Bare Metal Servers Research Articles

Fifth generation core: the performance enhancement of virtual private server and bare metal

The fifth generation (5G) architecture represents the most recent advancement in mobile networks and is presently operational in various global places. Several new use cases and applications have been introduced, with a specific focus on improving throughput, reducing latency, minimising packet loss, optimising CPU usage, and maximising memory utilisation. In order to effectively address each scenario, it is necessary to integrate the most advanced technology, putting in significant effort to optimise resources and ensure system adaptability. This strategy will establish an architecture capable of accommodating many scenarios of a shared physical infrastructure by using techniques such as virtualization and cloud-based service deployment. Therefore, in this study, a test was carried out related to the performance of the 5G core network (CN) on bare metal servers and virtual private servers (VPSs). The quality of service (QoS) using Wireshark and Iperf3 is tested by utilizing ‘cpustat’ and free tools. The results of performance comparisons of these two methods on the 5G CN shows throughput values of ≥10 Gbps ≤20 Gbps, latency values of ≤4 ms, and packet loss values of 0%, in accordance with IMT 2020 standards. Thus, the ideal 5G CN services can be realized.

New Spam Filtering Method with Hadoop Tuning-Based MapReduce Na飗e Bayes

As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in terms of execution time and accuracy. Malicious email was filtered with MapReduce programming using the Naïve Bayes technique, which is a supervised machine learning method, in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied. According to the results of a comparison of the accuracy and predictive error rates of the two methods, the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.

Research of virtualized log management system in communication dispatching center of power grid

Logs happen every day. They record all kinds of events of current network and its management facilities. Communication dispatchers can use it to aware network or facility failures. In the purpose of improving awareness of communication network and its management facilities, this research is focusing on a log management system for different types of communication devices and network management servers. From log system architecture, to actual situations of log collection. Then, the distribution of system, from bare-metal servers to virtual machines. Most a virtualized log management system is deployed and tested in a dispatching center of power grid.

Monitoring Platform Evolution Toward Serverless Computing for 5G and Beyond Systems

Fifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application’s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks of monitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.

NASCENT: A Non-Invasive Solution for Detecting Utilization of Servers in Bare-Metal Cloud

Physical servers are available as-a-service in bare-metal public and private cloud platforms, and their demand has been proliferating because of the high levels of privacy and security guarantees they provide to the tenants. This raises the need for efficient management of bare-metal clouds to keep operational costs low such as by reducing energy consumption. For efficiently managing the cloud infrastructure, bare-metal cloud operators need to monitor the utilization of servers. However, the privacy and security concerns prohibit the installation of third-party monitoring agents on the servers; thus, finding the server-utilization becomes a challenge. In this work, we present NASCENT, a scalable machine-learning (ML) based non-invasive solution for finding the utilization of servers without compromising the privacy and security of bare-metal cloud tenants. Our key idea is to infer utilization from various sensor readings accessible via a server’s baseboard management controller (BMC) hardware. We evaluate the proposed solution with three regression based supervised ML algorithms in a Bare-metal-as-a-service (BMaaS) cloud. Our experimental evaluation shows that one of the ML algorithms employed in NASCENT infers the utilization with a root-mean-square error (RMSE) between 2.9 to 9.3 for different workloads. Also, the proposed solution uses minimal memory resources (19 KB) and can even run on BMC hardware which has very limited memory. We also propose a BMaaS cloud architecture that seamlessly integrates automated training and deployment of the ML algorithm in our solution into the life-cycle of bare-metal servers. NASCENT’s codebase can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/iithcandle/dhi-ojas</uri>

Cloud-based Network Virtualization in IoT with OpenStack

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

Containers Placement and Migration on Cloud System

Currently, many businesses are using cloud computing to obtain an entire IT infrastructure remotely while delegating its management to a third party. The provider of this architecture ensures the operation and maintenance of the services while offering management capabilities via web consoles.These providers offer solutions based on bare metal or virtualization platforms (mainly virtual machines). Recently, a new type of virtualization-based on containerization technology has emerged. Containers can be deployed on bare metal servers or in virtual machines. The migration of virtual machines (VMs) and Containers in Dynamic Resource Management (DRM) is a crucial factor in minimizing the operating costs of data centers by reducing their energy consumption and subsequently limiting their impact on climate change.In this article, live migration for both types of virtualization will be studied. for that, container placement and migration algorithms are proposed, which takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, a dynamic approach is suggested based on a threshold of RAM usage for host and virtual machines in the data center to avoid unnecessary power consumption. In this paper, the proposed work is compared with VM/Container placement and migration methods, the results of the experiment indicate that using container migration instead of VMs demonstrates a reduction in power consumption, and also reduces the migration time which impacts QoS and reduces SLA violation.

