Serverless computing offers an effective way to improve resource utilization and can significantly leverage edge-cloud environments. In this context, serverless request response times are one of the main focuses for optimization. However, the end-to-end routing path from a request to a function, which considerably contributes to the overall delay, is often overlooked. This oversight raises concerns about the actual performance of serverless in edge-cloud deployments, as serverless architectures are initially designed for cloud computing where node-to-node delay is negligible. To address this concern, our paper examines the current serverless networking design in a realistic edge-cloud setup. Our findings reveal significant performance degradation when serverless is applied in such environments due to key mismatches in the networking architecture. To mitigate these issues, we propose a novel latency-aware scheme and a load balancing mechanism for edge-cloud environments, called FaaSt. This proposal is implemented using open sources and standards, resulting in a significant reduction in response times.
The usual Machine Learning (ML) uses raw clients in a centralized mechanism and generalized training, which demands a lot of time and processing to send the network this sensitive data and to process only on Edge. Federated Learning (FL) improves the ML by using local training in each client device, selecting some specific clients to train, and sending only their local parameters for an edge. In this way, a client selection mechanism is essential to select clients to ensure data diversity and high-quality data, which enables global model to generalize well across different data distributions. In addition, while high-quality data contributes to the accuracy and reliability of the learned model. However, it is important to asses the device importance based on multiple criteria, aiding in identifying and prioritizing the most valuable and promising clients for participation in the training process. In this paper, we introduce a Robust multi-criteria Client Selection Mechanism called RICAm, which address the challenges of non-IID data FL environments. RiCAm uses client selection to search for the best-fit clients to train, calculated using the multicriteria method MACBETH. In our evaluation, RiCAm reached only 16 rounds, 80% of which is around 10% more than the standard random selection approach random selection approach.
Cloud-native applications, characterized by scalability, resilience, and flexibility, adopt microservices architectures to decompose applications into smaller, independently manageable services. Although microservices offer significant benefits, this architectural approach introduces challenges in service-to-service communication, commonly relying on advanced orchestration and communication frameworks such as Kubernetes and Istio, respectively. However, the added complexity imposes substantial overhead by introducing longer packet processing paths. This paper discusses performance bottlenecks arising from service meshes and proposes eZtunnel to address some of the identified challenges. Leveraging extended Berkeley Packet Filter (eBPF) to transparently offload networking traffic, the elongated network path is bypassed, optimizing resource utilization and enhancing application performance. Experiments show that eZtunnel can reduce median latency over 20% and jitter to almost 10%.
After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity.
Modern cloud-native applications, built with containerization and microservices architectures, present significant hurdles for proactive failure prediction and classification. Traditional anomaly detection and threshold-based algorithms often fall short: they either lack proactive capabilities or fail to provide granular enough output for effective remediation and root cause analysis. This paper proposes a novel, multi-stage framework that tackles these limitations. The framework leverages a combination of time-series forecasting models, such as NHITS, to predict resource consumption patterns. Subsequently, machine learning models like KNN and Random Forest are utilized to classify failures across various dimensions, including service names, HTTP methods, and statuses. This multistage approach empowers fine-grained identification of potential failures and their root causes. Our evaluation demonstrates the framework's effectiveness in a simulated environment. The NHITS model outperformed even cutting-edge architectures in predicting resource utilization. Additionally, the chosen machine learning models exhibited promising accuracy in classifying failure types.
