Server Machines Research Articles

Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series

Detecting anomalies in multivariate time series poses a significant challenge across various domains. The infrequent occurrence of anomalies in real-world data, as well as the lack of a large number of annotated samples, makes it a complex task for classification algorithms. Deep Neural Network approaches, based on Long Short-Term Memory (LSTMs), Autoencoders, and Variational Autoencoders (VAEs), among others, prove effective with handling imbalanced data. However, the same does not follow when such algorithms are applied on multivariate time-series, as their performance degrades significantly. Our main hypothesis is that the above is due to anomalies stemming from a small subset of the feature set. To mitigate the above issues in the multivariate setting, we propose forming an ensemble of base models by combining different feature selection and transformation techniques. The proposed processing pipeline includes applying a Feature Bagging techniques on multiple individual models, which considers separate feature subsets for each specific model. These subsets are then partitioned and transformed using multiple nested rotations derived from Principal Component Analysis (PCA). This approach aims to identify anomalies that arise from only a small portion of the feature set while also introduces diversity by transforming the subspaces. Each model provides an anomaly score, which are then aggregated, via an unsupervised decision fusion model. A semi-supervised fusion model was also explored, in which a Logistic Regressor was applied on the individual model outputs. The proposed methodology is evaluated on the Skoltech Anomaly Benchmark (SKAB), containing multivariate time series related to water flow in a closed circuit, as well as the Server Machine Dataset (SMD), which was collected from a large Internet company. The experimental results reveal that the proposed ensemble technique surpasses state-of-the-art algorithms. The unsupervised approach demonstrated a performance improvement of 2% for SKAB and 3% for SMD, compared to the baseline models. In the semi-supervised approach, the proposed method achieved a minimum of 10% improvement in terms of anomaly detection accuracy.

Minimum-energy virtual machine placement using embedded sensors and machine learning

Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

Virtualisasi Server Menggunakan Proxmox Untuk Mengoptimalkan Resource Server Pada SMK Bhakti Persada

The server is a media that has an important role to provide internet access services for its users. So that the network can be used optimally, a server is needed. This study aims to design server virtualization in the Vocational School Bhakti Persada with the aim of building a flexible server infrastructure that optimizes the use of server resources, so it can be adjusted to the workload so that hardware resources are optimal. This research conducted a solution using the concept of virtualization, namely the use of proxmox to design a virtual server that can minimize maintenance costs as well as in hardware procurement. The use of hardware resources provided by the server machine has not been used optimally so that the right steps are needed to overcome this problem. Research results obtained from server virtualization using proxmox to optimize server resources can manage servers efficiently in terms of time and cost.

Unsupervised feature selection using chronological fitting with Shapley Additive explanation (SHAP) for industrial time-series anomaly detection

With the development of the industrial Internet of Things (IIOT), an amount of industrial multivariate time series (IMTS) data has been collected by various sensors. IMTS data anomaly detection plays an important role in industrial process monitoring and operation condition identification. Many real-world IMTS datasets usually have a large number of redundant features, which may lead to deviation of the anomaly detection model and misunderstanding of potential feature correlation. Hence, it is essential to select features that have critical and effective effects on anomalies and operation conditions. In this paper, a feature selection that introduces knowledge-based target variables into inner feature selectors (IFSs) is proposed as a wrapper method, referred to as ILSFS. The IFS is constructed by integrating a timing fitter, which is established based on the long and short-term memory (LSTM) network, with the Shapley Additive explanation (SHAP) assisted to eliminate unimportant features. The experiments were conducted on two datasets: the vertical roller mill dataset (VMD) of a slag milling factory in Jiangsu Province and the public server machine dataset (SMD). Compared with None feature selection, SVM-RFE, LGBM -Boruta, Genetic-based methods, and other SHAP-assisted feature selection methods such as XGBSVFIR and ShapHT+, the experiment results highlighted the proposed methodology ILSFS improves the best F1 score of each the above baselines by 0.2799, 0.2208, 0.2647, 0.1882, 0.3775, 0.2765 for the VMD and 0.1475, 0.0586, 0.0567, 0.0638, 0.0519, 0.2250 for the SMD on the downstream anomaly detection. The best precision of the ILSFS for the VMD and the SMD was 0.8224 and 0.9499, respectively, while its best recall was 0.7353 and 0.4902, respectively.

