Virtual Machine Consolidation Research Articles

A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

In this paper, we develop a model for the competitive relationship between cloud service providers and cloud brokers during the virtual machine (VM) placement. The model adopts the idea of a strategy game, in which the cloud service provider and all VMs are participants. First, we define the strategy for the participants in the strategy game model according to the difference between the number of physical resources provided by the provider, and that requested for the VMs by the brokers. Next, payment values between the providers and brokers are obtained from each strategy in the game matrix, which corresponds to the strategy set of VM placement. In addition, the Nash equilibrium solution of the current strategy game demonstrates the trade-off between reducing energy consumption and guaranteeing QoS. On the basis of this, the optimal VM placement scheme is obtained, and the ongoing VM consolidation is examined. Finally, a strategy-game-based VM consolidation (SG-VMC) method is proposed. The extensive experimental results show that the proposed SG-VMC method can significantly reduce energy consumption while guaranteeing QoS.

Virtual machine allocation strategy in energy-efficient cloud data centres

Energy consumption is significant in cloud data centres even when some virtual machines (VMs) are idle. Taking into account both the energy efficiency and the response performance, we propose a VM allocation strategy with speed switch and VM consolidation in the cloud data centre. When the traffic is lighter, the processing speed of VMs in baseline module will be switched between a low speed and a high speed. When the traffic is much heavier, the VMs in reserved module will be consolidated with the VMs in baseline module, and operates at a high speed. We establish a two-dimensional Markov chain to mathematically estimate the energy-saving degree and the response performance. Numerical experiments with analysis and simulation show that our proposed VM allocation strategy can effectively reduce the energy consumption as well as guarantee the response performance.

Energy-efficient fuzzy-based approach for dynamic virtual machine consolidation

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.

Virtual machine allocation strategy in energy-efficient cloud data centres

Energy consumption is significant in cloud data centres even when some virtual machines (VMs) are idle. Taking into account both the energy efficiency and the response performance, we propose a VM allocation strategy with speed switch and VM consolidation in the cloud data centre. When the traffic is lighter, the processing speed of VMs in baseline module will be switched between a low speed and a high speed. When the traffic is much heavier, the VMs in reserved module will be consolidated with the VMs in baseline module, and operates at a high speed. We establish a two-dimensional Markov chain to mathematically estimate the energy-saving degree and the response performance. Numerical experiments with analysis and simulation show that our proposed VM allocation strategy can effectively reduce the energy consumption as well as guarantee the response performance.

IBrownout: An Integrated Approach for Managing Energy and Brownout in Container-Based Clouds

Energy consumption of Cloud data centers has been a major concern of many researchers, and one of the reasons for huge energy consumption of Clouds lies in the inefficient utilization of computing resources. Besides energy consumption, another challenge of data centers is the unexpected loads, which leads to the overloads and performance degradation. Compared with VM consolidation and Dynamic Voltage Frequency Scaling that cannot function well when the whole data center is overloaded, brownout has shown to be a promising technique to handle both overloads and energy consumption through dynamically deactivating application optional components, which are also identified as containers/microservices. In this work, we propose an integrated approach to manage energy consumption and brownout in container-based cloud data centers. \color{black} We also evaluate our proposed scheduling policies with real traces in a prototype system. The results show that our approach reduces about 40%, 20% and 10% energy than the approach without power-saving techniques, brownout-overbooking approach and auto-scaling approach respectively while ensuring Quality of Service.

Survey Energy Management Approaches in Data Centres

Data centers are today the technological backbone for any company. However, the failure to control energy consumption leads to very high operating costs and carbon dioxide emissions. On the other hand, reducing power consumption in data centers can lead to a degradation of application performance and quality of service in terms of SLA Service Level Agreement. It is therefore essential to find a compromise in terms of energy efficiency and resource consumption. This paper highlights the different approaches of energy management, related studies, algorithms used, the advantages and weaknesses of each approach related to server virtualization, consolidation and deconsolidation of virtual machines.

KBR: Knowledge Based Reduction Method for Virtual Machine Migration in Cloud Computing

Through large scale deployment of cloud data centers, burning up of energy by data center is very high and violations of service level agreements (SLA) are increasing. Implementation of Virtual machine consolidation techniques direct to physical host over exploitation which degrades the performance of physical host and Virtual machine. It has been a momentous confronts to get better exploitation of resources and energy effectiveness in cloud data center. This paper proposed knowledge based reduction method for Virtual machine assignment and consolidation. This method chooses the Virtual machines from the overloaded host and migrate the Virtual machines to other physical host to diminish energy burning up and get better quality of service. To provide evidence and effectiveness of proposed method, the experiment is conducted with diverse workload circumstances of real system. The experimental consequences exhibits that the proposed method diminishes energy burning up and optimizes infringement of service level agreements in achieving better performance.

