Creation Of Virtual Machines Research Articles

Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuum

As an increasing number of Distributed Machine Learning (DML) tasks are hosted on cloud platforms in the edge-cloud continuum, Data Centers (DCs) with massive data and computational requirements have become one of the world’s largest energy consumers, leading to significant carbon emissions. Reducing energy consumption and carbon emissions is an extremely crucial and challenging issue for the sustainable development of cloud service providers. While utilizing renewable energy can help reduce the carbon emissions of DCs, the intermittent and unstable nature still causes DCs to rely heavily on high-carbon brown energy. For the resource-intensive and delay-tolerant DML tasks, this paper introduces multi-renewable energy in the geo-distributed continuum to address this issue, the spatiotemporal complementarity maximizes the renewable energy utilization and compensates for time-dependent energy differences with geographic advantages. Additionally, considering the dynamic differences in carbon intensity and electricity prices across distributed DCs in the continuum, we propose an energy and carbon-aware algorithm called ECMR for scheduling heterogeneous virtual machine creation tasks of DML among multi-clouds in different time zones. It is demonstrated that compared with the baseline methods, the ECMR significantly reduces the total power consumption, energy cost, and carbon emission of data centers while maintaining an acceptable service quality. The utilization of renewable energy in data centers has been significantly improved to 90.8% by flexibly leveraging the spatiotemporal complementarity of multi-renewable energy. Compared with existing competing algorithms, the proposed method exhibits significant improvements with an achieved average response time of 12.6 ms, and a task failure rate of 1.25%.

Development of an automated system for testing a cloud service for deploying virtual machines using modern monitoring tools

The object of this study is a service for managing virtual machines in a cloud environment. When developing and operating such a service, it becomes necessary to assess its availability and reliability for compliance with the selected quality level that the client can count on. This paper presents a developed system that allows testing the availability of a cloud service for managing virtual machines. The method of integration with the existing monitoring system at the enterprise using open source software in order to reduce the cost of development and operation is considered. A test case for deploying and removing a virtual machine using a graphical user interface has been developed and implemented, and triggering criteria have been defined. The requirements for the architecture and implementation of the system based on the production statistics of the virtual machine creation service using the Prometheus monitoring system are collected and analyzed. The novelty of the research lies in the development of a new method of testing a cloud service for managing virtual machines in order to increase its reliability and availability. Based on this method, a system for testing virtual machines is described and implemented, as well as a method for integration into the monitoring system of the Intel cloud service. During the operation of cloud environments with the help of this system, problem areas were identified in the architecture of the virtual machine creation service, which made it possible to optimize the system operation in a timely manner. The described method is an effective way to test cloud services, and can also be used to analyze and improve reliability and availability.

Confidence interval‐based overload avoidance algorithm for virtual machine placement

AbstractVirtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

A Systematic Review of Live VM Migration Techniques for Energy Management

The present computing era has been inclined towards providing services to the customers all round the clock and is driven by utilization of existing resources in its entirety to assist service providers in extracting maximized profits. The key technique in achieving the automated services despite of geographical locations is creation of virtual machines using virtualization hypervisor. Though it entails several benefits of increased resource utilization, but it faces challenges of energy mitigation and related migration overheads originated due to balancing of load on the physical servers, fault handling in VM or server, transfer of irrelevant data, etc. If the total time and energy invested in every single VM migration can be minimized, it can give better results in term of energy consumption, total downtime, and migration time. Thus, the paper outlines a detailed review about various components of VM migration along with existing techniques and related aspects.