Using a nested virtualization tool for teaching VPN fundamentals

A common problem of undergraduate courses about security and computer networks is the difficulty of providing practical exercises to students. Although different approaches have been followed to solve this problem, it is still an open issue. In this work, we first discuss several possible open-source simulation/emulation/virtualization tools that could be used in the design of a lab session for teaching Virtual Private Networks (VPN) fundamentals based on OpenVPN. Next, we describe how a hands-on lab about VPNs can be carried out with the selected virtualization tool, called NETinVM. The proposed lab starts installing the OpenVPN packages in client and server machines, as in a real-case scenario. Although in this case both client and servers are Kernel-based Virtual Machines (KVM) connected to networks inside NETinVM. The nested virtualization included in NETinVM allows to deploy all the required types of VPNs within the virtual machine, allowing the students to reproduce the lab without the need of a network infrastructure, and following exactly the same process required in a real scenario. The evaluation results show that the level of learning achieved by the students through this lab session seems to be high, and they consider this lab useful for their professional development, showing that virtualization technology can help in computer network education.

On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers

Data breaches and cyberattacks represent a severe problem in higher education institutions and universities that can result in illegal access to sensitive information and data loss. To enhance the security of data transmission, Intrusion Prevention Systems (IPS, i.e., firewalls) and Intrusion Detection Systems (IDS, i.e., packet sniffers) are used to detect potential threats in the exchanged data. IPSs and IDSs are usually designed as software programs running on a server machine. However, when the speed of exchanged data is too high, this solution can become unreliable. In this case, IPSs and IDSs designed on a real hardware platform, such as ASICs and FPGAs, represent a more reliable solution. This paper presents a packet sniffer that was designed using a commercial FPGA development board. The system can support a data throughput of 10 Gbit/s with preliminary results showing that the speed of data transmission can be reliably extended to 100 Gbit/s. The designed system is highly configurable by the user and can enhance the data protection of information transmitted using the Ethernet protocol. It is particularly suited for the security of universities and research centers, where point-to-point network connections are dominant and large amount of sensitive data are shared among different hosts.

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.

Specialised treatment of colorectal cancer in certified cancer centres: Do patients really have to travel further?

In Germany, many cancer patients are treated outside of cancer centres certified by the German Cancer Society (DKG) resulting in underuse of these facilities and inferior oncological treatment. One way to address this issue would be to restructure the healthcare landscape by following the Danish approach that limits cancer treatment to specialized hospitals. Such an approach would have an impact on the travelling times to treatment centers. The present study determines the impact on patient travel times using the example of colorectal cancer. For the present analysis, data from structured quality reports (sQB) and from patients insured with the AOK who underwent resection of the colon or rectum during 2018 were used. In addition, data from the DKG regarding an existing certification of a colorectal cance centre were used. Travel time was defined as the time patients spent in an average car with average traffic from the midpoint of the ZIP code of their residence to the coordinates of the hospital. The coordinates of the hospitals and the midpoints of the ZIP codes were obtained by querying the Google API. Travel times were calculated with a local Open Routing Machine server. The statistical programs R and Stata were used for analyses and cartographic representations. In 2018, nearly half of all patients with colon cancer were treated at the hospital nearest to their place of residence, of whom approximately 40% were treated at a certified colorectal cancer centre. Overall, only about 47% of all treatments took place at a certified colorectal cancer centre. Travel time to the chosen treatment site averaged 20 minutes. It was minimally shorter (18 minutes) if no certified centre was chosen and minimally longer (21 minutes) for those whose treatment took place in a certified colorectal cancer centre. Modeling of redistributions of all patients to certified centres resulted on an average travel time of 29 minutes. Even if treatment were limited to specialized hospitals, treatment close to home would still be guaranteed. Regardless of certification, parallel structures can be identified, especially in metropolitan areas, which indicate a potential for restructuring.

