This paper presents Dronuum, a drone-based application designed within the Computing Continuum to enable efficient, real-time wildfire detection and environmental monitoring. Addressing the inherent limitations of Unmanned Aerial Vehicles (UAVs), notably constrained computational resources, energy capacity, and intermittent connectivity, the proposed system leverages a distributed, microservice-oriented architecture spanning Edge (drone) and Cloud environments. By integrating principles of Osmotic Computing and leveraging Liquid Computing (LIQO)-enabled multi-cluster orchestration, Dronuum can migrate application components across heterogeneous resources in response to mission requirements. The system decomposes the wildfire detection pipeline into modular services, including image acquisition, preprocessing, inference, and alerting. A lightweight YOLOv8n-based classifier is employed for fire detection. Experimental evaluation, conducted on a testbed combining Raspberry Pi Edge nodes and Cloud Virtual Machines (VMs), demonstrates the effectiveness of the proposed approach. Results indicate that classification accuracy is independent of the deployment scenario. Energy measurements confirm that LIQO-based offloading reduces the per-image energy cost from 6.85 mWh to 4.58 mWh, enabling up to 49.6% more images per battery charge, while microservice migration incurs a service downtime below ≈ 170 m s .

This paper presentsdcGuard, a unified security approach for detecting and isolating misbehaving computing and forwarding nodes in multi-tenant virtualized cloud data centers.dcGuardemploys technological advancements in Virtual Machine Introspection (VMI), Software-Defined Networking (SDN), and secure probabilistic sketching to detect and isolate parts of the Virtual Machines (VMs) and network switches experiencing malicious behavior dynamically. The main contribution lies in designing a divide-and-conquer strategy that utilizes VMI and network programmability to apply focused distributed task and packet probing mechanisms on portions of the data center network rather than focusing the security functions on the entire physical network. The processing VMs and network switches are recursively partitioned into independent logical groups inspected individually to localize abnormal/malicious computing and switching nodes incrementally. This remarkably enhances the efficiency of the detection mechanisms, which opportunistically approaches a logarithmic time complexity in the number of protocol steps towards convergence (compared to a linear time complexity in traditional intrusion detection systems) when a relatively low number of hostile VMs and switches are present. Real experiments are evaluated, and a test-bed blueprint of the proposed design is emulated in a virtualized cloud environment using the Mininet emulator. The performance, convergence, and accuracy benchmarks corroborate the analytical advantage of the proposed security approach.

As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, devel-opers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99\% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

This paper presents Nodeguard, a security approach for detecting and isolating misbehaving Virtual Machines (VMs) in multi-tenant virtualized cloud data centers, based on the Virtual Machine Introspection (VMI) monitoring primitives. Nodeguard employs a divide-and-conquer strategy that checks logical groups of VMs to ensure the efficiency of the detection mechanisms which opportunistically approaches a complexity of$\mathcal{O}(\log_{2}(n))$when there is a relatively low number of hostile VMs. This greatly enhances the algorithmic time complexity of the pro-posed security system compared to the$\mathcal{O}(n)$complexity achieved by the traditional VMI inspection strategy that checks each VM separately. The approach has been evaluated in a virtualized cloud environment using the Mininet network emulator.

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, calledMultiScaler, to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions.MultiScaleris mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that theMultiScalerapproach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines,MultiScalerproduces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.

The rapid revolution of cloud computing model is accompanied by huge amounts of energy consumed by the cloud data centers. So, enhancing the energy efficiency of those data centers has become a major challenge. This paper tackles the problem of enhancing the energy consumption of cloud data centers by proposing a novel virtual machine placement strategy. The proposed strategy suits both static and dynamic placement process. It aims to better utilize the involved physical machines which host the virtual machines. As different types of jobs do not intensively use the compute and/or non-compute resources in the hosted physical machine, virtual machines allocated to the jobs of different types are placed on the same physical machine where possible. The paper presents a mathematical formulation of the virtual machine placement process based on the Multiple Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. The performance evaluation of the proposed strategy shows that it can enhance the energy efficiency of the cloud data centers by trying to minimize the number of the involved physical machines which host the virtual machines, and by optimally utilizing the involved physical machines.