Cloud management systems performing capacity autoscaling, application orchestration, server consolidation, and service differentiation increasingly rely on machine learning (ML) models for predictive decision-making. However, changes in user behavior, software updates, and hardware upgrades cause monitoring data to deviate from the training distribution, leading to model performance degradation. This phenomenon—known as concept drift poses a significant challenge to maintaining prediction accuracy in dynamic cloud environments. In this work, we propose a hybrid concept drift detection approach that combines statistical data monitoring with model performance metrics to inform efficient model retraining. The proposed method minimizes false positives, reduces adaptation delays, and avoids unnecessary retraining during stable periods. We conduct extensive experiments on both synthetic and real-world cloud workload datasets collected from multiple data centers, evaluating the hybrid approach against five established drift detection algorithms. To ensure statistical rigor, all experiments are repeated ten times, and the results are reported with 95% confidence intervals and significance tests. The results show that our proposed approach and implemented methods improve drift detection efficiency by eliminating unnecessary retraining and lead to more than 60% improvements over the baseline prediction accuracy. We Edited the abstract and removed the inconsistencies

Recent advances in multi-core chip technology have enabled the dynamic tuning of shared memory resources, such as last-level cache and memory bus bandwidth. However, despite proven performance benefits, the complexity of effectively utilizing these hardware-level QoS enforcement features has limited their adoption in real-world cloud computing environments. In this paper, we introduce ESTHER, a novel approach to autonomously fine-tune QoS enforcement features in cloud environments using extremum seeking control, focusing on applications needs and operator ease-of-use. We demonstrate that ESTHER effectively maintains latency-critical workload SLOs and rapidly resolves any infringements by prioritizing shared memory resources. Such fast node-level resolution of SLO violations ensures that costly cluster-level scaling events may be avoided. Furthermore, ESTHER improves best-effort job throughput without impacting latency-critical workloads, achieving performance gains without utilizing workload profiling or prior knowledge of system dynamics.

Accurate future workload prediction is an essential step for proactive resource allocation and efficient provisioning in cloud computing environments. Deep learning strategies have proven successful for this task, but they face challenges due to the high dimensionality of monitoring data, extensive preprocessing requirements, and computational overhead. In this paper, we propose a hybrid framework that integrates autoencoders for workload compression with Long Short-Term Memory (LSTM) networks for time-series forecasting. Unlike prior studies, our approach systematically analyzes the trade-off between compression ratio and predictive accuracy, demonstrating how dimensionality reduction can improve both scalability and robustness. Thereby reducing the computational burden associated with processing massive-scale monitoring data. Experiments conducted on both synthetic and real-world datasets demonstrate that the proposed method achieves up to 60% data compression with minimal reconstruction loss, while also improving prediction accuracy compared to baseline LSTM models. We evaluate the overall performance of the framework using various metrics, including data reduction ratio, prediction accuracy, and the effects of different compression stages on predictive performance. Additionally, we quantify the computational savings in terms of CPU usage, memory footprint, and training/inference times, confirming the framework’s feasibility for real-world deployment. These results underscore the potential of integrating compression and prediction to achieve scalable, accurate, and resource-efficient management of cloud workloads.

The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.

Optimal placement of service chains (composed of multiple connected service components) on heterogeneous cloud/fog infrastructure is a challenging problem. Even when deploying a small set of service components, the search-space of all possible solutions is quite large. Furthermore, current approaches sacrifice either precision or scalability when presented with heterogeneous platform configurations. In this scenario, the goal is to support orchestration that optimally selects solutions that meet performance constraints, leverages differentiating platform features and delivers these solutions within reasonable run-time. This paper presents the novel Hybrid Fitness-Utility Algorithm that addresses these issues by utilizing concepts derived from evolutionary algorithms to dimensionally reduce complexity, and incorporates the utility of placing each individual service component on an infrastructure resource for optimized selection between potential solutions. Our results show that the algorithm succeeds in determining optimal service chain placement onto distributed infrastructure for a simulated cloud/fog scenario and for a live multi point-of-presence OpenStack-based testbed with over 90 percent confidence.

SLAs are an integral part of all modern service provisioning operations. They have been a topic of discussion, research and development for many years but still the norm is the use of rigid, complex and not easy to automate Service Level Agreements. In this paper we are presenting a service framework that is leveraging the OCCI specification in order to facilitate standardized SLA provisioning and violation detection. This SLA as a Service (SLAaaS) offering is provided as an open source framework to any Service Provider that wants to efficiently enhance his infrastructure with SLA support.

In the last years we have experienced a growing industrial interest in Mobile Cloud Networking (MCN) as the opportunity to exploit the cloud computing paradigm through Network Function Virtualization (NFV), primarily with the goal to reduce CAPEX/OPEX for future mobile networks deployment and operation. The gain from the point of view of infrastructure costs reduction is almost clear and recognized, while many technical challenges are still to be solved, especially with industry-mature solutions, due to the complexity of managing such type of infrastructures. In particular, the dynamicity and flexibility introduced by the virtualization of network functions add novel requirements on the service management and orchestration layers. In this perspective, this paper originally presents the architecture and primary implementation guidelines of the Mobile Cloud Networking framework developed within a large EU FP7 project. More specifically, it focuses on the innovative technical elements of our solution for service management and orchestration, namely i) orchestration strategies based on resource unit affinity and ii) compliance with emerging Open Cloud Computing Interface (OCCI) standards. To practically demonstrate the suitability of the proposed approach, a specific real use case has been implemented, i.e., the cloudification of the 3GPP IP Multimedia Subsystem (IMS), by reporting and analyzing the related performance results.

Service performance degradation and downtimes are a common on the Internet today. Many on-line services (e.g. Amazon.com, Spotify, and Netflix, etc.) report huge loss in revenue and traffic per episode. This is perhaps due to the correlation between performance and end-users's satisfaction.

Cloud computing is a promising solution for the flexible, on-demand delivery of communication services and infrastructures. The European Union funded project Mobile Cloud Networking is developing a cloud-based mobile communication service platform where service providers deliver virtualized wireless infrastructures integrated with high-level applications running on top of them. Mobile networks are redesigned, placed upon and operated on cloud computing platforms. These then are available with the on-demand, elastic and pay-as-you-go characteristics derived from the cloud principles. In this platform, a variety of services including heterogeneous radio networking, federated computing resources, virtualized network functions, support services and high-level applications are automatically and seamlessly orchestrated in composite end-to-end services delivered to enterprise end-users. The management of Service Level Agreements (SLAs) for these cloud-based composite services requires a dedicated framework to combine and jointly manage the multiple SLAs associated to the different service components. This paper presents an architecture for the enforcement and validation of SLAs for mobile cloud services, discussing its positioning in the overall mobile cloud networking platform and its role across the different phases of the service lifecycle.

Cloud Computing is becoming more and more a commodity service to all kinds of businesses. This leads to a stronger need for dependable service guarantees on the resources or applications offered to the customer by the provider via Service Level Agreements. Most offerings on the market, however, rely on non-electronic modes rather than machine-manageable means. In this paper, we propose a protocol that closes this gap for REST-based services and leverages the benefits of HTTP. We build upon the widely established WS-Agreement document format and show how our protocol integrates with the currently popular Open Cloud Computing Interface.