Quantifying Inter-City Network Latency in Europe: A Measurement based Study for Time-Critical Cloud Services

3rd Workshop on Engineering Techniques for Distributed Computing Continuum Systems (EDCCS), 22-25 June 2026, Seoul, South Korea

Authors: Thomas Schleicher, Kurt Horvath, Dragi Kimovski, Bernd Spiess, Oliver Hohlfeld

Abstract: Time-critical cloud and edge services depend on predictable and low-latency wide-area connectivity, yet inter-city network behavior often deviates from expectations based on geographic distance alone. This paper presents an evaluation framework and results on inter-city network latency across major European metropolitan areas, treating latency as a non-functional property relevant to benchmarking and service placement in cloud computing. We develop a scalable measurement framework based on a distributed probing infrastructure, analyze round-trip latency, and assess spatial efficiency and temporal stability. Initial results reveal unexpectedly high latency on long-distance paths from the Iberian Peninsula toward Turkey. Distance-normalized analysis further exposes pronounced inefficiencies on short-distance paths between Greece and Turkey, suggesting non-distance-related network effects beyond geographic proximity. Temporal analysis shows elevated latency variance and instability on paths involving Turkey, while most other inter-city connections closely follow distance-based expectations and remain stable over time. These findings highlight the importance of distance-normalized and stability-aware metrics for evaluating wide-area cloud connectivity. The presented methodology and results provide practical insight for benchmarking, placement, and operation of latency-sensitive cloud services across geographically distributed infrastructures.

Energy and Compression Efficiency in Large-Scale Video Streaming

IEEE International Conference on Image Processing (ICIP 2026)

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Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria),  and Christian Timmerer (AAU, Austria)

Abstract: The rise in large-scale video streaming has led to increased energy demands across the encoding, transmission, and decoding pipeline. While energy consumption in video streaming has been widely studied, encoding decisions are typically made without explicitly accounting for expected content demand. As a result, the impact of view count on energy consumption and compression efficiency remains largely unexplored. This limits the ability to make informed and efficient encoding decisions in real-world streaming scenarios. In this paper, we propose EcoEncode, an analytical framework to evaluate the impact of view count on codec-level encoding decisions and the resulting trade-offs between energy consumption and compression efficiency. We further show that these decisions depend on video content characteristics and encoding configurations. Based on our findings, we provide practical insights to guide the selection of codecs and presets. Experimental results show that view count is a key factor in codec-level decisions. For low-popularity videos, EcoEncode achieves up to 99% energy savings with only 1-4 VMAF points of quality loss. Across all scenarios, the selected configurations lie on or near the Pareto frontier, and EcoEncode improves quality by up to 14 VMAF points over the least energy-consuming configuration.

Title: Token-Wise Attention-Guided Semantic Quality Assessment for Compressed Visual Features

Authors: Shien Ke, Changsheng Gao, Hadi Amirpour, Zhihua Wang,  Xaoyan Sun

Event: QoMEX 2026, Cardiff, UK, June 29th – July 3rd, 2026

Abstract: In collaborative and distributed intelligent systems, compressed intermediate features are routinely transmitted and reused, making semantic quality assessment (SQA) crucial for reliable deployment. Recent compressed feature quality assessment (CFQA) benchmarks, however, show that conventional similarity measures often correlate poorly with downstream semantic utility and lack robustness across diverse feature codecs. In this paper, we propose a token-wise, attention-guided method for assessing the semantic quality of compressed features. First, motivated by the observation that many downstream heads normalize and process tokens largely independently, we assess quality at the token level. This token-wise formulation exploits the intrinsic correspondence between the original and reconstructed tokens while reducing cross-token interference. Second, since tokens contribute unequally to downstream task performance, we adopt an attention-guided aggregation scheme: we derive task-adaptive importance weights from DINOv2 self-attention and use them to pool token-wise quality predictions into a global semantic quality score. Third, to accommodate heterogeneous supervision across tasks, we cast CFQA as a regression problem and rescale classification-based rank targets to mitigate label imbalance. Experiments on the CFQA benchmark demonstrate that our method consistently improves PLCC and SROCC across three tasks and four codecs, yielding a practical, codec-agnostic quality interface for next-generation intelligent systems.