Exploring Analytical Models for Performability Evaluation of Virtualized Servers using Dynamic Resource

Virtualization of resources is a widely accepted technique to optimize resources in recent technologies. Virtualization allows users to execute their services on the same physical machine, keeping these services isolated from each other. This paper proposes the analytical models for performability evaluation of virtualized servers with dynamic resource utilization. The performance and avalability models are considered separately due to the behaviour of the proposed system. The well-known Markov Reward Model (MRM) is used for the solution of the analytical model considered together with an exact spectral expansion and product form solution. The dynamic resource utilization is employed to enhance the QoS of the proposed model which is another major issue in the performance characterization of virtulazilation. In this paper, the performability output parameters, such as mean queue length, mean response time and blocking probability are computed and presented for the proposed model. In addition, the performability results obtained from the analytical models are validated by the simulation (DES) results to show the accuracy and effectiveness of the proposed work. The results indicate the proposed modelling results show good agreement with DES and understand the factors are very important to improve the QoS

Energy-aware virtual machine allocation for cloud with resource reservation

© 2018 Elsevier Inc. To reduce the price of pay-as-you-go style cloud applications, an increasing number of cloud service providers offer resource reservation-based services that allow tenants to customize their virtual machines (VMs) with specific time windows and physical resources. However, due to the lack of efficient management of reserved services, the energy efficiency of host physical machines cannot be guaranteed. In today\u27s highly competitive cloud computing market, such low energy efficiency will significantly reduce the profit margin of cloud service providers. Therefore, how to explore energy efficient VM allocation solutions for reserved services to achieve maximum profit is becoming a key issue for the operation and maintenance of cloud computing. To address this problem, this paper proposes a novel and effective evolutionary approach for VM allocation that can maximize the energy efficiency of a cloud data center while incorporating more reserved VMs. Aiming at accurate energy consumption estimation, our approach needs to simulate all the VM allocation updates, which is time-consuming using traditional cloud simulators. To overcome this, we have designed a simplified simulation engine for CloudSim that can accelerate the process of our evolutionary approach. Comprehensive experimental results obtained from both simulation on CloudSim and real cloud environments show that our approach not only can quickly achieve an optimized allocation solution for a batch of reserved VMs, but also can consolidate more VMs with fewer physical machines to achieve better energy efficiency than existing methods. To be specific, the overall profit improvement and energy savings achieved by our approach can be up to 24% and 41% as compared to state-of-the-art methods, respectively. Moreover, our approach could enable the cloud data center to serve more tenant requests