Dynamic Virtual Machine Research Articles

Dynamic virtual machine allocation policy in cloud computing complying with service level agreement using CloudSim

Cloud computing provides services over the internet and provides application resources and data to the users based on their demand. Base of the Cloud Computing is consumer provider model. Cloud provider provides resources which consumer can access using cloud computing model in order to build their application based on their demand. Cloud data center is a bulk of resources on shared pool architecture for cloud user to access. Virtualization is the heart of the Cloud computing model, it provides virtual machine as per application specific configuration and those applications are free to choose their own configuration. On one hand, there is huge number of resources and on other hand it has to serve huge number of requests effectively. Therefore, resource allocation policy and scheduling policy play very important role in allocation and managing resources in this cloud computing model. This paper proposes the load balancing policy using Hungarian algorithm. Hungarian Algorithm provides dynamic load balancing policy with a monitor component. Monitor component helps to increase cloud resource utilization by managing the Hungarian algorithm by monitoring its state and altering its state based on artificial intelligent. CloudSim used in this proposal is an extensible toolkit and it simulates cloud computing environment.

Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method

In a data center, virtual machine consolidation has been proposed to improve the resource utilization and energy efficiency. An effective and efficient virtual machine consolidation method should achieve an appropriate balance among multiple goals, including guaranteeing service quality, reducing energy consumption and maximizing resource utilization. This problem is a multi-objective optimization problem with multiple resource constraints. To solve this problem, we propose an energy-aware dynamic virtual machine consolidation (EC-VMC) method that migrates virtual machines while satisfying constraints on the probabilities of multiple types of resources being overloaded. In our method, a series of algorithms for selecting and placing virtual machines to be migrated are utilized, with constraints on the probabilities of various resources in a physical machine being overloaded. Our algorithms integrate and cooperate similarly to artificial bee colony foraging behavior to perform an optimized search for the mapping relation between virtual machines and physical machines for consolidation. Extensive simulation is conducted to compare our EC-VMC method with previous virtual machine consolidation methods. The simulation results demonstrate that the EC-VMC method effectively overcomes the deficiencies of some existing heuristic algorithms and is highly effective in reducing VM migrations and energy consumption of data centers and in improving QoS.

Performance Analysis for Pareto-Optimal Green Consolidation Based on Virtual Machines Live Migration

Huge energy requirement of cloud data centers is prime concern. Dynamic Virtual Machine (VM) consolidation based on VM live migration to switched-off or put some of the under-loaded host Physical Machines (PMs) into a low power consumption mode can significantly save energy in data centers and achieve green cloud computing. Performance overheads imposed on source and destination hosts during and after VM live migration is the main focus of research. Existing VM consolidation approaches are inefficient regarding VM live migration time, application downtime, VM pre and post-migration overheads which results in Quality of Service (QoS) degradation. So, near-optimal solution which optimizes these overheads is main challenge. This paper discusses the causes of VM live migration performance overheads and comparison of different overhead optimization techniques on the basis of parameters like accuracy and migration cost. Pareto-Optimal solution is proposed to eliminate the VM performance overheads.

Integration of Openstack cloud resources in BES III computing cluster

Cloud computing provides a new technical means for data processing of high energy physics experiment. However, the resource of each queue is fixed and the usage of the resource is static in traditional job management system. In order to make it simple and transparent for physicist to use, we developed a virtual cluster system (vpmanager) to integrate IHEPCloud and different batch systems such as Torque and HTCondor. Vpmanager provides dynamic virtual machines scheduling according to the job queue. The BES III use case results show that resource efficiency is greatly improved.

Mobile Edge Computing Empowers Internet of Things

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

Dynamic VM allocation in a SaaS environment

Given the costs associated with a cloud infrastructure, dynamic scheduling of virtual machines (VMs) can significantly lower costs while providing an acceptable service level. We develop a series of forecasting models for predicting demand for VMs in a cloud-based software used as a software-as-a-service (SaaS). These models are then used in a periodic-review provision model which determines how many VMs should be provisioned or de-provision at each inspection interval. A simple provisioning heuristic model is also proposed, whereby a fixed reserve capacity of VMs is continuously maintained. We evaluate and compare the performance of these models for different model parameters using historical data from the Virtual Computing Laboratory (VCL) at North Carolina State University.

