360° video streaming is rapidly advancing, with emerging applications in immersive communications envisioned for 6G Networks. However, dynamic user VR navigation (e.g., with head-mounted displays) under variable network conditions remains challenging, leading to severe stalls and quality degradation. In this work, we experimentally evaluate 360° video streaming over state-of-the-art protocols such as HTTP over QUIC, i.e., HTTP/3, focusing on the impact of stream scheduling. We implement and evaluate scheduling policies in two classes: (1) explicit priority, coordinated with QUIC, which dynamically prioritises 360° video tile requests based on temporal and spatial criteria; and (2) implicit priority, confined to the application layer and hidden from QUIC, relying on request order strategies. In realistic experiments with network emulation and 360° user navigation, we observe that explicit priority policies applying temporal criteria to urgent tiles reduce stall time by up to 81 % while maintaining competitive video quality compared to application-only stream scheduling.

360° video streaming represents a shift in the way media is consumed: Users can immerse themselves, interact and engage with the viewing environment. This paper proposes the design and implementation of an end-to-end 360° video streaming stack on top of emerging Internet standards such as HTTP/3 and MPQUIC. Preliminary experiments with real network traces show that, compared to single-path streaming, multi-path with stream-aware scheduler and non-blocking buffers achieves up to 25% reduction in tile download times, lower rebuffering ratio (down to 0.53), and decreasing tile misses, while providing a higher Structured Similarity Index Measure (SSIM) of around 17.2 dB. 

The promises of multipath transport are to aggregate bandwidth, improve resource utilisation and enhance reliability. In this article, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule- and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution and reduced buffering, resulting in an enhanced quality of experience for users.

Software-Defined Networking (SDN) has increasingly shifted toward hardware solutions that accelerate packet processing within data planes. However, optimizing the interaction between the data plane and the control plane, commonly referred to as the slow path, remains a significant challenge since the control plane installs rules in the data plane reactively as new flows arrive, which requires time-consuming transitions to user space. In this paper, we demonstrate how to prevent a bottleneck in the slow path by investigating the impact of predicting flows and preemptively installing their rules in the data plane. Using a workload containing different levels of "coflows", our results demonstrate that this predictive approach varies with the flow rate of traffic traces. Notably, a system that could predict 25% of the traffic flows would decrease the average latency by up to approximately 24% and reduce CPU utilization by approximately 12%. 

Performance Isolation in Multi-Tenant Cloud Data Centers (MTCDCs) consists of a set of mechanisms to make sure tenants’ use of resources does not impact other tenants. In this context, traffic shapers and rate limiters are fundamental to addressing the challenges of performance isolation in MTCDCs, which include predictable performance as minimum bandwidth guarantees, tenants-level fairness, and optimal resource utilization. However, the classical linear programming process to find the optimal rates to apply does not scale in terms of computing time, especially with the huge number of nodes, dominated mainly by virtual machines in an MTCDC environment. Motivated by this observation, this paper introduces gPerfIsol, a novel Graph Neural Network (GNN)-based approach designed to find near-optimal rates allocation in near-real-time to ensure performance isolation in MTCDC. gPerfIsol’s key innovation leverages Heterogeneous GNNs to capture MTCDC-specific topological information and demand traffic matrix. Evaluations based on datasets generated through simulation demonstrate the effectiveness of gPerfIsol’s binary classification model with a precision score of 0.964 and a recall score of 0.973. Ultimately, gPerfIso1 offers a promising solution for nearoptimal rate limit allocation for traffic shapers in multi-tenant environments, enhancing performance isolation. 

Accurate predictive models for cloud workloads can be helpful in improving task scheduling, capacity planning and preemptive resource conflict resolution, especially in the setting of co-located jobs. Alibaba, one of the leading cloud providers co-locates transient batch tasks and high priority latency sensitive online jobs on the same cluster. In this paper, we consider the problem of using a publicly released dataset by Alibaba to model the batch tasks that are often overlooked compared to online services. The dataset contains the arrivals and resource requirements (CPU, memory, etc.) for both batch and online tasks. Our trained model predicts, with high accuracy, the number of batch tasks that arrive in any 30 minute window, their associated CPU and memory requirements, and their lifetimes. It captures over 94% of arrivals in each 30 minute window within a 95% prediction interval. The F1 scores for the most frequent CPU classes exceed 75%, and our memory and lifetime predictions incur less than 1% test data loss. The prediction accuracy of the lifetime of a batch-task drops when the model uses both CPU and memory information, as opposed to only using memory information. 

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments’ size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.

The lack of consideration for application delay requirements in standard loss-based congestion control algorithms (CCAs) has motivated the proposal of several alternative CCAs. As such, Copa is one of the most recent and promising CCAs, and it has attracted attention from both academia andindustry. The delay performance of Copa is governed by amostly static latency-throughput tradeoff parameter, δ. However,a static δ parameter makes it difficult for Copa to achieve consistent delay and throughput over a range of bottleneck bandwidths. In particular, the coexistence of 4G and 5G networks and the wide range of bandwidths experienced in NG-RANs can result in inconsistent CCA performance. To this end, we propose a modification to Copa, Copa-D, that dynamically tunes δ to achieve a consistent delay performance. We evaluate the modification over emulated fixed, 4G, and 5G bottlenecks. The results show that Copa-D achieves consistent delay with minimal impact on throughput in fixed capacity bottlenecks. Copa-D also allows a more intuitive way of specifying the latency-throughput tradeoff and achieves more accurate and predictable delay invariable cellular bottleneck.