Intra-Balance Virtual Machine Placement for Effective Reduction in Energy Consumption and SLA Violation

Cloud computing has emerged as one of the most important technological revolutions globally. However, the rapid growth of cloud computing has imposed a massive financial burden and resulted in environmental side effects due to excessive energy consumption. The high power consumption is not only attributed to the size of data centers but also to the ineptitude of resource usage. Most of the extant research has focused on reducing power consumption by an aggressive VM consolidation, which leads to the violation of the service level agreement (SLA). Furthermore, the unbalanced resource consumption exacerbates the unavailable wasted resources that are referred to as unavailable resource fragmentation. In this paper, we propose the use of a balanced resources consumption algorithm called BRC-IBMMT in order to enhance the efficiency of resource consumption while achieving an acceptable balance between conflicting correlation objectives of power consumption as well as SLA violation. The extensive simulation results of different types of workload validate and lend credence to the significance of the proposed method in reducing power consumption and SLA violation of the cloud data center.

Failure-aware energy-efficient VM consolidation in cloud computing systems

VM consolidation is an important technique used in cloud computing systems to improve energy efficiency. It migrates the running VMs from under utilized physical resources to other resources in order to reduce the energy consumption. But in a cloud computing environment with failure prone resources, focusing solely on energy efficiency has adverse effects. If the reliability factor of resources is ignored then the running VMs may get consolidated to unreliable physical resources. This will cause more failures and recreations of VMs, thus increasing the energy consumption. To solve this problem, this paper proposes a failure-aware VM consolidation mechanism, which takes the occurrence of failures and the hazard rate of physical resources into consideration before performing VM consolidation. We proposed a failure prediction technique based on exponential smoothing to trigger two fault tolerance mechanisms (VM migration and VM checkpointing). A simulation based evaluation of the proposed VM consolidation mechanism was conducted by using real failure traces. The results demonstrate that by using the combination of checkpointing and VM migration with the proposed failure-aware VM consolidation mechanism, the energy consumption of cloud computing system is reduced by 34% and reliability is improved by 12% while decreasing the occurrence of failures by 14%.

A survey: energy-efficient sensor and VM selection approaches in green computing for X-IoT applications

ABSTRACTCloud computing (CC) enables enumerable services to manipulate sensor data generated from X-internet of things (IoT) applications. It is accomplished by selecting an accurate decision-making system, sensors, and VMs. This paper reviews energy-efficient sensor, resource-based VM selection approaches for X-IoT applications. It is prompted to distinguish measurement functions, architectures, VM scheduling mechanism challenges. The first field was surveyed to identify the technical measurement variables of agriculture and the second field to distinguish the difficulties of sensor selection, communication impact on the rate of sensor data generation. The last field represents VM consolidation approaches based on a type of task, resource, and energy utilization rate impact on balancing the resources of VMs during sudden changes in the network field. The precise implementation details of selected articles are bounded with sensor energy consumption, edge computing modules, and communication strategies. The outcomes of investigation consolidate a sensor-cloud framework that implies prevailing solution to CC for X-IoT.

Energy-aware dynamic resource management in elastic cloud datacenters

In clouds, placement of on-demand applications on heterogeneous machines, has turned out to be a crucial research problem, particularly, in terms of performance and energy consumption. Techniques like Dynamic Voltage and Frequency Scaling (DVFS), processor speed adjustment and features such as turning off displays, activating sleep modes, etc. are only useful for decreasing the energy consumption of a single machine, at marginal loss in performance. They cannot be used to achieve significant power optimization in High Performance Computing (HPC) systems such as grids, and cloud datacenters; because power saved by scaling down the processor voltage is far less than switching off a machine. Resource management, using dynamic consolidation of VMs, allows cloud service providers to optimize resource usability, performance and decrease power consumption. This paper investigates various resource management techniques, and suggests several heuristic approaches to optimise energy consumption and performance in elastic datacenters. Using real workload datasets, our evaluation suggests that a combination of the proposed VM allocation and consolidation with migration control technique could save approximately 1.96%–9.38% energy, and improve 0.32%–5.96% performance, as compared to its closest rivals.

A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

To achieve energy efficiency in data centers, dynamic virtual machine (VM) consolidation as a key technique has become increasingly important nowadays due to the significant amounts of power needed...

Virtual machine consolidation using constraint-based multi-objective optimization

With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective Boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of Boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods.