DeepRan: Attention-based BiLSTM and CRF for Ransomware Early Detection and Classification

Ransomware is a self-propagating malware encrypting file systems of the compromised computers to extort victims for financial gains. Hundreds of schools, hospitals, and local government municipalities have been disrupted by ransomware that already caused 12.1 days of system downtime on average (Siegel 2019). This study aims at developing a deep learning-based detector DeepRan for ransomware early detection and classification to prevent network-wide data encryption. DeepRan applies an attention-based bi-directional Long Short Term Memory (BiLSTM) with a fully connected (FC) layer to model normalcy of hosts in an operational enterprise system and detects abnormal activity from a large volume of ambient host logging data collected from bare metal servers. DeepRan also classifies abnormal activity as one of the candidate ransomware attacks by extending attention-based BiLSTM with a Conditional Random Fields (CRF) model. The Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract semantic information from high dimensional host logging data. An incremental learning technique is used to extend the model’s existing knowledge to prevent DeepRan quality degradation over time. We develop a testbed of bare metal servers and collect normal host logs of two users for 63 days (IRB-approved). 17 ransomware attacks are executed on the victim hosts, and the infected host logging data is used for validating DeepRan. Experimental results present that DeepRan produces 99.87% detection accuracy (F1-score of 99.02%) for ransomware early detection. The detector also achieves 96.5% accuracy to classify abnormal events as one of 17 candidate ransomware families. The application of incremental learning is validated as an efficient technique to enhance model quality over time.

Auto-Scale Resource Provisioning In IaaS Clouds

AbstractUsers of cloud computing technology can lease resources instead of spending an excessive charge for their ownership. For service delivery in the infrastructure-as-a-service model of the cloud computing paradigm, virtual machines (VMs) are created by the hypervisor. This software is installed on a bare-metal server, called the host, and acted as a broker between the hardware of the host and its VMs. The host is responsible for the allocation of required resources, such as CPU, RAM and network bandwidth, for VMs. Therefore, allocating resources to a VM is equivalent to finding the location of the VM on the hosts. In this paper, we propose a model for resource allocation of a datacenter that includes clusters of hosts. This model is based on the birth–death process of queueing systems and continuous-time Markov chains. We will focus on RAM-intensive VMs and consider the allocation of RAM for a VM as a job in the queueing systems. The purpose of this modeling is to keep the number of running hosts minimum while guaranteeing the quality of service in terms of response. When the utilization of active hosts reaches a predefined threshold value, a new host is added to prevent response time violation, and when host utilization is reduced to a certain threshold, one of the hosts can be deactivated. The experimental results show that, in the long run, the odds of working with more jobs are increased.

Joint Diversity and Redundancy for Resilient Service Chain Provisioning

Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of “N” active instances called replicas while redundancy provides “P” standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service Function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.

HPCCloud Seer: A Performance Model Based Predictor for Parallel Applications on the Cloud

With the continual increase in the high performance computing (HPC) market share, the need for a cheaper and widely available system rather than the expensive typical HPC systems increases. A promising alternative to HPC typical systems is the cloud computing environment which is characterised by being cheap, flexible, scalable and available. However, the cloud is based on virtualization which increases the latency to access the processing and network resources due to resource sharing. This makes the cloud an unpredictable environment to long run time programs such as HPC applications. Hence, modelling and understanding performance is essential for exploiting such environment. In this paper we propose a predictor for the execution time of the message passing interface (MPI) based applications on the cloud, as they are a major class of HPC applications. The predictor is based on an analytical performance model through considering the cloud resources as a queueing network, and the parallel applications as jobs contesting for the shared resources. The prediction based on the proposed model is measured on both a cluster of bare-metal servers and on a group of virtual machines. The overall accuracy of this prediction is 88% for 10 benchmarks, 5 from SPEC-MPI and 5 from NASA parallel benchmarks.

How secure are bare metal servers?

Bare metal servers have been around for a while now and are often seen as the perfect halfway house between a full-blown dedicated server and a shared server. They offer the same high-end performance and security you would get from a dedicated server, but with the same levels of flexibility as a shared solution.

Running Oracle WebLogic on containers

CERN IT department is providing production services to run containers. Given that, the IT-DB team, responsible to run the Java based platforms, has started a new project to move the WebLogic deployments from virtual or bare metal servers to containers: Docker on Kubernetes allow to improve the overall productivity of the team by reducing both operations time and time-to-delivery. Additionally, this framework allows us deploy our services in a fully-reproducible fashion. The scope of the project goes from the design and production of Docker images, deployment environments based on Kubernetes as well as all procedures and operations including the needed tools to hand over to users the management of their deployed applications. This article illustrates how at CERN we have faced all the technical and design aspects to run it in a production environment. That means the implementation of the solutions needed including monitoring, logging, high availability, security and traceability.

