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

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3745 ◽  
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Mathematical Modeling ◽  
Data Center ◽  
Virtual Machines ◽  
Temperature Sensors ◽  
Network Activity ◽  
Sensor Technology ◽  
Systematic Analysis ◽  
Feature Description ◽  
Virtual Infrastructure ◽  
Passive Sensor

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.

A Methodology for Redistributing the Operating Virtual Machines among the Servers in a Data Center

Vestnik MEI ◽  
2018 ◽  
pp. 98-105
Author(s):  
Alexander A. Larin ◽  
◽  
Leonid I. Abrosimov ◽  
Keyword(s):  
Data Center ◽  
Virtual Machines

A network of low-cost temperature sensors for real-time monitoring of combined sewer overflow

2021 ◽  
Author(s):  
Mathias Riechel ◽  
Oriol Gutierrez ◽  
Silvia Busquets ◽  
Neus Amela ◽  
Valentina Dimova ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Temperature Sensors ◽  
Sensor Technology ◽  
Detection Accuracy ◽  
Combined Sewer Overflow ◽  
Monitoring Equipment ◽  
Flow Monitoring ◽  
Combined Sewer ◽  
Sewer Overflow

<p>The H2020 innovation project digital-water.city (DWC) aims at boosting the integrated management of water systems in five major European cities – Berlin, Copenhagen, Milan, Paris and Sofia – by leveraging the potential of data and digital technologies. The goal is to quantify the benefits of a panel of 15 innovative digital solutions and achieve their long-term uptake and successful integration in the existing digital systems and governance processes. One of these promising technologies is a new generation of sensors for measuring combined sewer overflow occurrence, developed by ICRA and IoTsens.</p><p>Recent EU regulations have correctly identified CSOs as an important source of contamination and promote appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects on receiving waters. Traditionally there has been a lack of reliable data on the occurrence of CSOs, with the main limitations being: i) the high number of CSO structures per municipality or catchment and ii) the high cost of the flow-monitoring equipment available on the market to measure CSO events. These two factors and the technical constraints of accessing and installing monitoring equipment in some CSO structures have delayed the implementation of extensive monitoring of CSOs. As a result, utilities lack information about the behaviour of the network and potential impacts on the local water bodies.</p><p>The new sensor technology developed by ICRA and IoTsens provides a simple yet robust method for CSO detection based on the deployment of a network of innovative low-cost temperature sensors. The technology reduces CAPEX and OPEX for CSO monitoring, compared to classical flow or water level measurements, and allows utilities to monitor their network extensively. The sensors are installed at the overflows crest and measure air temperature during dry-weather conditions and water temperature when the overflow crest is submerged in case of a CSO event. A CSO event and its duration can be detected by a shift in observed temperature, thanks to the temperature difference between the air and the water phase. Artificial intelligence algorithms further help to convert the continuous measurements into binary information on CSO occurrence. The sensors can quantify the CSO occurrence and duration and remotely provide real-time overflow information through LoRaWAN/2G communication protocols.</p><p>The solution is being deployed since October 2020 in the cities of Sofia, Bulgaria, and Berlin, Germany, with 10 offline sensors installed in each city to improve knowledge on CSO emissions. Further 36 (Sofia) and 9 (Berlin) online sensors will follow this winter. Besides its main goal of improving knowledge on CSO emissions, data in Sofia will also be used to identify suspected dry-weather overflows due to blockages. In Berlin, data will be used to improve the accuracy of an existing hydrodynamic sewer model for resilience analysis, flood forecasting and efficient investment in stormwater management measures. First results show a good detection accuracy of CSO events with the offline version of the technology. As measurements are ongoing and further sensors will be added, an enhanced set of results will be presented at the conference.</p><p>Visit us: https://www.digital-water.city/ </p><p>Follow us: Twitter (@digitalwater_eu); LinkedIn (digital-water.city)</p>

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

2021 ◽  
Vol 11 (3) ◽  
pp. 72-91
Author(s):  
Priyanka H. ◽  
Mary Cherian
Keyword(s):  
Data Center ◽  
Execution Time ◽  
Data Centers ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Large Data ◽  
Migration Time ◽  
Resource Usage ◽  
Total Execution Time ◽  
Effective Utilization

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

An Insight Into Openstack

2018 ◽  
pp. 243-259
Author(s):  
Srinivasa K. G. ◽  
Vikram Santhosh
Keyword(s):  
Operating System ◽  
Data Center ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Web Interface ◽  
Insight Into

OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a data center. All of the above components are managed through a dashboard which gives administrators control while empowering their users to provision resources through a web interface. OpenStack lets users deploy virtual machines and other instances which handle different tasks for managing a cloud environment on the fly. It makes horizontal scaling easy, which means that tasks which benefit from running concurrently can easily serve more or less users on the fly by just spinning up more instances.

Virtual Machine Placement in IaaS Cloud

2017 ◽  
pp. 130-158
Author(s):  
Prateek Khandelwal ◽  
Gaurav Somani
Keyword(s):  
Customer Satisfaction ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Attractive Alternative ◽  
Operational Cost ◽  
Trade Offs ◽  
Crucial Component

A crucial component of providing services over virtual machines to users is how the provider places those virtual machines on physical servers. While one strategy can offer an increased performance for the virtual machine, and hence customer satisfaction, another can offer increased savings for the cloud operator. Both have their trade-offs. Also, with increasing costs of electricity, and given the fact that the major component of the operational cost of a data center is that of powering it, green strategies also offer an attractive alternative. In this chapter, the authors will look into what kind of different placement strategies have been developed, and the kind of advantages they purport to offer.

A Two-Phase Load Balancing Algorithm for Cloud Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 38-55
Author(s):  
Archana Singh ◽  
Rakesh Kumar
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Virtual Machines ◽  
Second Phase ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Efficient Utilization ◽  
Two Phase ◽  
Excessive Load ◽  
Load Balancing Algorithm

Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.

LSRU: A Novel Deep Learning based Hybrid Method to Predict the Workload of Virtual Machines in Cloud Data Center

2020 ◽  
Author(s):  
Md. Nahid Hasan Shuvo ◽  
Md. Nahid Hasan Shuvo ◽  
Mirza Mohd Shahriar Maswood ◽  
Mirza Mohd Shahriar Maswood ◽  
Abdullah G. Alharbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hybrid Method ◽  
Data Center ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data

scholarly journals Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center

2019 ◽  
Vol 9 (16) ◽  
pp. 3223
Author(s):  
Jargalsaikhan Narantuya ◽  
Taejin Ha ◽  
Jaewon Bae ◽  
Hyuk Lim
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Principal Component ◽  
Service Performance ◽  
Data Traffic ◽  
Vm Placement ◽  
Matching Method ◽  
Placement Method ◽  
Underlying Network

In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.

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