Virtualized Data Centers Research Articles

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

BASECOL2023 scientific content

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements. Aims. We aim to present the scientific content of the BASECOL2023 edition. Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the database, the type of data that can be found, the type of metadata that are used, and the Virtual Atomic and Molecular Data Centre (VAMDC) standards that are used for the metadata. Finally, we present the different datasets species by species. Results. As the BASECOL database, interconnected with the VAMDC e-infrastructure, uses the VAMDC standards, the collisional data can be extracted with tools using VAMDC standards and can be associated with spectroscopic data extracted from other VAMDC connected databases such as the Cologne database for molecular spectroscopy (CDMS), the jet propulsion laboratory molecular spectroscopy database (JPL), and the high-resolution transmission molecular absorption database (HITRAN).

Remote DasaLAB: datacenter as a laboratory for remote learning

ABSTRACT The pandemic situation banned the presence of teachers and students in the classrooms disrupting the typical teaching process. Videoconferencing services have been used successfully for lectures, but they are insufficient for hands-on laboratories where the physical presence is a requirement (i.e. to access specific hardware). Enabling remote access to the equipment allows to overcome this issue, but the students must share resources (temporarily or physically) and lack full control on the equipment. This is not optimal for some subjects, such as the practical labs on datacenter management, which are increasingly being added into the IT higher education curriculum. In this paper, we present the design and implementation of a distributed laboratory environment, so-called Remote DasaLAB, to create and deploy virtual datacenters using the student's owned resources to support the practical training of students in remote (or hybrid) mode. This environment is flexible, cheap, easy to implement with available commodity resources (the students' computers) and provides full control over the platform. We also present a specific use case on leveraging Remote DasaLAB for the practical assignments of an Operating Systems subject, in which the students build a virtual datacenter composed of multiple nodes and can properly run a containerized application distributed in these nodes.

Real-time power management in virtualized data centers using predictive analytics

Real-time power management in virtualized data centers using predictive analytics

Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS

In industry along with academia, Task Scheduling (TS) along with Resources Allocation (RA) remains a challenging issue even though lots of work was carried out in the Cloud Computing (CC) field. CC uses massive virtualized Data Centers (DC). Still, it took an enormous quantity of energy on account of the constant demand by users for resources in addition to inefficient RA techniques. The higher Energy Consumption (EC) because of improper RA inside DC can be overcome using energy-aware RA techniques. Therefore, this paper proposes a Levy flight Firefly Modified Genetic Algorithm (L3F-MGA) as well as an Entropy-based Adaptive Neuros-Fuzzy Inferences System (E-ANFIS) for energy-efficient Resources Allocation (EE-RA) in cloud structure that manages the user demands and also lessens the EC of Cloud Data Center (CDC). In the proposed work, centred on task features together with cloud resource features, the input tasks are arranged using L3F-MGA. Conversely, the Virtual Machine (VM) status is identified. After that, centred on the task feature and VM status details, the E-ANFIS allows the active VM to complete the tasks effectively. The experiments compare the proposed method's performance for effective RA with other associated works. The proposed RA technique shows EC by 11 % lesser than the existing CM-GA. The Processing Time of E-ANFIS is 8 % lesser than CM-GA, 14 % less than ANFIS, 20 % less than GA, and 16 % less than PSO.