Server Structure Proposal and Automatic Verification Technology on IAAS Cloud of Plural Type Servers

In this paper, we propose a server structure proposal and automatic performance verification technology which proposes and verifies an appropriate server structure on Infrastructure as a Service (IaaS) cloud with bare metal servers, container based virtual servers and virtual machines. Recently, cloud services have been progressed and providers provide not only virtual machines but also new metal servers and container based virtual servers. However, users need to design an appropriate server structure for their requirements based on 3 types quantitative performances and users need much technical knowledge to optimize their system performances. Therefore, we study a technology which satisfies users' performance requirements on these 3 types IaaS cloud. Firstly, we measure performances of a bare metal server, Docker containers, KVM (Kernel based Virtual Machine) virtual machines on OpenStack with virtual server number changing. Secondly, we propose a server structure proposal technology based on the measured quantitative data. A server structure proposal technology receives an abstract template of OpenStack Heat and function / performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology which executes necessary performance tests automatically on provisioned user environments according to the template.

Server Selection, Configuration and Reconfiguration Technology for IaaS Cloud with Multiple Server Types

We propose a server selection, configuration, reconfiguration and automatic performance verification technology to meet user functional and performance requirements on various types of cloud compute servers. Various servers mean there are not only virtual machines on normal CPU servers but also container or baremetal servers on strong graphic processing unit (GPU) servers or field programmable gate arrays (FPGAs) with a configuration that accelerates specified computation. Early cloud systems are composed of many PC-like servers, and virtual machines on these severs use distributed processing technology to achieve high computational performance. However, recent cloud systems change to make the best use of advances in hardware power. It is well known that baremetal and container performances are better than virtual machines performances. And dedicated processing servers, such as strong GPU servers for graphics processing, and FPGA servers for specified computation, have increased. Our objective for this study was to enable cloud providers to provision compute resources on appropriate hardware based on user requirements, so that users can benefit from high performance of their applications easily. Our proposed technology select appropriate servers for user compute resources from various types of hardware, such as GPUs and FPGAs, or set appropriate configurations or reconfigurations of FPGAs to use hardware power. Furthermore, our technology automatically verifies the performances of provisioned systems. We measured provisioning and automatic performance verification times to show the effectiveness of our technology.

Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

In this paper, we propose a server architecture recommendation and automatic performance verification technology, which recommends and verifies appropriate server architecture on Infrastructure as a Service (IaaS) cloud with bare metal servers, container-based virtual servers and virtual machines. Recently, cloud services are spread, and providers provide not only virtual machines but also bare metal servers and container-based virtual servers. However, users need to design appropriate server architecture for their requirements based on three types of server performances, and users need much technical knowledge to optimize their system performance. Therefore, we study a technology that satisfies users’ performance requirements on these three types of IaaS cloud. Firstly, we measure performance and start-up time of a bare metal server, Docker containers, KVM (Kernel-based Virtual Machine) virtual machines on OpenStack with changing number of virtual servers. Secondly, we propose a server architecture recommendation technology based on the measured quantitative data. A server architecture recommendation technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology that executes necessary performance tests automatically on provisioned user environments according to the template. We implement proposed technologies, confirm performance and show the effectiveness.

OpenStack hypervisor, container and Baremetal servers performance comparison

OpenStack hypervisor, container and Baremetal servers performance comparison

Enabling dynamic data centers with a smart bare-metal server platform

Ever increasing data center complexity poses a significant burden on IT administrators. This burden can become unbearable without the help of self-managed systems that monitor themselves and automatically modify their state in order to carry out business processes according to high level objectives set by service level agreements (SLA) and policies. Among the key IT management tasks that must be automated and enhanced to realize the idea of an autonomic and highly dynamic data center, are discovery, configuration, and provisioning of new servers. In this direction, this paper describes pre-boot capabilities endowing the bare metal server with the ability to be discovered, queried, configured, and provisioned at time zero using industry standards like Common Information Model (CIM), CIM-XML, and Service Location Protocol (SLP). The capabilities are implemented as a payload of an Intel® Extensible Firmware Interface (EFI)-compliant BIOS, the Intel® Rapid Boot Toolkit (IRBT), allowing a resource manager to discover a new server during pre-boot, possibly in a bare-metal state, and then perform an asset inventory, configure the server including CPU-specific settings, and provision it with the most appropriate image. All these tasks may be carried out based on decisions taken by the resource manager according to server capabilities, application requirements, SLAs, and high-level policies. Additionally, this system uses reliable protocols, thus minimizing error possibilities. Future work is proposed, including the integration of a persistent hypervisor for enhanced management capabilities.