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Mobile Edge Clouds (MECs) are emerging as a key complement to centralized cloud infrastructures by bringing computing and storage resources closer to the network edge, thereby reducing network bandwidth, latency, and jitter. A critical challenge in leveraging MECs effectively is the application placement problem, which seeks to minimize operational costs while ensuring end-user Quality of Service (QoS). This problem is further complicated by user mobility, as applications must migrate to maintain optimal QoS, yet frequent migrations can lead to unnecessary bandwidth consumption due to state transfer. In this paper, we tackle the application placement problem for stateful applications in MEC environments. We model the dynamic workloads, applications, and infrastructure typical of MECs and define the associated costs: resource utilization, migration, and QoS degradation. Based on this model, we propose two online placement algorithms – Gale-Shapley-based and Follow-me – designed to minimize the total cost of operating applications. These algorithms are compared against an offline benchmark that has complete future knowledge. Experimental results demonstrate that both proposed algorithms efficiently place applications in MECs, achieving operating costs within 8% of the global optimum approximated by the offline algorithm. Furthermore, the Gale-Shapley-based algorithm outperforms the Follow-me algorithm, reducing operating costs by up to 17% and improving load balancing across MECs to mitigate resource scarcity.

Emerging deployments of Generative AI increasingly execute inference across decentralized and heterogeneous edge devices rather than on a single trusted server. In such environments, a single device failure or misbehavior can disrupt the entire inference process, making traditional best-effort peer-to-peer routing insufficient. Coordinating distributed generative inference therefore requires mechanisms that explicitly account for reliability, performance variability, and trust among participating peers.

In this paper, we present G-TRAC, a trust-aware coordination framework that integrates algorithmic path selection with system-level protocol design to ensure robust distributed inference.First, we formulate the routing problem as a Risk-Bounded Shortest Path computation and introduce a polynomial-time solution that combines trust-floor pruning with Dijkstra's search, achieving sub-millisecond median routing latency at practical edge scales, and remaining below 10 ms at larger scales.Second, to operationally support the routing logic in dynamic environments, the framework employs a Hybrid Trust Architecture that maintains global reputation state at stable anchors while disseminating lightweight updates to edge peers via background synchronization.

Experimental evaluation on a heterogeneous testbed of commodity devices demonstrates that G-TRAC significantly improves inference completion rates, effectively isolates unreliable peers, and sustains robust execution even under node failures and network partitions.

Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training, whereas FL leverages numerous user devices to train a shared global model without the need to share private data. Current robotic manipulation tasks are constrained by the individual capabilities and speed of robots due to limited low-latency computing resources. Consequently, the concept of cloud robotics has emerged, allowing robotic applications to harness the flexibility and reliability of computing resources, effectively alleviating their computational demands across the cloud-edge continuum. Undoubtedly, within this distributed computing context, as exemplified in cloud robotic manipulation scenarios, FL offers manifold advantages while also presenting several challenges and opportunities. In this paper, we present fundamental concepts of FL and their connection to cloud robotic manipulation. Additionally, we envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL, where researchers adapt to design and verify FL models in either centralized or decentralized settings.

Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional anomaly detection methods. These conventional approaches, designed for stateful and long-running services, struggle in serverless settings where executions are short-lived, functions are isolated, and observability is limited. 

In this first comprehensive vision paper on anomaly detection for serverless systems, we systematically explore the unique challenges posed by this paradigm, including the absence of persistent state, inconsistent monitoring granularity, and the difficulty of correlating behaviors across distributed functions. We further examine a range of threats that manifest as anomalies, from classical Denial-of-Service (DoS) attacks to serverless-specific threats such as Denial-of-Wallet (DoW) and cold start amplification. Building on these observations, we articulate a research agenda for next-generation detection frameworks that address the need for context-aware, multi-source data fusion, real-time, lightweight, privacy-preserving, and edge-cloud adaptive capabilities.

Through the identification of key research directions and design principles, we aim to lay the foundation for the next generation of anomaly detection in cloud-native, serverless ecosystems.

Serverless computing has transformed cloud application deployment by introducing a fine-grained, event-driven execution model that abstracts away infrastructure management. Its on-demand nature makes it especially appealing for latency-sensitive and bursty workloads. However, the cold start problem, i.e., where the platform incurs significant delay when provisioning new containers, remains the Achilles' heel of such platforms. 

This paper presents a predictive serverless scheduling framework based on Model Predictive Control to proactively mitigate cold starts, thereby improving end-to-end response time. By forecasting future invocations, the controller jointly optimizes container prewarming and request dispatching, improving latency while minimizing resource overhead.

