Energy-Aware VM Consolidation Using Similarity-Driven Intelligence in Green Cloud Environments

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.

In cloud environments, the overload leads to performance degradation and Service Level Agreement (SLA) violation while underload results in inefficient utilisation of resources and needless energy consumption. Dynamic Virtual Machine (VM) consolidation is considered as an effective solution to deal with both overload and underload problems. However, dynamic VM consolidation is not a trivial solution as it can also lead to violation of negotiated SLA due to runtime overheads in VM migration. Further, dynamic VM consolidation approaches need to answer many questions such as (i) when to migrate a VM? (ii) which VM is to be migrated? and (iii) where to migrate the selected VM? In this work, efforts are made to develop a comprehensive approach to achieve better solution to above discussed problems. In the proposed approach, future forecasting methods for host overload detection are explored; a fuzzy logic based VM selection approach that enhances the performance of VM selection strategy is developed; and a VM placement algorithm based on destination CPU utilisation is also developed. The performance evaluation of the proposed approaches is carried out on CloudSim toolkit using PlanetLab data set. The simulation results have exhibited significant improvement in the number of VM migrations, energy consumption, and SLA violations.

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. The detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper proposes the dynamic threshold-based fuzzy approach (DTFA) for detecting overloaded and underutilized PMs, and the Lowest Interdependence Factor Exponent Multiple Resources Predictive (LIFE-MP) approach for VM placement, by considering multiple computing resources being used simultaneously in a cloud environment. The DTFA is a fuzzy threshold-based approach used to adjust the threshold values of PMs in a cloud environment. The LIFE-MP approach selects a PM at which to place the migrating VM, based on the PM with the lowest correlation coefficient value among the already-running VMs and the migrating VM to reduce performance degradation because of the VM migration. The comparison between LIFE-MP scheme and a power-aware best-fit decreasing (PABFD) scheme shows that the proposed scheme reduces power consumption by 22.52, SLA violations by 45.63, and the number of VM migrations by 56.68.

In recent years, cloud data centers are rapidly growing with a large number of finite heterogeneous resources to meet the ever-growing user demands with respect to the SLA (service level agreement). However, the potential growth in the number of large-scale data centers leads to large amounts of energy consumption, which is constantly a major challenge. In addition to this challenge, intensive number of VM (virtual machine) migrations can decrease the performance of cloud data centers. Thus, how to minimize energy consumption while satisfying SLA and minimizing the number of VM migrations becomes an important challenge classified as NP-hard optimization problem in data centers. Most VM scheduling schemes have been proposed for this problem, such as dynamic VM consolidation. However, most of them failed in low time complexity and optimal solution. Hence, this paper proposes a dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment. Specifically, the proposed approach consists of four methods which include: BPSO meta-heuristic-based load balancing to impact on energy consumption and number of host shutdowns, overloading host detection and VM placement-based Pearson correlation coefficient to impact on SLA, and VM selection based on imbalance degree to impact on number of VM migration. Moreover, Pearson correlation coefficient and imbalance degree correlate CPU, RAM and bandwidth, respectively, in each host and each VM. Through extensive analysis and simulation experiments using real PlanetLab and random workloads, the performance results demonstrate that the proposed approach exhibits excellent results for the NP-problem.

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. Detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper presents a new proposal for the dynamic adjustment of threshold values that aims to minimize the number of migrations in varying workload environments. The proposed approach, named the ‘dynamic threshold-based fuzzy approach’ (DTFA), is a fuzzy threshold-based approach used for adjusting the threshold values of PMs in a cloud environment. The proposed approach allows the number of migrations caused by overloading to be reduced and SLAs to be met. Three sets of experiments with different workloads were conducted to validate the proposed approach. The results demonstrate that DTFA outperforms existing solutions by an average of 22.52%, 45.63% and 56.68% in power consumption, VM migration count, and SLA violations, respectively.

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

Virtual machines (VMs) consolidation has emerged as an important method for the design of energy-efficient data centers. The purpose is to aggregate VMs to fewer physical machines and put the idle servers into power-saving mode. Existing researches mainly focus on transforming the VMs consolidation into various bin packing problems. However, VMs consolidation may cost Service Level Agreement (SLA) violations just after the migration due to the uncertainty of applications' demands. In this paper, we provide a SLA risk management framework, involving a stochastic program to solve the resource allocation for VMs and an algorithm for dynamic VMs consolidation at runtime, to optimize both the energy consumption saving and SLA violations. We validate the proposed algorithm using workloads from a real world system. The results compare with other VMs consolidation algorithms that without considering risk, and show that our SLA violations is reduced by four times from 25% to 2% - 5% while only losing little energy consumption saving.

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.

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).

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively.

Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption and improve resource utilization in data centers. Since VM consolidation problem is strictly NP-hard, many heuristic algorithms have been proposed to tackle the problem. However, most of the existing works deal only with minimizing the number of hosts based on their current resource utilization and these works do not explore the future resource requirements. Therefore, unnecessary VM migrations are generated and the rate of Service Level Agreement (SLA) violations are increased in data centers. To address this problem, our VM consolidation method which is formulated as a bin-packing problem considers both the current and future utilization of resources. The future utilization of resources is accurately predicted using a k-nearest neighbor regression based model. In this paper, we investigate the effectiveness of VM and host resource utilization predictions in the VM consolidation task using real workload traces. The experimental results show that our approach provides substantial improvement over other heuristic algorithms in reducing energy consumption, number of VM migrations and number of SLA violations.

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.

In the past few years, many researchers attempted to tackle the problem of decreasing energy consumption in cloud data centers. One of the widely adopted techniques for this purpose is dynamic Virtual Machine (VM) consolidation. Consolidation moves VMs between hosts to decrease energy consumption. However, it has a negative impact on performance leading to Service Level Agreement (SLA) violations. Accordingly, selecting which VM to migrate from one host to another is a challenging task since it can affect performance. Researchers came up with several solutions and policies for efficient VM selection. In this paper, we exploit the fact that many tasks and users may tolerate some performance degradation which means, the tasks running on the VMs can be of different priorities. Accordingly, we propose augmenting consolidation with the priority concept, where low priority tasks are always selected first for migration. Towards this goal, we modified the popular Minimum Migration Time VM selection algorithm using the priority concept. The efficiency of the proposed algorithm is confirmed through extensive simulations using CloudSim toolkit and a real workload. The results show that priority awareness has a positive impact on decreasing energy consumption as well as maximizing SLA obligation.

With the large-scale deployment of virtualized data centers, energy consumption and SLA (Service Level Agreement) violation have already become the urgent issue to be solved. And it is essential and important to design energy-aware allocation policy for energy-aware and SLA violation reduction. In this paper, we propose a novel allocation and selection policy for the dynamic virtual machine (VM) consolidation in virtualized data centers to reduce energy consumption and SLA violation. Firstly, we use the mean and standard deviation of CPU utilization for VM to determine the hosts overloaded or not, secondly we use the positive maximum correlation coefficient to select VMs from those overloading hosts for migration. Although the proposed allocation and selection policies performs a little worse than the previous ones in energy consumption, experiments show that it performs greatly better than the previous ones on the whole.