Adaptive Virtual Machine Research Articles

Adaptive virtual machine placement: a dynamic approach for energy-efficiency, QoS enhancement, and security optimization

Adaptive virtual machine placement: a dynamic approach for energy-efficiency, QoS enhancement, and security optimization

I-Neat: An intelligent framework for adaptive virtual machine consolidation

With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine (VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation. However, the performance of different algorithms is related with the characteristics of the workload and system status; some algorithms are suitable for Central Processing Unit (CPU)-intensive workload and some for web application workload. Therefore, an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms. Neat is an open-source dynamic VM consolidation framework, which is well integrated into OpenStack. However, it cannot conduct dynamic algorithm scheduling, and VM consolidation algorithms in Neat are few and basic, which results in low performance for energy saving and Service-Level Agreement (SLA) avoidance. In this paper, an Intelligent Neat framework (I-Neat) is proposed, which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system. The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms. The algorithm library is designed based on a template, and in addition to the algorithms of Neat, I-Neat adds six new algorithms to the algorithm library. Furthermore, the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code. Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.

Multi-resource collaborative optimization for adaptive virtual machine placement.

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform.

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Cloud Data Centers (CDCs) have become a vital computing infrastructure for enterprises. However, CDCs consume substantial energy due to the increased demand for computing power, especially for the Internet of Things (IoT) applications. Although a great deal of research in green resource allocation algorithms have been proposed to reduce the energy consumption of the CDCs, existing approaches mostly focus on minimizing the number of active Physical Machines (PMs) and rarely address the issue of load fluctuation and energy efficiency of the Virtual Machine (VM) provisions jointly. Moreover, existing approaches lack mechanisms to consider and redirect the incoming traffics to appropriate resources to optimize the Quality of Services (QoSs) provided by the CDCs. We propose a novel adaptive energy-aware VM allocation and deployment mechanism called AFED-EF for IoT applications to handle these problems. The proposed algorithm can efficiently handle the fluctuation of load and has good performance during the VM allocation and placement. We carried out extensive experimental analysis using a real-world workload based on more than a thousand PlanetLab VMs. The experimental results illustrate that AFED-EF outperforms other energy-aware algorithms in energy consumption, Service Level Agreements (SLA) violation, and energy efficiency.

Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centers

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement’s performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications ”E-PRWA” in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing.

Thermal-aware adaptive VM allocation considering server locations in heterogeneous data centers

Virtualized data centers usually consist of heterogeneous servers which have different specifications (performance). Though there usually exist unused heterogeneous servers in such data centers, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not exploit the unused servers to cool down hot servers. In this paper, we propose a novel DTM technique which adaptively exploits external computing resources (unused servers with different performance) as well as internal computing resources (unused CPU cores in the server) available in heterogeneous data centers. Additionally, we also propose to consider locations of the servers when migrating VMs (Virtual Machines) among servers in a rack, which has a large impact on the on-chip temperatures and performance due to the heat conduction; when VMs run on the two closest servers in the rack, the ambient temperature of servers is up to 6.2° higher, compared to the case where VMs run on the two farthest servers, so that on-chip temperature more rapidly increases causing up to 13.5% of performance degradation due to more frequent thermal throttling. When the temperature of a CPU core in a server exceeds a pre-defined thermal threshold, our proposed technique estimates the impact of VM migrations on performance (e.g., performance degradation due to the physical machine migrations and/or core migrations of VMs). Depending on the estimated performance impact of VM migrations, our technique adaptively employs the following three methods: (1) a method that migrates a VM to another distant server with different performance, (2) a method that migrates VMs among CPU cores in the server, and (3) a DVFS-based method. In our experiments, our proposed technique improves performance by 15.1% and saves system-wide EDP by 22.9%, on average, compared to a state-of-the-art DVFS-based DTM technique, satisfying thermal constraints.

Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers

Efficient resource management in a Cloud data center relies on minimizing energy consumption and utilizing physical resource efficiently while maintaining the service-level agreement (SLA) at its highest level. To achieve this goal, dynamically consolidating virtual machines (VMs) is considered a promising method, because it eliminates the hotspots resulting from overloaded hosts and switches the underloaded hosts to sleep mode through the live migration of VMs. However, during the consolidation, each VM migration consumes additional resource, leading to performance degradation and SLA violation. To address this issue, this study proposes a novel adaptive performance-to-power-ratio (PPR)-aware dynamic VM consolidation framework based on both the predicted resource utilization and PPR of the heterogeneous hosts to resolve the trade-off of performance and energy. The proposed framework consists of four stages: (1) host overload detection based on residual available computing capacity; (2) selection of the appropriate VMs for migration from the overloaded hosts based on minimum data transfer; (3) host underload detection based on multi-criteria Z-score approach; (4) allocating the VMs selected for migration from the overloaded and underloaded hosts based on the modified power-aware best-fit decreasing algorithm. To validate the reliability and scalability of the proposed method, we performed experimental evaluation in both real and simulated environments. The experimental results demonstrate that the proposed approach can reduce the energy consumption effectively and ensure maximal conformity to the quality of service (QoS) requirements across heterogeneous infrastructures, in comparison with the existing competitive approaches.

Ada-Things: An adaptive virtual machine monitoring and migration strategy for internet of things applications

Internet of Things (IoT) applications running on mobile devices are subject to the low storage capacity and short battery lifetime. Edge clouds (EC) provide an approach to offload computation tasks and reduce network latency for these applications. The main challenge in such ecosystems is how to efficiently monitor and allocate VM resources to realize load balancing among edge clouds. In this paper, we propose Ada-Things, an adaptive VM monitoring and live migration Strategy for IoT applications in edge cloud architecture. The basic idea of Ada-Things is that the migration method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current memory dirty page rate in IoT applications, Ada-Things can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations (application generality and performance imbalance) of existing VM migration methods in edge cloud. Evaluation results show, compared with traditional methods, Ada-Things can significantly reduce the total migration time by 21%, the VM downtime by 38% and the amount of pages transferred by 29% in average.

CONTEXTUAL COMPUTATION AUGMENTATION IN MOBILE CLOUD COMPUTING

Mobile cloud computing has emerged as a promising solution for augmenting the cognitive capabilities of the resource constraint smart mobile devices. Although a lot of efforts has been made for augmenting the computational capabilities of these hand-held smart devices, but there are significant problems which need to addressed e.g. enabling the individual mobile users to contextually tune their computing requirements and efficiently utilizing cloud resources while offering the quality of service (QoS). This paper proposes a context based computation augmentation framework to balance these competing goals. It incorporates individual contextual demands in its optimization engine to dynamically adjust its offloading decisions. In addition, it also proposes an adaptive virtual machine (VM) provisioning technique for efficient utilization of cloud resources and maintaining the QoS. We evaluated the design space of the proposed system by extending CloudSim simulator. The experimental results show that the proposed system is able to provide contextual augmentation services with QoS and low costs.

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

SummaryPrivate Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.

Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism

To overcome the problem of virtual machine (VM) scheduling in cloud computing, a novel adaptive VM resource scheduling algorithm based on auction mechanism is presented by considering multiple factors including network bandwidth and auction deadline. First, the sequencing of the clients’ bids is conducted in the given competition deadline. Second, the client group is screened and corresponding VM resource is configured according to the minimum costs of the cloud service providers. Finally, the final payment price can be figured by considering the levels of average payments and competitive payments, so that the tasks clients request can be completed with the given VM resource. The simulation experimental results show that the proposed algorithm can effectively enhance the quality of service of the cloud environment, the profits of cloud service providers and the resource utilization rate of VM.