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

Light: A Compatible, high-performance and scalable user-level network stack

As the number of CPU cores and the speed of Ethernet NICs keep increasing on server machines, the network stack in the kernel has become the bottleneck for applications demanding very high throughput and ultra-low latency. Recently there is a trend towards moving the network stack out of the kernel. However, most kernel-bypass network stacks discard POSIX APIs that legacy applications have been built on, and the intricate work of transplanting applications brings the barrier to real-world deployment of kernel-bypass stacks. In this work, we propose Light, a novel user-level network stack, which not only gains highly scalable performance on multi-core server, but also achieves compatibility with legacy applications. For compatibility, Light realizes efficient blocking APIs in the user space, intercepts network-related APIs in a non-intrusive manner, and uses the FD space separation technique for proper API redirection. For high performance and scalability, Light adopts lock-free shared queue based inter-process communication and full connection affinity to reduce overheads of system call, cache miss, etc. Experiments demonstrate that many types of legacy applications could run on Light without modifying their source code. Compared with the latest kernel stack, Nginx on Light achieves up to 2.86× throughput and 78.2% lower tail latency (99.9th percentile) with 14 CPU cores.

Federal learning edge network based sentiment analysis combating global COVID-19

As one of the important research topics in the field of natural language processing, sentiment analysis aims to analyze web data related to COVID-19, e.g., supporting China government agencies combating COVID-19. There are popular sentiment analysis models based on deep learning techniques, but their performance is limited by the size and distribution of the dataset. In this study, we propose a model based on a federal learning framework with Bert and multi-scale convolutional neural network (Fed_BERT_MSCNN), which contains a Bidirectional Encoder Representations from Transformer modules and a multi-scale convolution layer. The federal learning framework contains a central server and local deep learning machines that train local datasets. Parameter communications were processed through edge networks. The weighted average of each participant’s model parameters was communicated in the edge network for final utilization. The proposed federal network not only solves the problem of insufficient data, but also ensures the data privacy of the social platform during the training process and improve the communication efficiency. In the experiment, we used datasets of six social platforms, and used accuracy and F1-score as evaluation criteria to conduct comparative studies. The performance of the proposed Fed_BERT_MSCNN model was generally superior than the existing models in the literature.

A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

The traditional method of saving energy in Virtual Machine Placement (VMP) is based on consolidating more virtual machines (VMs) in fewer servers and putting the rest in sleep mode, which may lead to the overheating of servers resulting in performance degradation and cooling cost. The lack of an accurate and computationally efficient model to describe the thermal condition of the data center environment makes it challenging to develop an effective and adaptive VMP mechanism. Although recently, data-driven approaches have acted successfully in model construction, the shortage of clean, adequate, and sufficient amounts of data put limits their generalizability. Moreover, any change in the data center configuration during operation, makes these models prone to error and forces them to repeat the learning process. Thus, researchers turn to applying model-free paradigms such as reinforcement learning. Due to the vast action-state space of real-world applications, scalability is one of the significant challenges in this area. In addition, the delayed feedback of environmental variables such as temperature give rise to exploration costs. In this paper, we present a decentralized implementation of reinforcement learning along with a novel state-action representation to perform the VMP in the data centers to optimize energy consumption and keep the host temperature as low as possible while satisfying Service Level Agreements (SLA). Our experimental results show more than 17% improvement in energy consumption and 12% in CPU temperature reduction compared to baseline algorithms. We also succeeded in accelerating optimal policy convergence after the occurrence of a configuration change.

A sustainable and secure load management model for green cloud data centres

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%.

