Abstract Although numerous methods for handling the technical aspects of developing domain-specific modeling languages (DSMLs) have been formalized, user needs and usability aspects are often addressed late in the development process and in an ad hoc manner. To this concern, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user’s perspective. Because CJML targets a wide and heterogeneous group of users, its usability can be challenging to plan and assess. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group, whose feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we distill what we learned into general lessons and methodological guidelines.

Today’s networks and services undoubtedly require a high level of protection from cyber threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have shown to improve the accuracy and confidence in threat detection compared to previous approaches, making it suitable for detecting today’s sophisticated attacks such as Distributed Denial of Service (DDoS). However, in real-world deployments, input data streams take large bandwidth and processing capacity, especially for Deep Learning (DL) solutions that require extensive input data. On the other hand, deployment environments usually have limited bandwidth and computing resources, such as in the Internet of Things (IoT). Thus, a lightweight detection solution that satisfies such constraints is needed. In this paper, we utilize a feature reduction approach for our DL-based DDoS detector based on the Analysis of Variance (ANOVA), which is used to identify important data features and reduce the data inputs needed for detection. Our result shows that we can reduce the data input needed by up to 84.21% while only reducing 0.1% detection accuracy. We also provide a detailed analysis of the characteristics of DDoS attacks using ANOVA and compared our work with recent DL-based DDoS detection systems to demonstrate that our results are comparable to existing approaches.

Distributed Denial of Service (DDoS) attacks disrupt global network services by mainly overwhelming the victim host with requests originating from multiple traffic sources. DDoS attacks are currently on the rise due to the ease of execution and rental of distributed architectures such as the Internet of Things (IoT) and cloud infrastructures, which could potentially result in substantial revenue losses. Therefore, the detection and prevention of DDoS attacks are currently topics of high interest. In this study, we use traffic flow information to determine if a specific flow is associated with a DDoS attack. We used traditional Machine Learning (ML) methods in developing our DDoS detector and applied an exhaustive hyperparameter search to optimize their detection capability. Using lightweight approaches is suitable for resource-constrained environments such as IoT to reduce computing overhead. Our evaluation shows that most algorithms provide satisfactory results, with Random Forests achieving as high as 99% of detection accuracy, which is similar to the performance of current deep learning solutions for DDoS detection.

The Network Functions Virtualization (NFV) paradigm is undoubtedly a key technological advancement in the Information and Communication Technology (ICT) community, especially for the upcoming 5G network design. While most of its promise is quite straightforward, the implied reduction of the power consumption/carbon footprint is still debatable, and not in line with the energy efficiency perspective forecasted by the ETSI NFV working group (WG). In this paper, we provide an estimate of the possible future requirements of this upcoming technology when deployed according to the virtual Evolved Packet Core (vEPC) use case specified by the ETSI NFV WG. Our estimation is based on real performance levels, certified by independent third-party laboratories, and datasheet values provided by existing commercial products for both the legacy and NFV network architectures, under different deployment scenarios. Obtained results show that a massive deployment of the current NFV technologies in the EPC may lead to a minimum increase of 106 % in the carbon footprint/energy consumption with respect to the Business As Usual (BAU) network solutions. Moreover, these values tend to increase at a very high pace when the most suitable software/hardware combination is not applied, or when packet processing latency is taken into account.

Free-to-play models, streaming of games and eSports are reasons for online gaming to grow in popularity recently. On the forefront are multiplayer online battle arenas, which gain high popularity by introducing a competitive format that is easy to access and requires cooperation and team play. These games highly rely on fast reaction of the players, which makes latency the key performance indicator of such applications. To obtain low latency, this paper proposes moving game servers close to players towards the edge of the network. The performance of such mechanism highly depends on the geographic distribution of players. By analyzing match histories and statistics, we develop models for the arrival process and location of game requests. This allows us to evaluate the performance of edge server resource migration policies in an event based simulation. Our results show that a high number of edge servers is preferable compared to few larger edge servers to reduce the latency of players. This supports approaches that allow deploying virtual server instances in the back-haul.