The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we proposeDiktopos, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos' effectiveness in dynamic, resource-constrained edge scenarios.

Efficient workload scheduling in Kubernetes is crucial for optimizing energy consumption and resource utilization in large-scale and heterogeneous clusters. However, existing Kubernetes schedulers either ignore power-awareness or rely on simplified, static power models, which limit their effectiveness in managing energy efficiency under dynamic workloads. To address these shortcomings, we present a multi-objective scheduling framework for online Kubernetes pod placement that jointly considers power consumption, resource utilization, and load balancing. The framework follows a two-stage design: (i) a node power–profiling component trains a machine–learning model from real power measurements to predict per-node consumption under varying utilizations; and (ii) an online scheduler uses these predictions within a multi-objective optimization formulation. We implement scheduling optimization using two algorithms, TOPSIS and NSGA-II, adapting them to the Kubernetes context, and also propose a distributed variant of the NSGA-II algorithm that parallelizes fitness evaluation with controlled migration between workers. Experimental results show that the proposed framework outperforms baseline schedulers, achieving a 40% reduction in power consumption and improvements of 74% and 68% in CPU and memory utilization, respectively, while sustaining scalability under high workloads. To the best of our knowledge, this is the first work to integrate learned power models and distributed multi-objective optimization into Kubernetes for power-aware pod scheduling.

The Cloud Edge Continuum enables the deployment of latency-sensitive and data-intensive applications closer to end users, but it poses challenges for microservice placement due to resource heterogeneity and limited edge capacity, especially when storage requirements must be met through aggregated node resources. To address this, we propose a two-stage, topology-aware optimization framework that jointly handles microservice deployment and distributed storage volume allocation in edge networks. Our framework decomposes this joint placement problem into two subproblems, microservice placement followed by a distributed volume allocation subproblem, with the goal of optimizing computation, communication, energy, and storage costs. At its core is a lightweight, rank-based heuristic that ensures scalable, accurate placement across distributed edge nodes. Evaluations on real-world scenarios show our method achieves near-optimal placement (within 1.67% of the exact solution), reduces system costs by up to 30%, and accelerates convergence by 5× compared to state-of-the-art approaches, demonstrating its suitability for dynamic, resource-constrained edge environments.

This paper proposes a new scheduling framework to optimize the placement of cloud workloads submitted online by users within a Kubernetes-orchestrated environment. The proposed method aims to incorporate power awareness during the scheduling process, along with other criteria, such, load balancing, and bin packing. The framework equitably distributes workloads across cluster nodes while also selecting the most resource-efficient node to reduce the number of active nodes and prevent resource fragmentation. Existing strategies often focus on a single criterion, leading to suboptimal and unsatisfactory workload placements. The proposed framework utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision analysis algorithm, to account for power consumption and other criteria defined by cloud operators, such as load balancing and bin packing. The algorithm is implemented as a Kubernetes scheduling plugin to rank worker nodes based on these criteria and the submitted workloads. Simulation results demonstrate the effectiveness of the proposed strategy across various scenarios, reducing power consumption by 26.46% and comparable CPU and memory load balancing performance within a large Kubernetes cluster under heavy workloads.

In recent years, AI inference has seen widespread adoption across fields like finance and healthcare, driving significant demand for high-performing applications. This demand brings about a complex relationship between inference application types, such as real-time applications, and their specific service level objectives (SLOs), like tail-latency. Tail-Latency is a metric requiring a defined percentage of requests to meet a maximum response time, which is crucial for applications where delays can impact user experience or decision-making. This dependency creates a challenging research problem in scheduling inference workloads. The core question becomes: How can we deploy AI workloads in a way that minimizes SLO violations?Specifically, we worked on real-time applications that require tail-latency guarantees. To address this, we developed a tail-latency-aware scheduler designed for resource-constrained devices. Our scheduler employs advanced machine learning techniques to optimize task placement, aiming to minimize SLO violations and enhance performance for latency-sensitive applications. We have developed and integrated our custom scheduler into Kubernetes, which operates on a specially configured cluster designed to test its performance. This cluster features diverse computing capabilities, enabling a comprehensive evaluation of the scheduler’s effectiveness. The experimental results highlight that our proposed scheduler outperforms the native Kubernetes scheduler in terms of efficiency.

The emergence of the Beyond Visual Line of Sight (BVLOS) operations for Unmanned Aerial Vehicles (UAVs) unlocked a wide range of new applications across various domains, such as urban transportation, package delivery, and aerial surveillance. However, due to the possibility of losing control and collisions, BVLOS operations present several risks to people on the ground. Therefore, it is crucial to minimize safety risks by flying UAVs along paths that traverse less populated areas. Nevertheless, implementing such a solution requires access to real-time data on the population density distribution across UAV operational areas. Consequently, in this paper, we harness the network exposure capabilities of 5G mobile networks, proposing a framework that integrates the UAV Traffic Management (UTM) system with the 5G Core (5GC). The proposed framework can collect real-time information about the density of mobile users in Areas of Interest (AoI), leveraging this data to estimate ground risks and subsequently devise optimized flight paths. Moreover, we propose a Deep Reinforcement Learning (DRL) solution to compute optimized flight paths. The simulation results show the efficiency of our proposed solution to achieve the designed goals in terms of reducing the experienced ground risk and total flight distance.

Unmanned aerial vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt visual line of sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs’ commercial operations to cover the urban and populated area-controlled airspace beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs’ autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a multiaccess-edge computing (MEC) that can read the gathered data from different UAV’s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This article proposes probabilistic and deep-reinforcement-learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

Unmanned aerial vehicles (UAVs) is one of the promising technology in the future. A recent study claims that by 2026, the commercial UAVs, for both corporate and customer applications, will have an annual impact of 31 billion to 46 billion on the country's GDP. Shortly, many UAVs will be flying everywhere. For this reason, there is a need to suggest efficient mechanisms for preventing the collisions among the UAVs. Traditionally, the collisions are prevented using dedicated sensors, however, those would generate uncertainty in their reading due to their external conditions sensitivity. From another side, the use of those sensors could create an extra overhead on the UAVs in terms of cost and energy consumption. To deal with these challenges, in this paper, we have suggested a solution that leverages the chance-constrained optimization technique for avoiding the collision in an energy-efficient manner. Building on the expressions for the non-central Chi-square CDF and expected value, and through the convexification of the resulting expressions, the chance-constrained optimization program is transformed into a convex Mixed Binary Nonlinear one. The resulting program allows us to find the optimal safety distance that extends UAVs life-time and allows every UAV to move with a guaranteed probability of collision between any pair of UAVs.

Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy- and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDCUAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

Unmanned Aerial Vehicles (UAVs) are expected to be used everywhere to provision different services and applications, impacting different aspects of our daily lives. Basically, UAVs are characterized by their high mobility. Some may remain motionless for a specific time to perform pre-programmed missions. Whilst UAVs would be used for specific applications, they could additionally offer numerous IoT (Internet of Things) value-added services (VAS) when they are equipped with suitable IoT devices. Many IoT VAS applications require high amount of resources and/or diverse IoT devices that cannot be offered by a single UAV. In order to overcome this limitation, this paper aims to explore, i) the diversity of IoT devices on-board UAVs, and ii) the mobility of UAVs for offering UAVs-based IoT VAS. Two solutions are proposed for carrying out different IoT VAS. Both solutions are modeled using linear integer programming. While the first solution aims to reduce the energy consumption, the second one aims to shorten the response time. The simulation results demonstrate the efficiency of both solutions in achieving their design goals.