Measuring the impact of SR-IOV and virtualization on packet round-trip time

This article explores different data plane configurations for data center servers, focusing on scenarios involving SR-IOV in conjunction with KVM virtualization. Specifically, it evaluates the performance of different ways of using SR-IOV, including MacVTap “passthrough” mode with SR-IOV and PCI Passthrough on the Linux data plane. The study aims to shed light on how SR-IOV can be leveraged to optimize round-trip time (RTT) and resource usage. Through the experiments, we confirmed the expected behaviors of the technology but also clarified and identified behavioral aspects not yet known. For example, we confirmed that SR-IOV could improve RTT and outperform more traditional configurations, but we identified that this comes at the price of utilizing more computational resources, such as the CPU, depending on the orchestrated usage configuration. Furthermore, our evaluations also found that configuring the SR-IOV MacVTap in “passthrough” mode appears to be the best configuration when CPU resources are scarce or low or when scalability and flexibility are design requirements. In this article, we also expanded the SR-IOV assessment regarding the concurrency of this feature. Through these assessments, we identified that a VM without SR-IOV in a data center with other VMs that benefit from SR-IOV is inefficient from a performance point of view of communication. Thus, in this way, this study provides valuable information on how to optimize the use of SR-IOV in KVM environments to design efficient and high-performance virtualized data centers.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

Assessment of the FAIRness of the Virtual Atomic and Molecular Data Centre following the Research Data Alliance evaluation framework

Assessment of the FAIRness of the Virtual Atomic and Molecular Data Centre following the Research Data Alliance evaluation framework

A Virtual Data Center Comparison Of Different Firewalls' Performance

Whether virtual or real, every data centre depends on its network, and the firewall is a crucial component of that network for secure communication. Different types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, can secure data centre connections. When choosing a firewall, there are many things to take into account, especially in a virtualized data centre where each firewall behaves differently depending on the situation. Reduced costs, effective administration extensibility, greater resource usage, scalability, and energy resilience are just a few benefits that virtualized data centres are supposed to produce. In this study, the use of firewalls is examined in relation to virtualized data centres. The effectiveness of several kinds of firewalls, including virtual firewalls, physical firewalls, and software .The performance of various types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, is being analysed. Virtual data centre firewall implementation and performance comparisons explain how to design and which firewall type provides the best performance. In all conditions, it was shown that kernel-integrated firewalls worked properly. Virtual machine IP addresses and networks can vary, and the kernel-based firewall can dynamically update its rules to keep pace with such changes.

Comparison Of Different Firewalls Performance In A Virtual For Cloud Data Center

Every data centre, whether virtual or physical, relies on its network, and the firewall is an essential part of that network for safe communication. Data centre connection can be protected by a variety of firewall types, including software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls. There are several factors to consider when selecting a firewall, especially in a virtualized data centre, where each firewall works differently in different situations. Virtualized data centres are intended to yield lower budgets, efficient management extensibility, better utilization of available resources, scalability, and energy resilience, among several other advantages. Virtualized data centres are the topic of this study, which examines the application of firewalls. The performance of various types of firewalls, such as software firewalls, physical firewalls, virtual appliance firewalls, and kernel-integrated firewalls, is being analysed. Virtual data centre firewall implementation and performance comparisons explain how to design and which firewall type provides the best performance. In all conditions, it was shown that kernel-integrated firewalls worked properly. Virtual machine IP addresses and networks can vary, and the kernel-based firewall can dynamically update its rules to keep pace with such changes. With its distributed firewall functionality, virtual machines can travel across hypervisors with no disruption to their security settings, if their policies remain the same. A kernel-based, distributed firewall is the best way to protect against viruses.

Critical Analysis on Detection and Mitigation of Security Vulnerabilities in Virtualization Data Centers

There is an increasing demand for IT resources in growing business enterprises. Data center virtualization helps to meet this increasing demand by driving higher server utilization and utilizing un-used CPU cycles without causes much increase in new servers. Reduction in infrastructure complexities, Optimization of cost of IT system management, power and cooling are some of the additional benefits of virtualization. Virtualization also brings various security vulnerabilities. They are prone to attacks like hyperjacking, intrusion, data thefts, denial of service attacks on virtualized servers and web facing applications etc. This works identifies the security challenges in virtualization. A critical analysis on existing state of art works on detection and mitigation of various vulnerabilities is presented. The aim is to identify the open issues and propose prospective solutions in brief for these open issues.

