Physical Data Center Research Articles

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.

Business Environment in the Context of Cloud Computing: A Review

Many businesses now rely on cloud environments for both personal and corporate use due to the industry's expanding trend of constantly improving technology. The on-demand, pay-as-you-go delivery of IT resources through the internet is known as cloud computing. This indicates that the user just needs to make payments based on how much they use. In layman's terms, it refers to the idea of using the Internet to rent the processing power and storage capacity of a computer from another location. Users can get technology services like computing power, storage, and databases from a cloud provider on a pay-as-you-need basis rather than purchasing and maintaining their own physical data centers and servers. Our object of this article is to peer-review and examines numerous cloud tools, applications, and more to decide which is the best choice for both individual users and businesses. We have investigated the advantages that cloud computing offers.

A Systematic Review of Cloud Storage Services- A Case Study on Amazon Web Services

Background/Purpose: Cloud computing is the buzz word today as it has made its presence felt in many fields, such as business, education, industries, e-commerce to list a few. The concept of cloud storage is the IT service provided on the internet which allows the flexibility of paying for only the actual usage that is in short it is pay as you go. The companies now have the option to rent computing power, storage, and databases from the service provider. This avoids huge financial investment for procuring, operating, and maintaining licensed software, physical data centers and servers. To overcome the challenges in general the storage medium, it is important upgrade to cloud concept. Nowadays, new business language has seen tremendous success by putting their services and data on the internet without relying on any physical devices. There are many service providers who cater this segment. This paper provides an analysis of the company Amazon Web Services which is the major service providers in recent times. Objective: In this case study we focus on the cloud storage services of Amazon Web Service. Design/Methodology/Approach: The related information and details for this case study on Amazon Web Services was obtained from various scholarly articles published in various peer reviewed journals, conferences and company websites. White papers have provided additional information. Findings/Result: The study of this paper focus on the relevance and importance of cloud storage in the present era. The need, requirements and necessity of various services provided by AWS, financial plan, different products of AWS, Leading clients and types of cloud services are discussed. Originality/Value: The study provides a brief overview of Amazon Web Services, nature and manner of various data collection, data management, information about cloud storage and various cloud services of AWS. Paper Type: Case Study on the importance of storage and computation requirements for services on web, various service providers, with special reference to Amazon Web Services.

A design in system architecture based on mobile cloud computing for a virtual try-on solution

Cloud computing is an emerging technology in this digital century. It provides an excellent but low-cost source for database storage, computing power, applications, and services through an internet-delivered cloud platform. Thanks to the cost savings in investing and maintaining physical data centers, as well as the stability of Quality of Service (QoS), it has no restrictions on company size or sector for enterprises to shift their operations to a cloud platform. The fashion industry, particularly the fashion e-commerce sector, is a case study in leveraging the cloud platform via a technology called “Virtual try-on” (VTO). VTO solution allows fashion brands to increase the shopping experience, however, requires installing and maintaining a bulky system for implementation. There are different methods and approaches to design architectures using cloud computing, however, there have not been many studies addressing tasks related to machine learning that uses the high Graphics Processing Unit (GPU) encountered in VTO solutions. To design a scheduler that could optimize the system performance while lowering operational expenses in VTO solutions, this research proposes a system to (1) handle synchronous model and asynchronous model separately and clearly, (2) perform multi-layered task processing architecture by hashing task ID and implementing a queue management system. This method would satisfy three major requirements: (1) Avoid complex hardware requirements for users, (2) Ensure the system stability and the ease of horizontal and vertical extension, and (3) Protect user information privacy.

Simulation of load balancing algorithms using cloud analyst for distinct regions IT resources

Cloud computing is currently on the rise as new technology and as a new business model, according to experts. Data archiving, servers (both real-world and virtual), telecommunications, machine learning tools for data analysis, and a cloud services provider's (CSP) data center are all examples of cloud computing resources that are available on demand over the internet. Cloud computing allows client devices to access data from faraway servers over the internet, which is referred to as "cloud computing." Rather than purchasing, operating, and maintaining physical data centers and servers, we can take advantage of technology services provided by third parties. A pay-per-use pricing structure is in place. It is spreading over the world simply because of its simple and service-oriented business approach. It delivers the greatest computing service to the user and makes the execution of large-scale activities much easier than before. Through the use of a cloud analyst tool, this study provides an overview of the evaluation of the response time and processing time of several existing algorithms that are used in cloud computing.