We implement our approach on Apache OpenWhisk, deployed on a Kubernetes-based testbed. Experimental results using real-world function traces and synthetic workloads demonstrate that our method significantly outperforms state-of-the-art baselines, achieving up to 85% lower tail latency and a 34% reduction in resource usage.

With the rise of ubiquitous networking and distributed computing, integrating robots with cloud-edge infrastructures offers significant potential. However, challenges remain in resource allocation and scheduling across distributed environments to meet robotics applications' performance demands. 

This paper introduces tinyKube, a middleware tailored for dynamic resource management across the cloud-edge platform for large-scale cloud robotics deployments. Leveraging Kubernetes for orchestration and Prometheus for monitoring, tinyKube enables unified monitoring, task dispatching, and resource provisioning across cloud-edge infrastructures.

We evaluate tinyKube using a robotic gripper application on the CloudGripper testbed in a real-world cloud-edge setup. Results demonstrate its ability to automate task dispatching and resource allocation, dynamically adapting to QoS requirements and workload variations.By simplifying resource management, tinyKube accelerates the development, testing, and deployment of large-scale cloud robotics applications, facilitating more efficient real-world implementation.

Accurate workload prediction is essential for proactive resource allocation in large-scale Content Delivery Networks (CDNs), where traffic patterns are highly dynamic and geographically distributed. This paper introduces a CDN-tailored prediction and autoscaling framework that integrates statistical and deep learning models within an adaptive feedback loop. The framework is evaluated using 18 months of real traffic traces from a production multi-tier CDN, capturing realistic workload seasonality, cache–tier interactions, and propagation delays. Unlike generic cloud-edge predictors, our design incorporates CDN-specific features and model-switching mechanisms to balance prediction accuracy with computational cost. Seasonal ARIMA (S-ARIMA), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Online Sequential Extreme Learning Machine (OS-ELM) are combined to support both short-horizon scaling and longer-term capacity planning. The predictions drive a queue-based resource-estimation model, enabling proactive cache–server scaling with low rejection rates. Experimental results demonstrate that the framework maintains high accuracy while reducing computational overhead through adaptive model selection. The proposed approach offers a practical, production-tested solution for predictive autoscaling in CDNs and can be extended to other latency-sensitive edge-cloud services with hierarchical architectures.

Machine Learning (ML) models play a crucial role in enabling intelligent decision-making across diverse cloud system management tasks. However, as cloud operational data evolves, shifts in data distributions can occur, leading to a gradual degradation of deployed ML models and, consequently, a reduction in the overall efficiency of cloud systems.

We introduce CloudResilienceML, a framework designed to maintain the resilience of ML models in dynamic cloud environments. CloudResilienceML includes: (1) a performance degradation detection mechanism, using dynamic programming change point detection to identify when a model needs retraining, and (2) a data valuation method to select a minimal, effective training set for retraining, reducing unnecessary overhead.

Evaluated with two ML models on real cloud operational data, CloudResilienceML significantly boosts model resilience and reduces retraining costs compared to incremental learning and data drift-based retraining. In high-drift scenarios (e.g., Wikipedia trace), it reduces overhead by 50% compared to concept drift retraining and by 91% compared to incremental retraining. In stable environments (e.g., Microsoft Azure trace), CloudResilienceML maintains high accuracy with retraining costs 96% lower than concept drift methods and 86% lower than incremental retraining.

Autonomous resource management is essential for large-scale cloud data centers, where Machine Learning~(ML) enables intelligent decision-making. However, shifts in data patterns within operational streams pose significant challenges to sustaining model accuracy and system efficiency.

This paper proposes an auto-adaptive ML approach to mitigate the impact of data drift in cloud systems. A knowledge base of distinct time-series batches and corresponding ML models is constructed and clustered using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. When model performance degrades, the system uses Dynamic Time Warping (DTW) to retrieve matching hyperparameters from the knowledge base and apply them to the deployed model, optimizing inference accuracy on new data streams.

Experiments with two real-world cloud data traces -- representing both stable and highly fluctuating environments - demonstrate that the proposed approach maintains high model accuracy (over 89%) while minimizing retraining costs. Specifically, for the Wikipedia trace with frequent data drift, retraining overhead is reduced by 22.9% compared to drift detection-based retraining and by 97\% compared to incremental retraining. In stable environments, like the Google cluster trace, retraining costs decrease by 96.3% and 88.9%, respectively.

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance. Our investigation considers the offload shaping strategies of mapping processes to different computing tiers, gating, and decluttering. We find that all three strategies offer a significant bandwidth savings compared to transmitting full camera images to a cloudlet. Two out of the three devices we test are capable of running all offload shaping strategies within a reasonable latency bound.