TARA: temperature aware online dynamic resource allocation scheme for energyoptimization in cloud data centres

Cloud data centres, which are characteristic of dynamic workloads, if not optimized for energy consumption, may lead to increased heat dissipation and eventually impact the environment adversely. Consequently, optimizing the usage of energy has become a hard requirement in today's cloud data centres wherein the major part of energy consumption is mostly attributed to computing and cooling systems. Motivated by which this paper proposes an online algorithm for dynamic resource allocation, namely, temperature aware online dynamic resource allocation algorithm (TARA). TARA demonstrates a novel algorithm design to adapt dynamic resource allocation based on the temperature of a data centre using computational fluid dynamics (CFD). Also, TARA demonstrates a new dynamic resource reclaim strategy for making efficient resource allocations leading to efficient energy consumptions in dynamic environments. The proposed algorithm provides optimal resource allocation considering energy efficiency without being overwhelmed by online dynamic workloads. The optimal energy-efficient dynamic resource allocation for online workloads eventually optimizes the computing and cooling energy consumption. We show through theoretical analysis the correctness, efficiency and optimality bounds given as $TARA(P) \leq 2OPT(P)$, relative to the optimal solution provided by offline dynamic resource allocation algorithm $(OPT(P))$. We show through empirical analysis that the proposed method is efficient and significantly saves energy by 26\% when the data centre utilization is 100\% compared to batched reclaim. The performance analysis shows significant improvement in optimizing computing and cooling efficiency. TARA can be used in multiple areas of on-demand dynamic resource allocation in cloud computing like resource allocation for virtual machine creation, resource allocation for virtual machine migrations, and virtual resources assignment for elastic cloud applications.

Забезпечення функціонування системи прогнозування атмосферних забруднень у хмарній інфраструктурі шляхом автоматизації створення віртуальних машин

The paper improves the web service Povitrya aimed at forecasting air pollution in case of man-made disasters by saving the configured CALPUFF atmospheric transport model in a pre-configured image and integrating into the system of software tools for the automatic creation of virtual machines from a pre-saved image after the user request. To do this, an image based on the Ubuntu operating system was configured in the Ukrainian National Grid Infrastructure, to-gether with the necessary CALPUFF atmospheric transport model libraries. Based on the saved image, one can quickly create and run a virtual machine and start the calculations. After the cal-culation, the Povitrya system removes the virtual machine, freeing up cloud resources for other tasks. The example of test calculations shows that due to the transfer of the module of calcula-tion of atmospheric transport to the cloud infrastructure it was possible to reduce the calculation time of the CALPUFF model by almost 2 times, and the download time of weather forecast data was reduced by more than 10 times. In total, the system calculation time after the user's request was reduced by 4 times for the test configuration. In addition, the resilience of the system to cloud infrastructure failures has been increased. The created archive with the operating system and configured for the CALPUFF model runs can also be transferred to other private clouds (for example, Amazon, Google Cloud Platform, Microsoft Azure) and their virtual machines could be used. Thus, the developed web system corresponds to modern trends in the implementation of cloud computing technologies, if necessary, allows scaling and can be adapted to other pri-vate or public cloud computing infrastructures. The system is available for registered users by the link: http://cloud-2.bitp.kiev.ua/ airsystem_english/airsystem.html.

Novel framework for secured bulk creation of virtual machine in IaaS platform

Novel framework for secured bulk creation of virtual machine in IaaS platform

Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.

A Comprehensive Survey On Cloud Computing Simulators

Cloud Computing is one of the upcoming technologies which has gotten the attention of many researchers and investor. But cloud computing still faces challenges because it is not economical and impractical for research institutions and industries to set up a physical cloud for research and experiments on it (cloud computing). Due to this, the researchers have chosen to test their contributions with simulators. Therefore, the purpose of this study is to perform a survey on existing cloud simulators. These cloud simulators aid in modeling cloud application through the creation of virtual machine, data Centre, and other thing which can be easily added and configured to it in order to provide stress free analysis. Till this present time, many cloud simulators with various features have been proposed and available for use. In this paper a comprehensive study has been performed on major cloud simulators by highlighting their features, strength and weakness through analysis. After which comparative analysis was done on the simulation, from the study, none of the simulators have the feature to simulate mobile cloud computing issues. This study has not been published anywhere else.

Virtual Machine Migration in Cloud Computing using Artificial Intelligence

Cloud Computing is known as the most fast evolving computing platform which is the future of supercomputing. A time will come when everyone would be on the cloud network and at that time, it would be essential for the cloud network to perform well. Cloud is also a computing server and hence, it takes every order as million instruction set. These instruction sets are often referred as Jobs. Scheduling an instruction set or job requires a lot of computing and one wrong placement may lead to wastage of energy units. The proposed work has taken these matters in a very serious manner and has designed an architecture diagram which deal with the job scheduling process from start to end. The proposed algorithm covers placement of the job at server, monitoring of the server to prevent them from overloading and when they are exhausted from jobs, the creation of Virtual Machine. The presented algorithm improves the Modified Best Fit Decreasing Algorithm by introducing artificial intelligence to it. Job handling has been done by using one of the finest swarm intelligence techniques known as Cuckoo search Algorithm that monitors the performance of the servers or host in order to check that they do not get overloaded. The proposed architecture has been evaluated on the basis of energy consumption, Service Level Agreement violation and total number of migrations.

Performance Analysis of Load Balancing Algorithms using Microsoft Windows Azure Cloud Platform

As we are living in technology dominating era, the role of cloud computing as one of the rapidly emerging technologies is highly indispensable. This cloud computing holds load distribution concept, which is usually addressed as a challenging phenomenon, to handle multiple requests which are coming from geographically differentiated locations. There are various load balancing mechanisms exits for the effective distribution of the work load. This load balancing mechanism takes the advent of the virtualization approach on resources to optimize the utilization of the resources. In virtualization, the creation of virtual machine and mapping the same to the suitable request makes the cloud computing a prominent technology. This paper work considers various load balancing mechanisms for the allocation of resources and also shows a comparative study on them with the help of an example scenario. These load balancing algorithms are applied on applications like real-time systems and prioritized medical field related data storage system. This research work is implemented in Windows Azure Platform as Cloud Operation System under load balancing component.

Використання веб-орієнтованого віртуального середовища Proxmox в педагогічних закладах освіти

В статті розглядаються засоби для створення віртуальних машин з використанням веборієнтованої системи Proxmox VE за допомогою трьох відкритих технологій віртуалізації, а саме: KVM, QEMU, LXC. Використання Proxmox VE у закладах освіти може мати такі переваги, як покращення рівня обслуговування комп’ютерного обладнання, спрощення управління і захисту даних, зниження вартості апаратних засобів та електроживлення, а також переваги віртуалізації під час розробки в середовищах тестування. Саме використання веб-орієнтованого інтерфейсу системи дозволяє здійснювати управління віртуальними машинами з використанням сучасних підходів у віртуалізації, тому у статті більш детально описано використання та створення віртуальних машин за допомогою відкритих технологій віртуалізації.

MDesk: a scalable and reliable hypervisor framework for effective provisioning of resource and downtime reduction

With the advent of cloud computing and rapid integration of different architectures to form hybrid systems, the need for creation of virtual machines with the existing system is essential, to process voluminous data for a wide variety of application sets, and provide optimal solutions to their clients. This paper presents a framework for providing optimal utilization of resources through a hypervisor technique. This framework helps to reduce downtime of resources and improve machine utilization. The framework creates more than one virtual machine on a single physical machine. The performance of the partitioned machine is being examined using various signature cryptographic hash algorithms. Results show that the proposed framework is efficient and secure. The technique brings in reduced computation and supports better scalability of the virtual machines in bare metal.

Reducing distributed JR program start‐up time via extending JR's operation abstraction