Joint IT-Facility Optimization for Green Data Centers via Deep Reinforcement Learning

The data center market grows rapidly with the increase of data and its corresponding applications (e.g., machine learning, cloud storage, Internet of Things, and so on). The growth is boosted recently due to the shift of activities online during the COVID-19 pandemic. Reducing the energy consumption of data centers faces various challenges that are further aggravated by the tropical conditions with high temperature and humidity in the tropics like Singapore. The prevailing siloed approach of operating the information technology (IT) and the facility systems separately has resulted in wasteful over-provisioning. The recently proposed approaches for energy usage minimization under various constraints including thermal safety scale poorly with the data center size and often result in non-optimal solutions. To advance the state of the art, we apply deep reinforcement learning (DRL) to address the scalability problem and achieve optimality over a long time horizon in reducing data center energy usage. In particular, we deploy the data-driven deep model and physical rule based model in lieu of the physical data center during the training and validation phases to manage the thermal safety risks caused by DRL's strategy of learning from errors.

High Availability of Datacenter in Karachi, Pakistan

The datacenter is the core infrastructure of today's world. Every data center should have many resources and applications that are running for several decades or even more. Many failures happen in the physical datacenter in on-premises environments. In this research paper evaluating the datacenter by having the dataset provided by Premier Systems (Pvt.) Ltd. This dataset is having all the failures and datacenter related issues from Jan-2016 to Dec-2019 in Karachi, Pakistan. This research performed the Linear Regression via the Microsoft Azure Machine Learning Studio for the machine learning model. It would allow us to know which fault will more and which is not for the concern requirement. This experiment would have the Feature Engineering feature in Microsoft Azure Machine Learning Studio, which will automatically apply the filters required. After knowing which the central issue are related to any physical data center in Karachi. This research allows to handle the required precaution in datacenters of Karachi, Pakistan.

Annotated Dataset for Anomaly Detection in a Data Center with IoT Sensors.

The relative simplicity of IoT networks extends service vulnerabilities and possibilities to different network failures exhibiting system weaknesses. Therefore, having a dataset with a sufficient number of samples, labeled and with a systematic analysis, is essential in order to understand how these networks behave and detect traffic anomalies. This work presents DAD: a complete and labeled IoT dataset containing a reproduction of certain real-world behaviors as seen from the network. To approximate the dataset to a real environment, the data were obtained from a physical data center, with temperature sensors based on NFC smart passive sensor technology. Having carried out different approaches, performing mathematical modeling using time series was finally chosen. The virtual infrastructure necessary for the creation of the dataset is formed by five virtual machines, a MQTT broker and four client nodes, each of them with four sensors of the refrigeration units connected to the internal IoT network. DAD presents a seven day network activity with three types of anomalies: duplication, interception and modification on the MQTT message, spread over 5 days. Finally, a feature description is performed, so it can be used for the application of the various techniques of prediction or automatic classification.

Efficient Compute-Intensive Job Allocation in Data Centers via Deep Reinforcement Learning

Reducing the energy consumption of the servers in a data center via proper job allocation is desirable. Existing advanced job allocation algorithms, based on constrained optimization formulations capturing servers’ complex power consumption and thermal dynamics, often scale poorly with the data center size and optimization horizon. This article applies deep reinforcement learning to build an allocation algorithm for long-lasting and compute-intensive jobs that are increasingly seen among today's computation demands. Specifically, a deep Q-network is trained to allocate jobs, aiming to maximize a cumulative reward over long horizons. The training is performed offline using a computational model based on long short-term memory networks that capture the servers’ power and thermal dynamics. This offline training approach avoids slow online convergence, low energy efficiency, and potential server overheating during the agent's extensive state-action space exploration if it directly interacts with the physical data center in the usually adopted online learning scheme. At run time, the trained Q-network is forward-propagated with little computation to allocate jobs. Evaluation based on eight months’ physical state and job arrival records from a national supercomputing data center hosting 1,152 processors shows that our solution reduces computing power consumption by more than 10 percent and processor temperature by more than 4°C without sacrificing job processing throughput.