Novel Approaches for Resource Management Across Edge Servers

Edge computing aims at reducing computation and storage across the cloud and provides service with reduced latency. Edge devices can be mobile devices, routers, cameras, printers or any Internet of Things (IoT) devices that generate vast amounts of data. The processing of these data is done by virtual machines (VMs) present in the edge servers that are located within close proximity of the edge devices. This work proposes two models which predict resource contention at the edge servers, namely, a Dynamic Markov model for Resource Contention Prediction in Edge Cloud (DMRCP) and a Hybrid Cascade of Regression and Markov model for Resource Contention Prediction (CRMRCP). In DMRCP, a history matrix is updated based on the CPU utilization of a Virtual Machine (VM). This history matrix is used to update a transition probability matrix. This matrix is used to predict the future state of the VM. In the CRMRCP approach, the past CPU utilization values of the virtual machines in the edge servers are used for predicting a set of future CPU utilization values using linear regression, polynomial regression, lasso regression and ridge regression. Then, the predicted future CPU utilization values are used by the dynamic and the second-order Markov models to classify the state of the edge servers as overloaded, underloaded or normally loaded. In both the approaches, the VMs that may cause resource contention are predicted and are migrated to other edge servers such that the destination edge server does not get overloaded after the migration. The DMRCP method is compared with the first-order and the second-order Markov models and the number of VM migrations is analysed to evaluate the performance. The number of VM migrations in the CRMRCP method is compared with that in the second-order Markov model. The overall results prove that the number of VM migrations for the DMRCP is 52.9% less compared to the first-order Markov model and 21.1% less when compared to the second-order Markov model. The number of VM migrations in CRMRCP is reduced by 81.8% when ridge regression cascaded with the second-order Markov model is used.

Multivariate Variance-Based Genetic Ensemble Learning for Satellite Anomaly Detection

Proactive diagnosis of spacecraft issues and response to conceivable hazards has attracted considerable interest. Hidden anomalies in satellites can cause overall system degradation. In multivariate time series anomaly detection (AD), several sensors report to a fusion center at each timestamp. Each of these sensors measures a specific feature of the satellites. In this paper, we first leverage the eigenvalues of covariance metrics of a multivariate time series to determine the anomaly score at each timestamp. Then, the anomaly scores are fed to various machine learning models. After that, we employ a Multivariate Variance-based Genetic Ensemble (MVVGE) learning method to ensemble the results of several models based on their corresponding performance. More specifically, we ensemble different Neural Network (NN), Random Forest (RF), and Linear Regression (LR) models based on their associated variance to catch the anomalies of three datasets, i.e., two satellite datasets: i) Soil Moisture Active Passive (SMAP), ii) Mars Science Laboratory rover (MSL), and Server Machine Dataset (SMD). Given that establishing the model confidence region is the bottleneck of our approach, we use an approximated version of the Bayesian NNs (BNN)s for acquiring confidence intervals of NN-based models. For RF and LR confidence intervals, we use an empirical method and bootstrapping, respectively. Simulation results confirm the superiority of our proposed approach compared to other methods.

Product Inspection System on Single Evidence

Thailand requires the Log management server or known as Log Server to store and manage computer traffic information for 90 days according to Computer-related Crime Act B.E. 2560 [1]. Therefore the log server requires to test according to the law. The Product inspection system has been created and used for Log server testing according to the National Electronics and Computer Technology Center standard (NTS 4003.1-2560). To test the Log Server, need a highly trained and experienced tester to evaluate the log server manually. The burden occurs in finding highly trained and experienced testers. To generate knowledge on the Log server test does take years to build. Furthermore, training a tester to acquire competent skills and knowledge would take even longer time. The test evaluation has been done manually, which takes a great time to complete as the tester has to evaluate the evidence one by one without any helping tools. In addition, the chance of an error from emotional or biased judgment can occur and cause the inconsistency verification result. Thus, to overcome the mentioned burdens, an automated log server inspection, and verification system has been developed. This system is called a “Computer traffic data storage system (SAS Log)” [2], which evaluates the evidence based on the label on the log server machine. The system would diminish the unambiguous and inconsistent verification result with Machine Learning in Image Processing [3] and Ontology technology [4].

Scalable solutions for the Control Unit of the KM3NeT DAQ system

The neutrino telescopes of KM3NeT are being incrementally expanded, and will reach their final size in the coming years. New versions of optical modules running new versions of firmware and new instrumentation for calibration are being introduced in the originally repetitive lattice. The inner architecture and data flow of the Control Unit of the KM3NeT telescopes is described, with information about computational and architectural complexity. The current goal is to control two full blocks of the KM3NeT/ARCA detector, i.e. 4370 CLBs and 128340 photomultipliers for 230 detection units, with a single mid-range commercial server machine. The system is designed with software protections and fault tolerance for hardware failure.

Serverless utilization in microservice e-learning platform

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances.

Job scheduling problem in fog-cloud-based environment using reinforced social spider optimization

Fog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.