Workload aware VM consolidation method in edge/cloud computing for IoT applications

Wide-ranging edge cloud data centers are a vital part of the solution for the problems caused by enormous growth in the IT industry for high computational power by advanced service applications. Majority of IoT applications switched to the Cloud and this stimulated the emergence of Edge technology to better manage the computing applications, data, resource and services. Consequently, with the massive client size and enormous applications trying to benefit from the cloud service, it makes it a challenging task for the edge cloud data centers to work in a power saving mode. In this paper, we propose a virtual machine consolidation method to switch the idle physical servers into hibernation mode, resulting in reduced power usage. We know that edge cloud data centers offer storage as a service, in this study we address the issues pertaining to storage units in the data centers. A unique classification approach is adopted to ensure load is balanced accordingly during allocation and our main contribution is on the VM migration technique. The VM migration is aimed at consolidating the VMs based on the workload to reduced number of physical machines to mitigate the energy consumption and promoting green computing. Therefore, we name the approach as Workload Aware Virtual Machine Consolidation Method (WAVMCM). We validate the proposed method with a competitive analysis of experimental results gathered from comparing it with Artificial Intelligence based probabilistic algorithm like Simulated Annealing, Genetic Algorithm and a case of no migration. Experimental results demonstrate that the proposed WAVMCM reduces 9% active servers saving 15% of power consumption when compared to genetic algorithm based method.

Resource Request Based Energy Efficient Heuristic for Server Offloading in Cloud Computing Environment

Increasing demand for high computing power led to the establishment of large-scale data centers. A data center is a collection of millions of servers. These large-scale data centers consume a huge amount of electrical energy. Managing these servers for provisioning and de-provisioning of resources in an automatic and efficient way is a great challenge. We attempt to minimise the power consumption by reducing the number of servers and maximizing the resource utilization of the servers that are in use through virtualization of resources. We work upon Virtual Machine Consolidation for consolidating them on fewer servers for maximizing resource utilization. In this article, we propose a resource request based heuristic for offloading the overloaded servers to optimise power consumption efficiency.

A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Recent years have witnessed a rapid growth in exploiting Cloud environments to host and deliver various types of virtualized resources as on-demand services. In order to optimally use Cloud resources, the arrangement of virtual machines (VMs) in physical machines (PMs) must be performed strategically, because the placement of VMs in accordance with the available resources can reduce energy consumption, improve resource utilization and, consequently, can increase companies benefits. However, VMs could have time varying workloads, which leads to degradation of performance and power consumption. Thus, re-configuring the VMs placement is essential. Virtual machine consolidation aims to optimally use the available resources by allocating several virtual machines on a set of physical ones (PMs). To determine the PMs capacities to reallocate VMs, it is important to predict their states based on resource utilization history within each VM, and the past VMs migration traffic. However, a common limitation between existing VM consolidation approaches is the lack of information about the history of (and the future) VM migration traffic. Through this paper, we aim to propose a virtual machine consolidation approach based on the estimation of requested resources and the future VM migration traffic. We exploit the strength of Kernel Density Estimation technique (KDE) as a powerful mean to forecast the future resource usage of each VM, and AKKA toolkit as an actor-based model that allows exchanging useful information about the host’s states. We adopt a weighted-graph representation to model the history of migration traffic between PMs and to design the actor-based topology of the data center. The obtained results show the effectiveness of our approach in terms of total number of migrations and energy consumption.

Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance

This survey is an up-to-date account of the research on the performance–energy trade-off in virtualized environments, specifically in virtual machine consolidation. The factors that influence the performance and energy in consolidated data centres and the performance–energy trade-off itself are analysed. Based on these factors, we propose a categorization that classifies the most important research on performance and energy in consolidated systems. We have analysed and summarized 91 selected research works from an initial set of 1030. This article summarizes all previous surveys on the subject of virtual machine consolidation and updates them with the most recent papers in the field.

Secure distributed adaptive bin packing algorithm for cloud storage

Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud Computing’s services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). However, increasing user base and remote storage of data introduces challenges like inefficient usage of resources and insider threat to data at rest in the cloud storage. Time varying workload and data intensive applications are vulnerable to encounter such challenges while using cloud computing services. The consolidation of virtual machines to minimize the total number of active servers is a promising solution to conserve the energy but it requires efficient resource management strategies. Additionally, the encryption strategies and third party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for the cloud storage users. In this paper, we designed a distributed storage allocation architecture for fair utilization of storage resources and we also design an integrated end to end security framework for data at rest in cloud storage to eliminate insider threats.

Review of the quality of service scheduling mechanisms in cloud

Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application’s computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.