One‐on‐one contract game–based dynamic virtual machine migration scheme for Mobile Edge Computing

AbstractIn the evolution toward fifth‐generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever‐increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one‐on‐one contract game model and develop a learning‐based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

A Heuristic-Based Approach for Dynamic VMs Consolidation in Cloud Data Centers

Cloud computing providers have to deal with the energy-performance trade-off: minimizing energy consumption, while meeting service level agreement (SLA) requirements. This paper proposes a new heuristic approach for the dynamic consolidation of virtual machines (VMs) in cloud data centers. The fast best-fit decreasing (FBFD) algorithm for intelligent VMs allocating into hosts and dynamic utilization rate (DUR) algorithm for utilization space and VM migration are successfully proposed. We performed simulations using PlanetLab and GWDG data center workloads to compare our approach against the existing models. It has been observed that the FBFD heuristic algorithm produces better results compared to modified BFD algorithm in terms of energy consumption and SLA violation. Additionally, the time complexity of FBFD algorithm is significantly improved from the order of O(\(m\,*\,n\)) to O(\(m\,*\,\log _2{n}\)). Furthermore, leaving some rates of capacity in the physical machines by the proposed DUR algorithm for VMs to be extended reduces the number of migrations which in turn improves the energy consumption and SLA violation. Our heuristic approach is evaluated using CloudSim and the results show that it performs better than the current state-of-the-art approaches.

Support for spot virtual machine purchasing simulation

With the rapid progress of cloud computing technology, a growing number of big data application providers begin to deploy applications on virtual machines rented from infrastructure as a service providers. Current infrastructure as a service provider offers diverse purchasing options for the application providers. There are mainly three types of purchasing options: reserved virtual machine, on-demand virtual machine and spot virtual machine. The spot virtual machine is a specific type of virtual machine that employs a dynamic pricing model. Because can be stopped by the infrastructure as a service providers without notice, the spot virtual machine is suitable for large-scale divisible applications, such as big data analysis. Therefore, spot virtual machine is chosen by many big data application providers for its low rental cost per hour. When spot virtual machine is chosen, a major issue faced by the big data application providers is how to minimize the virtual machine rental cost while meet service requirements. Many optimal spot virtual machine purchasing approaches have been presented by the researchers. However, there is a shortage of simulators that enable researchers to evaluate their newly proposed spot virtual machine purchasing approach. To fill this gap, in this paper, we propose SpotCloudSim to support for dynamic virtual machine pricing model simulation. SpotCloudSim provides an extensible interface to help researchers implement new spot virtual machine purchasing approach. In addition, SpotCloudSim can also study the behavior of the newly proposed spot virtual machine purchasing approaches. We demonstrate the capabilities of SpotCloudSim by using three spot virtual machine purchasing approaches. The results indicate the benefits of our proposed simulation system.

Topology and Application Aware Dynamic VM Management in the Cloud

Cloud computing continues to mature and more applications continue to be deployed in public clouds. Client applications deployed in the cloud should automatically scale up and down to match changing workload demands, though they must be careful to ensure that sufficient resources are provisioned to achieve performance objectives. The cloud provider, on the other hand, attempts to reduce costs by reducing power consumption by consolidating load onto fewer, highly utilized machines. In this work, we introduce an algorithm that integrates both application autoscaling and dynamic virtual machine (VM) allocation into a single algorithm in order to achieve the goals of both cloud provider and client. Further, we consider multi-VM applications, such as multi-tiered web-based applications, and extend the integrated algorithm to take the network topology into account when placing or migrating applications. The goal is to reduce VM-to-VM communication latency; our focus is on trying to contain applications within the same racks. We evaluate our work through simulation, showing that the integrated algorithm can achieve better application performance with a significant reduction in virtual machine live migrations, and the topology-aware extension successfully places applications within a single rack.

An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements.

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

Versatile workload-aware power management performability analysis of server virtualized systems

Abstract The widespread integration of virtualization technologies in data centers has enabled in the last few years several benefits in terms of operating costs and flexibility. These benefits maybe boosted through join optimization of power management (PM) and dependability for virtualized systems. This indeed involves developing appropriate models to better understand their performability behavior whenever they are exposed to predictable (e.g. rejuvenation) and unpredictable breakdowns. We propose in this paper a performability analysis of server virtualized systems (SVSs) using a workload-aware PM mechanism based on non-Markovian Stochastic Reward Nets (SRNs) modeling approach. This analysis investigates interactions and correlations between several modules involving workload-aware PM mechanism, dynamic speed scaling processing, virtual machine (VM) and virtual machine monitor (VMM) both subject to software aging, failure and rejuvenation. We show through numerical results, using quantitative and qualitative metrics, how performance, power usage and efficiency are impacted by workload-aware PM mechanism. We show also how judicious choice of tunable attribute (i.e. Timeout ) of the proposed PM mechanism with respect to workload can lead to a good power-performance trade-off.