Efficiently Consolidating Virtual Data Centers for Time-Varying Resource Demands

Data center virtualization is a flexible and efficient way to enable multiple users to share the common resources of a physical data center (DC). For efficient sharing, virtual data center (VDC) embedding is a vital problem that should be carefully addressed. However, existing studies on VDC embedding mostly assume that the capacity of each VDC is fixed, but do not consider the time-varying feature of resource demands. Considering the fact that the resource demands of most enterprise IT services exhibit the time-varying feature, resource allocation based on the fixed capacity assumption would cause a great inefficiency. To overcome this inefficiency, we propose a new VDC consolidation scheme that takes into account the time-varying feature of resource demands when embedding VDCs. We first develop a resource demand prediction model for each VDC using the Long Short-Term Memory (LSTM) neural network, which is used to predict the real-time resource demands of VDCs at different future moments. Based on the predicted resource demands, we then embed VDCs whose peaks and valleys of resource demands stagger each other onto common physical servers and links, such that the required physical resources can be minimized under the condition that all the resource demands of different VDCs are satisfied at all the different moments. An integer linear programming (ILP) model and a resource demand correlation-based heuristic algorithm are also developed for the proposed scheme. Simulation results show that the proposed consolidation scheme can significantly improve resource utilization in a DC. It can save up to 25 percent of physical servers and 29 percent of physical links used for accommodating the same requests as compared to a scheme assigning resources based on the fixed capacity assumption.

Scalable constraint-based virtual data center allocation

Constraint-based techniques can solve challenging problems arising in highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To address this problem, we introduce Netsolver, a system for VDC allocation that is based on constraint solving. Netsolver represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. Netsolver scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, Netsolver can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Finally, we show how Netsolver can be extended with additional optimization constraints, such as VM affinity and hotspot minimization, demonstrating the flexibility of our approach.The performance and flexibility of Netsolver are made possible by our formalization of the VDC allocation problem in terms of multi-commodity flows, and the corresponding efficient handling of network flow problems in the underlying constraint solvers. This shows the importance of supporting flow-based constraints, which are more mature in ILP- vs. SMT-based constraint solving.

Vocational and Technical Education Status of the Industry 4.0 Process in Turkey

It is only possible for our country to become a determining actor in global competition, to ensure sustainable development in all areas, to use all its resources efficiently and effectively, only by upbringing qualified manpower. As in developed countries, studies and policies aimed at increasing the quality of Vocational and Technical Education are of great importance for the brighter future of our country. While the innovations in information and communication technologies, which are led by technological developments, continue at full speed, it is a fact that education is the most affected sector. Following this rapid change in accordance with the conditions of the day and creating the infrastructures parallel to it must be one of the main subjects of education. Synchronizing with the needs of the modern age and cooperating with all relevant sectors is important to ensure competitiveness. It is necessary for the young population, which is the most valuable resource of our country, to receive education in Vocational and Technical Education environments equipped with technology and facilities suitable for the conditions of the age for sustainable, social and economic development. Human-centered development requires an updated education system in line with technological developments. In this direction, developing strategies that are suitable for regional conditions is inevitable for the young population to have a dynamic structure. The basis of this development in industrial applications and information technologies is the new generation of software and hardware. The current era is known as the Industry 4.0 process. This process incorporates these technologies, which have lower energy consumption, less carbon emissions, can operate with higher security and are environmentally friendly compared to their previous versions. At the same time, Industry 4.0 incorporates many components such as the internet of things, data processing, cyber physical processing, augmented reality, and data center and automation robots. In this study, the new model vocational and technical education work will be emphasized in which skills suitable for the age of technology and informatics, personal entrepreneurship and teamwork are supported, and students are equipped with features such as problem solving and system design. Keywords: Industry 4.0, vocational education, technical education, educational technologies, next generation education. DOI: 10.7176/JSTR/5-10-11

A Hybrid Method for Reduction of Energy Consumption in Cloud Networks

Cloud computing consists of physical data centers each of which including hundreds or thousands of computers. A key enabler technology is virtualization. The Virtualization allows us to separate virtual machines in a way that each of these so-called virtualized machines can be configured with a number of hosts according to the type of application of the user. It is also possible to dynamically alter the allocated resources of a virtual machine. One of the most important challenges in terms of cloud computing is to maintain an optimized load balance in cloud environment on servers because this issue is critical for energy consumption. In this paper, we propose a hybrid method for reduction of energy consumption in cloud networks which enables us to save energy through finding a suitable configuration for placement of virtual machines and through being aware of special features of virtual environments for scheduling and balancing their dynamic loads by the help of live migration method. The experimental results show that the proposed method reduced power consumption when comparing with other algorithms.

