Auto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of workload is dynamically changed because of the variation in service demand over time. The predominant auto-scaling approaches use scaling rules (threshold-based reactive approach) or scaling policies (schedule-based proactive approach) to adapt resources and meet the performance requirements of each VNF. The reactive approaches can significantly degrade the VNF performance for improper reconfiguration or variation of auto-scaling rules. Conversely, the proactive approaches dynamically adjust the scaling policies according to the workload variation. These approaches rely on accurate workload predictive models (e.g., time-series models). This paper proposes a real-time proactive auto-scalar system based on a deep learning model that can efficiently predict the future values of CPU, Memory, and Bandwidth for VNFs for a Service Function Chain (SFC) to proactively auto-scale the resources allocated to each VNF in a Cloud platform. A hybrid model of MLP-LSTM is used to forecast the values of different features. Auto-correlation is used to identify the abnormal events of instances in the Cloud platform by measuring the repeated pattern for each identified impact feature. Moreover, the auto-scalar system enables to predict the abnormal values for some features during the online stage using the Auto-regression model to meet the QoS requirements of an SFC.

Intelligent transportation systems (ITS) are becoming more prominent in our society (for example, in smart cities), although a number of challenges remain to be (fully) addressed (e.g., high vehicle mobility). In this paper, we propose a scheme that combines both a cluster algorithm and a Multi-Objective Task Distribution algorithm based on Differential Evolution (MOTD-DE), designed to ensure stability and reliability in vehicular ad-hoc network (VANET) deployments. Specifically, we use Kubernetes container-base as the cluster algorithm to select various vehicles that fulfill the algorithm's conditions. Hence, this allows us to perform complex tasks on behalf of data owner vehicles. In our approach, the vehicles' information will be available on the master vehicle (data owner vehicle) when the vehicle joins the cluster, and a deep learning model is used to define the fit complexity of sub-tasks. The proposed MOTD-DE distributes sub-tasks between vehicle clusters to reduce the execution time and the resources (vehicles) used to perform a task. We also assume the sub-tasks to be independent. To evaluate our work, we propose scenarios with varying number of tasks, vehicles, CPU and memories values, and distances between cluster vehicles and data owner vehicle. A comparative summary of the evaluation findings between MOTD-DE and four other widely used approaches (i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant-Colony algorithm (ACO), and Artificial-Bee-Colony (ABC) algorithm) shows that MOTD-DE outperforms these competing approaches.

In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.

While vehicular ad hoc Networks (VANETs) are relatively well-studied, a number of challenges remain, particularly as autonomous vehicles become more commonplace. For example, devices on a vehicle such as, On-Board Units (OBUs) may have resource constraints which render them incapable of supporting computationally expensive cryptography operations required to achieve various security features. One potential solution is to offload computationally expensive tasks to other nodes in the VANET; however, the dynamic nature of the setup (such as the, mobility of the requesting vehicles and other nodes) compounds the challenge of resource management. In this context, we propose a scheme that uses Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to achieve data confidentiality in VANETs despite such challenges. Specifically, we build a cluster of vehicles to perform CP-ABE operations without relying on other nodes in the VANET. We use Kubernetes, an open-source container orchestration system, to build vehicle cluster(s) to handle distributed micro-tasks. In this scheme, we use a set of factors that impact the computation operations in cluster vehicle components (i.e., the OBU). Each factor, including the distance between the data owner vehicle and the target vehicle, the duration of each target vehicle in the cluster, and the resource of each vehicle in the cluster, has a weight based on its influence in computational operations. The Euclidean method is used to calculate the weight value for each factor. Based on the final total weight for each vehicle, our approach distributes the tasks between vehicles. We evaluate our results by comparing our approach with the mechanism of Kubernetes for task distribution, which only considers the resources in each vehicle. We also consider several scenarios with varying factors to evaluate their impact on the execution time of CP-ABE on OBUs in addition to using simulations to evaluate the performance of our approach in terms of transmission and propagation overheads for vehicles in the cluster.