ReMatch: An Efficient Virtual Data Center Re-Matching Strategy Based on Matching Theory

A virtual data center (VDC) comprises multiple virtual machines (VMs) with communication dependencies represented as virtual links (VLs). These virtual components, i.e., VMs and VLs, often experience fluctuating demands across different resource types. In this article, we focus on addressing the issue of dynamic resource expansion that leads to the relocation of solution components (SCs), where a SC comprises a VM and its attached VLs, with either the VM and/or at least one of the VLs facing resource expansion. This is challenging because of the complexity involved in frequently relocating multiple dependent virtual components across the substrate network. This article presents a model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMatch</i> that aims at building an efficient remapping plan with reduced remapping cost and improved resource utilization for service providers (SPs) in polynomial time. The overall relocation problem is formulated as a one-to-many matching game with heterogeneous VM demands. Owing to the inapplicability of the classical deferred acceptance algorithm (DAA) and revised DA (RDA), we propose a modified version of the RDA (MRDA) to obtain a weakly stable assignment. Thorough simulation and analysis show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReMatch</i> outperforms the baseline algorithms considering multiple evaluation metrics.

Optimal network‐aware virtual data center embedding

SummaryRecently, the virtual data center embedding (VDCE) problem has drawn significant attention because of a growing need for efficient means of data center resource allocation. By ensuring a set of virtual data center (VDC) integration requests coming from his customers, among the main concern of an infrastructure provider is the maximization of the utilization rate of data center resources and benefits. However, existing VDCE solutions mostly focus on consolidating virtual machines in a single physical data center. Therefore, in this work, we improve the consolidated targets techniques, that consider only the virtual machines integration, by the consideration of network devices and fabrics (e.g., switches and paths/links). We consider new unreleased constraints such as multiple virtual nodes of the same request co‐location, and intermediate node requirements when a virtual link is mapped. To address the above problem, in this paper, we propose a binary linear programming‐based model, called BLP‐VDCE, to solve the VDCE problem with network‐aware consideration. This model ensures a simultaneous consolidated embedding of virtual nodes and virtual links. Extensive simulations show that solving the proposed BLP‐VDCE model can efficiently embed VDC requests with a high physical resource utilization rate.

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

GVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively.

A combined computing framework for load balancing in multi-tenant cloud eco-system

&lt;span&gt;Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.&lt;/span&gt;

Design and Implementation of Virtual Machine Migration Method in Cloud Computing

The cloud is a communication architecture that includes virtual machines, data centers, hosts, and brokers. For cloudlet execution, the broker looks for the most reliable virtual machine. Uncertainty may arise in the network as to which system is overloaded. A work technique is proposed in this study to improve the system's fault tolerance. The proposed enhancement is based on the ACO algorithm, which can determine the best virtual machine to migrate a cloudlet to. The proposed algorithm's performance is being evaluated on Cloudsim in terms of execution time and energy consumption. When compared to TESA, the simulation results showed that ACO (Ant Colony Optimization) takes the least amount of time and energy to run (Three Step Encryption Algorithm). The proposed algorithm can be used in cloud computing for load balancing.

Suggestions and regulations for designing, planning and constructing industrial parks, a case study of Vietnam

On the basis of experiences in designing, planning and constructing industrial zones in Vietnam, this paper aimed to give some suggestions and regulations for designing. The study used the qualititative method with synthesis and explanatory methods. Resources usage and environmental protection are more effective, while maximal economic and social benefits are simultaneously targeted. A concept of developing eco-industrial parks has established and evolved through time, and planning and constructing industrial zones based on current regulations in the nation. As a result, researches stated that we need to pay attention to network cloud service, big data service and investment management and meet some standards such as 100% of newly built industrial clusters have wastewater treatment stations that meet relevant national standards or technical regulations, etc. as well as certain principles such as 3R (Recycle, Reduce, Reuse). Operation services should include: cloud-based service operation providing basic cloud rental services, such as cloud hosting, cloud desktop, cloud disk, virtual data center, container services, collaborative development and Open API; SaaS Operation Service; APP Store; and space operation service to make full use of digital showrooms and free space resources, providing reservation and rental services, and demonstration services.