NDynamic Framework for Secure VM Migration over Cloud Computing

In the centralized cloud controlled environment, the decision-making and monitoring play crucial role where in the host controller (HC) manages the resources across hosts in data center (DC). HC does virtual machine (VM) and physical hosts management. The VM management includes VM creation, monitoring, and migration. If HC down,the services hosted by various hosts in DC can`t be accessed outside the DC. Decentralized VM management avoids centralized failure by considering one of the hosts from DC as HC that helps in maintaining DC in running state. Each host in DC has many VM`s with the threshold limit beyond which it can`t provide service.To maintain threshold , the host`s in DC does VM migration across various hosts. The data in migration is in the form of plaintext, the intruder can analyze packet movement and can control hosts traffic. The incorporation of security mechanism on hosts in DC helps protecting data in migration. This paper discusses an approach for dynamic HC selection, VM selection and secure VM migration over cloud environment.

Dynamic Virtual Machines Placement in a Cloud Environment by Multi-Objective Programming

Dynamic Virtual Machines Placement in a Cloud Environment by Multi-Objective Programming

Fuzzy logic-based VM selection strategy for cloud environment

In dynamic virtual machine consolidation (DVMC), when a host is overloaded, a list of virtual machines (VMs) must be selected to migrate. VM selection strategy has a substantial impact on the performance of DVMC algorithm, and a good number of strategies have been proposed in the literature. As different VM selection approaches offer distinct advantages, we explore and apply fuzzy logic in this research to innovate a new VM selection method which will provide a balanced and better result in all quality of service (QoS) measures. With this goal, we have designed the fuzzy VM selection using three metrics. We have devised the membership functions and inference rules based on the real cloud workload data, e.g., PanetLab. We implement our novel fuzzy VM selection method in CloudSim toolkit and compare the performance with the existing methods. Simulation results demonstrate a balanced performance by trading off power and QoS parameters.

MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

Need for a Transport API in 5G for Global Orchestration of Cloud and Networks Through a Virtualized Infrastructure Manager and Planner [Invited

The new 5G paradigm seeks for a scalable architecture that is able to efficiently manage the increasing volume of traffic generated by smart devices to be processed in a distributed cloud infrastructure. To this end, coordinated management of the network and the cloud resources forming an end-to-end system is of great importance. Software defined networking and network function virtualization architectures are the key enablers for integrating network and cloud resources, enabling cross optimization on both sides. This optimization requires efficient resource allocation algorithms, which take into account computing and network resources. In this paper, we propose an end-to-end orchestration architecture for distributed cloud and network resources aligned with the European Telecommunications Standards Institute management and orchestration architecture. The proposed architecture includes the virtual infrastructure manager and planner (VIMaP) component to enable dynamic resource allocation for interconnected virtual instances in distributed cloud locations. A heuristic algorithm for dynamic virtual machine graphs resource allocation is included to validate the VIMaP architecture and exploit its functionalities. Moreover, the control orchestration protocol is included between the architecture components to offer end-to-end transport services. Finally, the proposed architecture is experimentally validated, and the heuristic algorithm performance is evaluated.

WAYFINDER: parallel virtual machine reallocation through A* search

Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient path finding in VM placement problems for cloud computing environments of moderate scale with results indicating the solution is reasonable for managing hundreds of VMs. We present an overview of known approaches to dynamic VM placement and discuss their shortcomings with respect to dynamic reallocation. We then describe a novel design and implementation of a heuristic search algorithm to determine optimal sequential migration plans to transition from a given VM-to-host allocation to an arbitrary desired allocation state. We then elaborate nuances of A* application to this domain along with our simulation-based validation approach. Finally, this work demonstrates a novel and highly effective technique for exploiting migration parallelism in order to rapidly achieving VM reallocation convergence suitable for continual workload rebalancing in cloud data centers.

An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data

SummaryBecause of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on‐demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first‐class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy‐aware manner. In this paper, we propose an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.