Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks

Many modern navigation systems and map-based services do not only provide the fastest route from a source location s to a target location t but also provide a few alternative routes to the users as more options to choose from. Consequently, computing alternative paths has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps. We also present a web-based demo system that can be accessed using any internet-enabled device and allows users to see the alternative routes generated by the four approaches for any pair of selected source and target. We report the average ratings received by the four approaches and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers to interpret these results with caution because certain factors may have affected the participants' ratings.Comment: Extended the user study to also include the road networks of Dhaka and Copenhagen (the previous version only had Melbourne road network

SDN-based detection and mitigation of DDoS attacks on smart homes

The adoption of the Internet of Things (IoT) has proliferated across various domains, where everyday objects like refrigerators and washing machines are now equipped with sensors and connected to the internet. Undeniably, the security of such devices, which were not primarily designed for internet connectivity, is of utmost importance but has been largely neglected. In this paper, we propose a framework for real-time DDoS attack detection and mitigation in SDN-enabled smart home networks. We capture network traffic during regular operations and during DDoS attacks. This captured traffic is used to train several machine learning (ML) models, including Support Vector Machine (SVM), Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) algorithms. These trained models are executed as SDN controller applications and subsequently employed for real-time attack detection. While we utilize ML techniques to protect IoT devices, we propose the use of SNORT, a signature-based detection technique, to secure the SDN controller itself. Real-world experiments demonstrate that without SNORT, the SDN controller goes offline shortly after an attack, resulting in a 100% packet loss. Furthermore, we show that ML algorithms can efficiently classify traffic into benign and attack traffic, with the Decision Tree algorithm outperforming others with an accuracy of 99%

Serverless Vehicular Edge Computing for the Internet of Vehicles

Rapid growth in the popularity of smart vehicles and increasing demand for vehicle autonomy brings new opportunities for vehicular edge computing (VEC). VEC aims at offloading the time-sensitive computational load of connected vehicles to edge devices, e.g., roadside units. However, VEC offloading raises complex resource management challenges and, thus, remains largely inaccessible to automotive companies. Recently, serverless computing emerged as a convenient approach to the execution of functions without the hassle of infrastructure management. In this work, we propose the idea of serverless VEC as the execution paradigm for Internet of Vehicles applications. Further, we analyze its benefits and drawbacks as well as identify technology gaps. We also propose emulation as a design, evaluation, and experimentation methodology for serverless VEC solutions. Using our emulation toolkit, we validate the feasibility of serverless VEC for real-world traffic scenarios.We would like to thank Asama Qureshi for his contribution to the traffic visualizer application. We would also like to acknowledge support through the Australian Research Council's funded projects DP230100081 and FT180100140. This work is also partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation, the European Union-NextGenerationEU through the UNICO 5G IþD SORUS project and by the NWO OffSense, EU Horizon Graph-Massivizer and CLOUDSTARS projects

In Datacenter Performance, the only Constant Is Change

All computing infrastructure suffers from performance variability, be it bare-metal or virtualized. This phenomenon originates from many sources: some transient, such as noisy neighbors, and others more permanent but sudden, such as changes or wear in hardware, changes in the underlying hypervisor stack, or even undocumented interactions between the policies of the computing resource provider and the active workloads. Thus, performance measurements obtained on clouds, HPC facilities, and, more generally, datacenter environments are almost guaranteed to exhibit performance regimes that evolve over time, which leads to undesirable nonstationarities in application performance. In this paper, we present our analysis of performance of the bare-metal hardware available on the CloudLab testbed where we focus on quantifying the evolving performance regimes using changepoint detection. We describe our findings, backed by a dataset with nearly 6.9M benchmark results collected from over 1600 machines over a period of 2 years and 9 months. These findings yield a comprehensive characterization of real-world performance variability patterns in one computing facility, a methodology for studying such patterns on other infrastructures, and contribute to a better understanding of performance variability in general

AI-based fog and edge computing:a systematic review, taxonomy and future directions

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing