VM Allocation Policy Research Articles

A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.

Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Virtual Machine Allocation Policy for Load Balancing

This paper focuses on the VM allocation policies for load balancing in cloud computing environment. Intermittent nature of balancing the load scheme into the cloud computing becomes a challenging job and it also affects the load balancing of the cloud. The suggested proposed model generates and step-up the VM allocation policies but also transforms the generated cloud workload. Furthermore, to improve the workload distribution of workload and stability of the overall cloud computing environment the load balancing algorithm is most important for load balancing. The work of load balancing is equally effective in the cloud computing environment and it is most essential one for load balancing algorithms to take care of all issues at the time of the work load. The researchers studied different algorithms to solve the problems of load balancing that generate problems during the distribution of workloads. The analysis VM allocation policies are tested on CloudSim environment and the results, and discussion is about to which one VM allocation policy is superior.

Dynamic degree balanced with CPU based VM allocation policy for load balancing

In cloud computing environment, Load balancing is key challenge. To address above challenge, we have proposed Dynamic Degree Balance with CPU based VM allocation policy. The proposed algorithm includes both VM allocation and task allocation. This algorithm is compared with the two static algorithms viz. Shortest Job First (SJF) and First Come First Serve (FCFS). CloudSim simulator is used to perform experiment. Degree of imbalance and waiting time is considered as evaluation parameter to check the results of experiment. The experimental result shows that proposed algorithm reduces degree of imbalance and waiting time of task.

A secure VM allocation scheme to preserve against co-resident threat

Preserving the secrecy in cloud system is one of the biggest concerns for the cloud customers who faces security risks in the context of load balancing. The co-resident attacks are widely used by attackers, where malicious users build side channels and extract private information from VMs. The proposed model evaluates the possibility of VM co-residency and success rate of an attack. The emphasis of this paper is to reduce the possibility of co-resident attacks among different users. When cloud data centres receive requests for the tasks deployment, then the proposed system will find out the secure physical machine under VM allocation policies while avoiding the threats. The performance is calculated by these metrics: makespan, resource utilisation, co-residency probability and co-resident success rate. The results show that the most virtual machine allocation policy (MVMP) effectively reduces the risk under the safe states. The framework significantly improves the security by reducing the shared servers up to 32.2% and enhances the resource utilisation up to 44.14% over least VM allocation policy (LVMP), round robin VM allocation policy (RRVMP) allocation schemes.

Location-Aware Mobile Cloud using Artificial Intelligence

Mobile cloud computing is a rapidly evolving technology these days and it faces major problems of load imbalance due to the high demand for mobile applications. There are many techniques to solve the problem, but the user can improve load by using a more optimized solution. This research deals with the VM allocation and migration by means of location awareness. The research paper also presents a user authentication and server load management system to reduce the overload of the server. A captcha based authentication mechanism is also presented for user verification. The concept of Feedback is also introduced for the mobile servers. This concept makes the selection of mobile server for the job list. ANN (Artificial neural network) is used for location awareness judgment. ANN is a machine learning approach, which is used to minimize the human effort and also minimize the processing time to allocate job to an accurate server with minimum SLA violation. MBFD (Mobile best fit decreasing) algorithm is used for the VM allocation and selection policy. This research has considered SLA (Service level agreement) violation and energy consumption to compute the performance of the work with an aim of reducing energy consumption with maximized resource efficiency. The proposed work model is also compared with the work presented by Xiong in the same area.

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

Special Issue on Recent Trends and Future of Fog and Edge Computing, Services and Enabling Technologies

An Analysis of Energy Consumption in Mobile Cloud Computing

Mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services and computing concept by an explosive growth of the mobile applications and emerging of cloud computing. The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. This paper presents a unique VM allocation and migration policy of mobile cloud. VM migration is known as the technique for the optimization of the energy consumption in mobile cloud data center. Thus, aim of this paper is for minimizing the SLA violation, energy consumption with a number of migrations, MBFD algorithm and SVM technique would be used for energy optimization using VM migration.

Intelligence VM Allocation and Selection Policy based on Local Linear Regression (LR)

Intelligence VM Allocation and Selection Policy based on Local Linear Regression (LR)

Proportionate resource utilization based VM allocation method for large scaled datacenters

The newly emerged concept of dedicated servers for virtual machines in Cloud Data Centres has motivated researchers’ community to think more critically on optimal utilization of host resources. VMs hosted on dedicated servers are not allowed to migrate during their lifetime, thus it is very important for service providers to adopt that VM allocation policy which utilizes the resources of servers in an optimal way in order to improve the performance of whole data centre. Efficient utilization of resources of data centre is quite challenging due to unpredictability of workload. Host machines in a cloud data centre deal with heterogeneous VMs which have different RAM and CPU requirements. Compute intensive VMs require more computing power than RAM where as data intensive VMs require less computing power than RAM. Similarly other VMs like Small and Micro too have different RAM and computing capacity requirements. An improper VM allocation policy to place different types of VMs in hosts, sometimes leads to uneven usage of host resources and results in wastage of Hosts’ resources like RAM and computing power. In the present work, a novel VM allocation policy has been proposed that allocates VMs to hosts keeping in view the RAM and CPU consumption of host in a proportionate way. It uses the concept of skewness to measure the unevenness in usage of host resources and allocates VM to that host machine which has least skew value. Experimental results obtained through simulation are compared with the Simple First Fit and Power Aware Best Fit Decreasing (PABFD) Policies. It has been found that proportionate usage of RAM and CPU capacity of the host machine accommodates more VMs and reduces the total energy consumption of the data centre. It also outperforms PABFD in terms Energy Consumption and Number of Hosts shutdown.

Using Thermal-Aware VM Migration Mechanism for High-Availability Cloud Computing

Cloud computing, such as Infrastructure as a Service (IaaS), enables vendors to use virtualization technology to rent computing resources on a physical machine to execute the desired applications of users. IaaS is the most common business model of cloud computing; however, its availability remains a concern among users. Several factors affect the availability of a cloud computing center, such as interruption of service caused by hardware component damage. In this study, we focused on the thermal emergency event of CPU overheating caused by chassis fan damage, and determined a method to resolve the crisis before a crash occurs. We designed a thermal-aware VM migration manager (TAVMM) that can determine the health of a physical machine from its temperature and resource use information. By leveraging VM migration, the risk to the physical machine can be removed by transferring its load to a normal one and reducing the CPU temperature. We propose heat transfer and migration time as criteria for a VM selection policy and the load balance algorithm regarding thermal tolerance as the VM allocation policy. The simulation results show that a TAVMM with the proposed VM selection and allocation policy can enhance system ability and reduce the number of VM failures.

A COMBINED BIN PACKING VM ALLOCATION AND MINIMUM LOADED VM MIGRATION APPROACH FOR LOAD BALANCING IN IAAS CLOUD DATACENTERS

Day by day increasing online computation and migration to the cloud, significantly increase the need of proper load balancing. Load balancing ensure service availability and performance to users. Downtime experienced by users is great issue which leads to violation of QoS requirements for users. Improving response time is one of the solution and some extend it helps to meet the SLA. The proposed system has the intention of achieving an improved load balancing performance to cloud data center, and a satisfying service response to users. By focusing on virtualization technology here consider both virtual machine allocation and migration processes to achieve a better load balancing solution. A VM allocation policy called Bin packing and a Minimum loaded VM migration policy based on load threshold are used together to achieve the goal in Infrastructure as a Service cloud environment. A cloud simulation tool called CloudReport is used to perform the simulation of the system. The experimental results show that the approach provides comparatively improved response time and completion time for users.