SummaryThis paper shows how to simplify and speed up distributed JR program start‐up. We accomplish this goal by extending JR's operation abstraction so that it includes the creation of virtual machines and remote objects, the key components in distributed JR programs. This extension is conceptually simple, fits well with the rest of the JR language, and provides additional flexibility. Extending JR's operation abstraction yields a considerably less complex solution and a simpler implementation than other approaches, which would have led to redundant or ad‐hoc language mechanisms. The extension also allows for reduced distributed JR program start‐up costs because the creation of virtual machines and remote objects can now be done in parallel. This paper also describes how we have simulated this extension and have implemented a prototype that provides this extension. These reflect closely what we would need to do to actually include the extension within the standard JR implementation. Using the simulation and prototype, we obtained performance data on a few typical distributed applications written to use the extension. The data show large reductions, ranging from about 50% to 95%, in distributed JR program start‐up times. Our experiments also explored a few strategies to further reduce start‐up times, but, among our strategies, the straightforward strategies seem the best. This paper also discusses design alternatives for creating virtual machines and remote objects. Our experience suggests that the proposed extension to JR's operation abstraction is actually worthwhile to add to the standard JR language and implementation.

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.

Managing virtual machines with Vac and Vcycle

We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

RJ: a Java package providing JR‐like concurrent programming

SummaryThe JR concurrent programming language extends Java with a richer concurrency model, by adding several new types and statements. JR provides dynamic remote virtual machine creation, dynamic remote object creation, remote method invocation, dynamic process creation, rendezvous, asynchronous message passing, semaphores, concurrent invocation, and shared variables. This paper presents RJ, a package for Java that provides JR‐like features. The paper gives an overview of RJ and its key features; describes the implications of RJ's design, including how RJ provides additional, useful flexibility; discusses the implementation of RJ; and gives qualitative and quantitative evaluations of our work with respect to feasibility and usability, experimentation, migration, and performance. RJ has been successful in meeting these goals and in providing insight into the trade‐offs between using a concurrent programming language versus using the equivalent concurrent package. Our work has yielded a few surprises in dealing with some concurrent programming language features, in understanding the run‐time performances of JR versus RJ programs, and in obtaining some additional, useful flexibility for concurrent programming applications. Copyright © 2015 John Wiley & Sons, Ltd.

Towards Efficient Virtual Appliance Delivery with Minimal Manageable Virtual Appliances

Infrastructure as a Service systems use virtual appliances to initiate virtual machines. As virtual appliances encapsulate applications and services with their support environment, their delivery is the most expensive task of the virtual machine creation. Virtual appliance delivery is a well-discussed topic in the field of cloud computing. However, for high efficiency, current techniques require the modification of the underlying IaaS systems. To target the wider adoptability of these delivery solutions, this article proposes the concept of minimal manageable virtual appliances (MMVA) that are capable of updating and configuring their virtual machines without the need to modify IaaS systems. To create MMVAs, we propose to reduce manageable virtual appliances until they become MMVAs. This research also reveals a methodology for appliance developers to incorporate MMVAs in their own appliances to enable their efficient delivery and wider adoptability. Finally, the article evaluates the positive effects of MMVAs on an already existing delivery solution: the Automated Virtual appliance creation Service (AVS). Through experimental evaluation, we present that the application of MMVAs not only increases the adoptability of a delivery solution but it also significantly improves its performance in highly dynamic systems.

Energetic Resource Allocation Framework Using Virtualization in Cloud

Virtualization provides an efficient solution to the objectives of the cloud computing paradigm by facilitating creation of Virtual Machines (VMs) over the underlying physical servers, leading to improved resource utilization and abstraction. Virtualization refers to creating a virtual version of a device or a resource such as a server, a storage device, network or even operating system where the mechanism divides the resource into one or more execution environments. To analyse the behaviour of a Cloud data centres: utilization, availability, waiting time, and responsiveness. The factors that a cloud provider must take into account are elasticity, scalability, live migration of VMs and performance isolation. Live migration of VMs, the process of dynamically transferring a virtual machine across different servers on the fly, has proved to represent a new opportunity to enable responsive and dynamic resource management in modern data centres. Through this algorithm to achieve migrations of VMs on the basis of overloading that occurs a physical server in cloud data centres. As indicated the results, the algorithm provides relatively less performance degradation that occurs due to VM migrations. The SLA violation is also less compared to other techniques and this means the cloud provider will incur less cost from VM migrations. Further, MHOD algorithm performs host selection and VM reallocation quicker than the existing algorithms. Maintenance