An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches.

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.