An Adaptive Virtual Machine Location Selection Mechanism in Distributed Cloud

Mobile cloud computing has recently become a new paradigm for the utilization of a variety of shared mobile resources via wireless network environments. However, due to the inherent characteristics of mobile devices, a limited battery life, and a network access requirement, it is necessary for mobile servers to provide a dynamic approach for managing mobile resources efficiently in mobile cloud computing environments. Since on-demand job requests occur frequently and the number of mobile devices is drastically increased in mobile cloud computing environments, a different mobile resource management method is required to maximize the computational power. In this paper, we therefore propose a cooperative, mobile resource sharing method that considers both the inherent properties and the number of mobile devices in mobile cloud environments. The proposed method is composed of four main components: mobile resource monitor, job handler, resource handler, and results consolidator. In contrast with conventional mobile cloud computing, each mobile device under the proposed method can be either a service consumer or a service provider in the cloud. Even though each device is resource-poor when a job is processed independently, the computational power is dramatically increased under the proposed method, as the devices cooperate simultaneously for a job. Therefore, the mobile computing power throughput is dynamically increased, while the computation time for a given job is reduced. We conduct case-based experiments to validate the proposed method, whereby the feasibility of the method for the purpose of cooperative computation is shown.

An Adaptive Virtual Machine Location Selection Mechanism in Distributed Cloud

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Holistic datacenter energy minimization operation should consider interactions between computing and cooling source specific usage patterns. Decisions like workload type, server configuration, load, utilization etc., contributes to power consumption and influences datacenter's thermal profile and impacts the energy required to control temperature within operational thresholds. In this paper, we present an adaptive virtual machine placement and consolidation approach to improve energy efficiency of a cloud datacenter; accounting for server heterogeneity, server processor low-power SLEEP state, state transition latency and integrated thermal controls to maintain datacenter within operational temperature. Our proposed heuristic approach reduces energy consumption with acceptable level of performance.

Multi-tier service differentiation by coordinated learning-based resource provisioning and admission control

Multiple Internet applications are often hosted in one datacenter, sharing underlying virtualized server resources. It is important to provide differentiated treatment to co-hosted applications and to improve overall system performance by efficient use of shared resources. Challenges arise due to multi-tier service architecture, virtualized server infrastructure, and highly dynamic and bursty workloads. We propose a coordinated admission control and adaptive resource provisioning approach for multi-tier service differentiation and performance improvement in a shared virtualized platform. We develop new model-independent reinforcement learning based techniques for virtual machine (VM) auto-configuration and session based admission control. Adaptive VM auto-configuration provides proportional service differentiation between co-located applications and improves application response time simultaneously. Admission control improves session throughput of the applications and minimizes resource wastage due to aborted sessions. A shared reward actualizes coordination between the two learning modules. For system agility and scalability, we integrate the reinforcement learning approach with cascade neural networks. We have implemented the integrated approach in a virtualized blade server system hosting RUBiS benchmark applications. Experimental results demonstrate that the new approach meets differentiation targets accurately and achieves performance improvement of applications at the same time. It reacts to dynamic and bursty workloads in an agile and scalable manner.

Energy saving and load balance strategy in cloud computing

An adaptive Virtual Machine(VM) dynamic migration strategy of soft energy-saving was put forward to optimize energy consumption and load balance in cloud computing. The energy-saving strategy adopted Dynamic Voltage Frequency Scaling(DVFS) as the static energy-aware technology to achieve the sub-optimized static energy saving,and used online VM migration to achieve an adaptive dynamic soft energy-saving in cloud platform. The two energy-saving strategies were simulated and compared with each other in CloudSim platform,and the data were tested on PlanetLab platform. The results show that: Firstly,the adaptive soft and hard combination strategy in energy-saving can significantly save 96% energy;secondly,DVFS + MAD_MMT strategy using Median Absolute Deviation(MAD) to determine whether the host is overload,and choosing VM to remove based on Minimum Migration Time(MMT),which can save energy about 87. 15% with low-load in PlanetLab Cloudlets than that of experimental environment; finally,security threshold of 2. 5 in MAD_MMT algorithm can consume the energy efficiently and achieve the adaptive load balancing of virtual machines migration dynamically.

Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.