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Related Articles from SNS
SHADOW: Seamless Handoff And Zero-Downtime Orchestrated Workload Migration for Stateful Microservices
arXiv:2603.25484v3 Announce Type: replace Abstract: Migrating stateful microservices in Kubernetes requires careful state management because in-memory state is lost when a container restarts. For StatefulSet-managed workloads, the problem is amplified by identity constraints that prohibit two pods with the same ordinal from running simultaneously, forcing a sequential stop-restore cycle with unavoidable downtime.
Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions
arXiv:2606.07046v1 Announce Type: new Abstract: Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions...
Real-World Deployment of a 5G-Connected Edge-Controlled Aerial Robot in Industrial Subterranean Mines
arXiv:2606.04818v1 Announce Type: new Abstract: This article presents the first real-world autonomous flight of a 5G-connected aerial robot controlled by an edge-offloaded controller, and aims to bridge the gap between controlled and factual setups. The robot operates within an active industrial subterranean mine, while the high-level controller is deployed in a nearby Kubernetes-based edge cluster. Communication between the robot and the edge is enabled via a 5G New Radio (NR) Standalone...
Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
Announce Type: new Abstract: Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from incident evidence rather than scenario-specific shortcuts. We present Graph Traversal Agent, a graph-guided RCA agent that combines LLM reasoning with specialized tools. The model reasons over a typed evidence graph, while deterministic graph and tool operations collect evidence, bound the search, and check proposed verdicts.
Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access
arXiv:2605.27575v2 Announce Type: replace Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building individual agents to operating them at scale with proper isolation, governance, and security. In this paper we present Agyn, an open-source platform designed around three key principles tailored...
A Low-Latency Semantic State Estimator using Latent Predictive Learning for Dynamic Network Monitoring and Orchestration
Announce Type: new Abstract: Closed-loop network monitoring and orchestration increasingly require semantic interpretations of live telemetry beyond raw counter collection. However, dynamic cloud-edge environments change both the active node set and the monitoring query at runtime, while control loops demand bounded millisecond-scale responses. We introduce a latent predictive state estimator (LPSE) for dynamic network monitoring and orchestration, built on latent predictive learning over...
HackerOne takes an axe to its bug bounty rewards
HackerOne has significantly reduced the reward rates for its Internet Bug Bounty (IBB) program across all severity levels. The program is currently paused while HackerOne evaluates adjustments to maximise value for researchers and sponsors. These changes follow instances where researchers reported receiving substantially lower payouts for vulnerabilities they submitted previously.
BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams
arXiv:2606.03364v1 Announce Type: new Abstract: Shuffling or repartitioning data streams is an essential operation of state-of-the-art stream processing frameworks to support stateful workloads in a large-scale, distributed setting. In today's cloud deployments, however, shuffling can become a major cost driver due to substantial network traffic across multiple availability zones (AZs) as well as an operational burden when operating a high-throughput, strongly consistent messaging backbone...
Predicting Lakehouse Performance in Clouds: An Empirical Exploration of Query Runtime Variance
Announce Type: new Abstract: Data analytics increasingly runs on distributed lakehouse systems, where platform operators must optimise monetary, resource, and environmental costs. Query Performance Prediction (QPP) helps to balance these costs and supports workload management techniques, such as adaptive resource scaling and low-carbon scheduling.
Digital sovereignty, the musical: One engineer’s bizarre crusade against hyperscalers
A French SRE has launched "Operation Dindon," a satirical campaign targeting major cloud providers like Amazon, Google, and Microsoft over issues of vendor lock-in and high egress fees. The campaign involves a series of AI-generated protest songs and poetry, culminating in a formal ultimatum demanding reforms to cloud commitment and pricing structures. The engineer threatens an ongoing barrage of creative content if the hyperscalers do not agree to meaningful changes.