PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Virtual machine (VM) consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers. Most existing studies have considered VM consolidation as a bin-packing problem, but the current schemes commonly ignore the long-term relationship between VMs and hosts. In addition, there is a lack of long-term consideration for resource optimization in the VM consolidation, which results in unnecessary VM migration and increased energy consumption. To address these limitations, a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers (MPaAFVMC) is proposed. The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs, and obtains the stage demand of resources in the future period through multi-step prediction, which is realized by iterative prediction. Then, based on the multi-step prediction, an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance. During the VM consolidation, the affinity value is used to select the migration VM and placement host. The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform. Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption, VM migration costs, and service level agreement (SLA) violations.

Efficient utilization of Cloud resources is of utmost importance for Cloud Data Centers. The overall objective is to reduce energy consumption without compromising over quality of service. In order to achieve this goal different Virtual Machine (VM) Consolidation techniques have been proposed. Threshold detection for over utilized and underutilized hosts is an important phase of these techniques. In this paper we have proposed an adaptive model based on current load to optimize thresholds for detection of over and underutilized hosts. The proposed model uses Mean utilization of hosts and Standard deviation of VM utilization to calculate an acceptable range of utilization thresholds. An algorithm based on the proposed model is devised and tested through simulation experiments using Cloudsim. Four threshold detection policies namely, Static Threshold (ST), Inter-Quartile Range (IQR), Local Regression (LR), and Median Absolute Deviation (MAD) with Minimum Migration Time (MMT) as VM selection policy are compared with our proposed algorithm. Experiment results validate that the proposed algorithm reduces energy consumption, SLA violations, number of VM migrations and standard deviation of VM utilization in order to optimize resource utilization considerably.

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).

Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.

The virtual machine (VM) provisioning in cloud computing offers a good possibility for energy and cost saving, given the dynamic nature of the cloud environment. However, the commitment of giving the best possible quality of service to end users often leads to the requirement in dealing with the energy and performance tradeoff. In this work, we have proposed and evaluated three different virtual machine selection policies (MedMT, MaxUT and HP) to achieve a better performance as compared with the existing state of art algorithms. The proposed policies are evaluated through simulation on large-scale workload data conducted over a period of 7 days in a series of experiments. The results clearly indicate how the virtual machine selection algorithms can improve upon the energy consumption by data centers as well as the overall reduction in service level agreements (SLAs), thus reducing the cost significantly.

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

The Effects of Hotspot Detection and Virtual Machine Migration Policies on Energy Consumption and Service Levels in the Cloud

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Virtual machine (VM) consolidation is an emerging solution for energy saving in cloud data centers. An appropriate VM consolidation policy must change accordingly, depending on the situation. To easily deploy a VM consolidation solution, this paper proposes a VM consolidation framework for Apache CloudStack, which is a popular open source cloud platform software suite. We separate the VM consolidation mechanism from the policy used to decide which VMs should be moved to somewhere more appropriate to run. This framework is a modular architecture that combines various resource monitoring systems, power control mechanisms, and VM packing algorithms. Using the proposed framework, we have demonstrated that a Web application can consolidate and deconsolidate VMs so as to balance the load of CPU utilization among physical machines in response to the number of physical machines in operation. Experimental results reveal the proposed framework is preliminary, but provides the required functionality.

An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.

Virtual machine (VM) consolidation is necessary for increasing the server utilization; however, it also leads to VM performance degradation. This work presents a method to predict the consolidated VMs performance from the critical system events data. Experiments are designed to demonstrate the effect of system events like interrupts, page faults, mutex operations, and context switching on the consolidated VMs. Results show that the host server counters are not reliable for such predictions. On the other hand, the coupling of the task execution time with the VM system events is an effective way to predict the consolidation performance. Results further show that the VM memory allocation plays an important part in the consolidated tasks performance. The system event data is also used to train an Artificial Neural Network (ANN) for performance prediction on three hypervisors; ESXi, Xen, and XenServer and similar results are observed in all three.

VM consolidation steps in cloud computing: A perspective review