cloud network operators

Abstract: Policy controllers must determine which workloads match rules spanning several label dimensions. Hash sets, compressed bitmaps, and GPU set-processing primitives address the underlying set operations. However, intermediate results, repeated scope evaluations, dependent policy updates, and host transfers can dominate the cost of a heterogeneous batch. We study this problem as batched intersection-of-unions (IoU). Our design exploits independent scope--word tasks, reusable endpoint expressions, and policy dependencies. The challenges are preserving wildcard, empty-group, and exclusion semantics, recognizing equal scopes, and retaining unchanged recipients affected by source edits. BitPCE addresses them with register-resident GPU evaluation, canonical scope compilation, and dependency-complete candidate selection under a stated rendering contract. IP-only changes reuse cached memberships. We establish correctness and word-work bounds. On a four-vCPU A10G host, resident GPU execution is 3.46--36.60$\times$ faster than the best tested native CPU path, including CRoaring, and 1.14--2.22$\times$ faster than an adapted operand-parallel GPU reduction. Gains narrow at the host-output boundary: on an eight-vCPU host, including upload and compact identifier output, resolution takes 16.3 ms versus 19.3 ms for CPU at 100K objects and 500 scopes. Sparse labels and small batches can favor CPU execution. Scope reuse, candidate selection, and incremental transfers are measured as components; complete update-to-enforcement latency remains unevaluated.

Abstract: The network virtualization (NV) technology has enabled the sharing of multiple resources among virtual networks (VNs) in cloud data centers. One of the key challenges is to allocate resources in real-time for virtual network request (VNR), which is known as online virtual network embedding (VNE). However, the existing online VNE methods do not exploit the multi-dimensional complementary relationship among diverse VNRs, resulting in the fragmentation and waste of substrate resources. In this paper, we propose the pattern matching based online VNE approach by constructing appropriate matching rules among observed patterns to maximize resources utilization. We devise the clustering based VNRs quantization method and conduct rigorous study on the pattern combination filtering problem. Then, we utilize the column generation to solve it and construct the pattern matching rules. Based on the rules, we propose an online pattern matching VNE algorithm with linear worst-case complexity. Evaluation on a 106-server testbed using Alibaba production cluster trace dataset shows that our algorithm achieves close-to-offline performance and more accepted workloads that outperforms traditional designs by 25%-30%.

Abstract: This paper presents the design implementation and evaluation of NetMon a hybrid network monitoring system designed for Kubernetes-based 5G packet core deployments specifically evaluated on Ericssons Access and Mobility Management Function AMF clusters NetMon combines eBPF-based passive kernel-level traffic observation with active TCP probing and centralized correlation to detect and localize network degradation within seconds The evaluation results demonstrate that the system detects faults as subtle as 10ms of added latency or 5 packet loss correctly attributes them to the affected infrastructure component and maintains this capability under application loads up to 50 simulated UE load The total resource overhead of 34 millicores CPU and 45 MiB memory per pod suggests that the approach is promising for further validation without impacting the monitored workload The hybrid approach addresses a gap in existing monitoring tools standard health checks cannot detect partial degradation scrape-based systems introduce detection delays measured in tens of seconds and purely passive tools cannot verify idle network paths By combining these complementary techniques and centralizing the analysis the system provides the early detection and fault localization capabilities required for maintaining service quality in cloud-native 5G infrastructure.