Hamiltonian Graph Analysis – Mixed Integer Linear Programming (HGA-MILP) Based Link Failure Detection System in Optical Data Center Networks

Abstract The internet services have become more popular rapidly over the decades. This is because of the inspiration created by new content-hungry applications on end-user devices like smart phones. Wavelength division multiplexed mesh networks are the optical backbone, working as the aggregating point of huge volume traffic, interconnecting several access networks and end users. The need of virtual data center is to retain services in case of failures such as optical link failures and resource or power outage in data centers that occurs in the cloud infrastructure and improve the survivability. The physical infrastructure may disconnect the virtual machines and physical data centers. This leads to the reduction in computational as well as communicational capabilities of the cloud. To overcome these issues, an integer linear programming model called hamiltonian graph analysis – mixed integer linear programming (HGA-MILP) is proposed in this work by utilizing hamiltonian graph model. The main motive of this research is to design a survivable data centre network mapping problem against physical link failures. Use of hamiltonian path methodology is helpful to organize data and improve network performance.

Energy-Aware Virtual Data Center Migration

Recently, the concept of virtual data center (VDC) has attracted significant attention from researchers. VDC is made up of virtual nodes and virtual links with guaranteed bandwidth. It offers elasticity and flexibility, which means VDC can adjust resources dynamically according to different requirements. Existing studies focus on how to design the optimal embedding algorithm to achieve high success rate for the virtual data center request. However, due to the resource of physical data center changes over time, the optimal solution may become sub-optimal. In this paper, we study the problem of virtual data center migration and propose an energy-aware virtual data center migration algorithm, called CA-VDCM-ACO. This novel algorithm leverages the migration technique to further reduce the energy consumption with the success rate for the physical data center guaranteed. The extensive experiments show that our algorithm is very effective to reduce the energy consumption.

Toward SLAs Guaranteed Scalable VDC Provisioning in Cloud Data Centers

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.

Science data visualization in planetary and heliospheric contexts with 3DView

We present a 3D orbit viewer application capable of displaying science data. 3DView, a web tool designed by the French Plasma Physics Data Center (CDPP) for the planetology and heliophysics community, has extended functionalities to render space physics data (observations and models alike) in their original 3D context. Time series, vectors, dynamic spectra, celestial body maps, magnetic field or flow lines, 2D cuts in simulation cubes, etc, are among the variety of data representation enabled by 3DView. The direct connection to several large databases, the use of VO standards and the possibility to upload user data makes 3DView a versatile tool able to cover a wide range of space physics contexts. The code is open source and the software is regularly used at Masters Degree level or summer school for pedagogical purposes. The present paper describes the general architecture and all major functionalities, and offers several science cases (simulation rendering, mission preparation, etc.) which can be easily replayed by the interested readers. Future developments are finally outlined.

Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization

Cloud computing has emerged in recent years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). As the number of cloud service provider increases, there exists a demand to dynamically provision virtual data centers (VDC) on top of the infrastructure provider’s physical data centers. This research addresses problems related to energy and resource efficiently embedding virtual data centers inside physical data centers under dynamic resource allocation conditions, in which VDCs continuously join and leave the system. Dynamic VDC embedding is challenging as it is an NP-hard problem that should meet multiple objectives. In this article, we propose heuristic joint VDC embedding – server consolidation approaches as one solution for that problem. Evaluation results show that the joint approach outperforms existing ones in terms of resource and energy efficiency and can keep system complexity acceptable.

Towards Trusted Location SpecificInformation for Cloud Servers

Every physical datacenter is located somewhere on the globe. It is subject to the local legislation, including data protection related laws. A cloud service can be delivered from a set of datacenters in several locations. Responsibilities of the service provider include ensuring that legal and agreed constraints are respected also by its subcontractors, for example, those providing cloud computing resources. Several countries have data protection legislation that restrict sharing copies of sensitive data to locations that do not have compliant legislation. This paper presents ideas to dependably detect location specific information, like the legislation properties, of the current physical host server executing a service.

Experimental investigations of air conditioning solutions in high power density data centers using a scaled physical model

The widespread use of data centers, the dramatical increase of the data center power density and the need for improving cooling system efficiency to maintain reliable operation temperature and save cooling energy make the study of data center thermal management an urgent issue. In the current paper, three different configurations for thermal management solution of high power density data centers are investigated, compared and evaluated. A scaled physical model data center has been designed and constructed for the sake of the study using the theory of scale modeling of air flow experiments. The results showed that (i) by using aisle partition and aisle containment the rack inlet temperature can be reduced by 3–13% and 13–15.5% for aisle partition and aisle containment configurations, respectively; (ii) the intake air temperature reduction increases with increasing power density; and (iii) using aisle partitions and aisle containment with raised floor improves the data center cooling performance.