Adaptive fog service placement for real-time topology changes in Kubernetes clusters

Recent trends have caused a shift from services deployed solely in monolithic data centers in the cloud to services deployed in the fog (e.g. roadside units for smart highways, support services for IoT devices). Simultaneously, the variety and number of IoT devices has grown rapidly, along with their reliance on cloud services. Additionally, many of these devices are now themselves capable of running containers, allowing them to execute some services previously deployed in the fog. The combination of IoT devices and fog computing has many advantages in terms of efficiency and user experience, but the scale, volatile topology and heterogeneous network conditions of the fog and the edge also present problems for service deployment scheduling. Cloud service scheduling often takes a wide array of parameters into account to calculate optimal solutions. However, the algorithms used are not generally capable of handling the scale and volatility of the fog. This paper presents a scheduling algorithm, named "Swirly", for large scale fog and edge networks, which is capable of adapting to changes in network conditions and connected devices. The algorithm details are presented and implemented as a service using the Kubernetes API. This implementation is validated and benchmarked, showing that a single threaded Swirly service is easily capable of managing service meshes for at least 300.000 devices in soft real-time

Older people’s experiences of informal care in rural Flanders, Belgium

Flanders (Belgium) is ageing at high speed. The number of people aged over 80 will increase from more than 400.000 today to nearly 800.000 in 2060, which is 10 % of the population. Although the entire region is ageing, older people are overrepresented in non-urban areas. A large majority of the elderly have to ''age in place', being the consequence of a very clear-cut policy to foster staying put. However, one can question if their quality of life can be guaranteed, knowing that basic facilities are often unavailable in sparsely populated areas. This is especially relevant when personal mobility decreases and (health) care is needed. Based on qualitative research methods (in-depth interviews, focus groups and observations made while following care providers), this paper discusses the everyday experiences and perceptions of older people with regard to informal care. We focus on elderly people living in two different rural regions, rudimentary defined as a work-poor area (Westhoek) and a work-rich area (Kempen). The results indicate that the availability of informal care is -among others- dependent on the spatial context and can therefore not be guaranteed everywhere

Removal of dimethylsulfide, n-hexane and toluene from waste air in a flat membrane bioreactor under continuous conditions

Dimethylsulfide (DMS), n-hexane and toluene removal from a waste air was carried out by using a flat composite membrane bioreactor under continuous feeding conditions. The composite membrane consisted of a dense polydimethylsiloxane top layer with an average thickness of 1.5 μm supported with a porous polyacrylonitrile layer of 50 μm. The membrane bioreactor (MBR) was operated during 9 months in which several operational conditions were applied. The inlet load of each compound ranged from 0 to 350 g m-3 h-1 and removal efficiencies of 80, 70 and 0 to 30 % were reached for DMS, toluene and hexane respectively. Two different empty bed residence time (EBRT) were applied on the MBR in order to check the influence of the residence time on the reactor performance. In this case, DMS and toluene removal increased with an increasing EBRT, while the removal of hexane remained constant. By increasing the flow rate of the recirculated liquid from 22 l min-1 to 45 l min-1, the total performance of the biofilter decreased. To increase the mass transfer of hexane in order to get a higher removal, an emulsion of water/silicone oil 80/20 V% was used as recirculated medium at the liquid side of the reactor. This caused a decrease in DMS removal while the removal of toluene remained constant. The variation on the hexane removal decreased significantly, so the reactor became more reliable for degrading hexane

In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats

This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017