the increasing demand for cloud computing and related services, cloud providers need to come up with methods and mechanisms that increase the performance, availability and reliability of data centers and cloud systems. Server virtualization is a key component to achieve this, which enables sharing of resources of a single physical machine among multiple virtual machines in a totally isolated manner. Optimizing virtualization has a very significant effect on the overall performance of a cloud computing system. This requires efficient and effective placement of virtual machines into physical machines. Since this is an optimization problem that involves multiple constraints and objectives, we propose a method based on genetic algorithms to place virtual machines into physical servers of a data center. By considering the utilization of machines and node distances, our method, called Utilization Based Genetic Algorithm (UBGA), aims at reducing resource waste, network load, and energy consumption at the same time. We compared our method against several other placement methods in terms of utilization achieved, networking bandwidth consumed, and energy costs incurred, using an open-source, publicly available CloudSim simulator. The results show that our method provides better performance compared to other placement approaches.
Open Access
Application placement with shared monitoring points in multi-purpose IoT wireless sensor networks
(Elsevier, 2022-11-09) Çavdar, Mustafa Can; Korpeoglu, Ibrahim; Ulusoy, Özgür
The main function of a wireless sensor network (WSN) is to gather data from a certain region and transfer the data to a center or remote locations for further processing. The collected data can be of interest for many applications. Therefore, a physical WSN owned by a single provider can be utilized by many customer applications. Additionally, the data of a particular point or sub-region can satisfy the need of multiple applications. Hence, sensing the data only once in such cases is beneficial to reduce the energy consumption, network traffic and acceptance ratio of the applications. We call this as monitoring point based shared data approach. In this paper, we focus on the placement of applications each of which requires several points to be monitored in an area a WSN covers. We first propose such a monitoring point based shared data approach for WSNs that will serve multiple dynamic applications. We also propose two methods for application placement over a shared physical WSN: one greedy method and one genetic algorithm based method called GABAP. We did extensive simulation experiments to evaluate our algorithms. The results show the effectiveness of our methods in fast and close-to-optimum placement of applications over a single network.
Open Access
Resource optimization of multi-purpose IoT wireless sensor networks with shared monitoring points
(2022-11) Çavdar, Mustafa Can
Wireless sensor networks (WSNs) have many applications and are an essential part of IoT systems. The primary functionality of a WSN is to gather data from certain points that are covered with sensor nodes and transmit the collected data to remote central units for further processing. In IoT use cases, a WSN infrastructure may need to be shared by many applications. Moreover, the data gathered from a certain point or sub-region can satisfy the need of multiple ap-plications. Hence, sensing the data once in such cases is advantageous to increase the acceptance ratio of the applications and reduce waiting times of applications, makespan, energy consumption, and traffic in the network. We call this approach monitoring point-based shared data approach. In this thesis, we focus on both placement and scheduling of the applications, each of which requires some points in the area a WSN covers to be monitored. We propose genetic algorithm-based approaches to deal with these two problems. Additionally, we propose greedy al-gorithms that will be useful where fast decision-making is required. We realized extensive simulation experiments and compared our algorithms with the methods from the literature. The results show the effectiveness of our algorithms in terms of various metrics.
Open Access
A utilization based genetic algorithm for virtual machine placement in cloud computing systems
(2016-09) Çavdar, Mustafa Can
Due to increasing demand for cloud computing and related services, cloud providers need to come up with methods and mechanisms that increase performance, availability and reliability of datacenters and cloud computing systems. Server virtualization is a key component to achieve this, which enables sharing of resources of a physical machine among multiple virtual machines in a totally isolated manner. Optimizing virtualization has a very signi cant e ect on the overall performance of cloud computing systems. This requires e cient and effective placement of virtual machines into physical machines. Since this is an optimization problem that involves multiple constraints and objectives, we propose a method based on genetic algorithms to place virtual machines. By considering utilization of machines and node distances, our method aims at reducing resource waste, network load, and energy consumption at the same time. We compared our method with several other methods in terms of utilization achieved, networking bandwidth consumed, and energy costs incurred, using the publicly available CloudSim simulation platform. The results show that our approach provides improved performance compared to other similar approaches.