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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.

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.

Network latency is a critical factor for the perceived quality of experience for many applications. With an increasing focus on interactive and real-time applications, which require reliable and low latency, the ability to continuously and efficiently monitor latency is becoming more important than ever. Always-on passive monitoring of latency can provide continuous latency metrics without injecting any traffic into the network. However, software-based monitoring tools often struggle to keep up with traffic as packet rates increase, especially on contemporary multi-Gbps interfaces. We investigate the feasibility of using eBPF to enable efficient passive network latency monitoring by implementing an evolved Passive Ping (ePPing). Our evaluation shows that ePPing delivers accurate RTT measurements and can handle over 1 Mpps, or correspondingly over 10 Gbps, on a single core, greatly improving on state-of-the-art software based solutions, such as PPing.

Modern information technology (IT) infrastructures are becoming more complex to meet the diverse demands of emerging technology paradigms such as 5G/6G networks, edge, and internet of things (IoT). The intricacy of these infrastructures grows further when hosting containerized workloads as microservices, resulting in the challenge to detect and troubleshoot performance issues, incidents or even outages of critical use cases like industrial automation processes. Thus, fine-grained measurements and associated visualization are essential for operation observability of these IT infrastructures. However, most existing observability tools operate independently without systematically covering the entire data workflow. This paper presents an integrated design for multi-stage observability workflows, denoted as DistributEd obServability frameworK (DESK). The proposed framework aims to improve observability workflows for measurement, collection, fusion, storage, visualization, and notification. As a proof of concept, we deployed the framework in a Kubernetes-based testbed to demonstrate the successful integration of various components and usability of collected observability data. We also conducted a comprehensive study to determine the caused overhead by DESK agents at the reasonably powerful edge node hardware, which shows on average a CPU and memory overhead of around 2.5 % of total available hardware resource.

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.

Traffic optimization and smart buffering are fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast and elephant traffics. However, general performance models providing insights on how various factors affect traffic performance metrics needed by these management functions are missing. For the special case of incast, the existing models are analytical ones, either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, this paper proposes an SDN-enabled machine-learning-based performance modeling approach in data center networks that leverages random forest predictions. Evaluations based on datasets constructed through intensive NS-3 simulations show that we can achieve accurate predictions of incast and elephant performance metrics based on various features. With this performance modeling capability, smart buffering schemes or traffic optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance continuously in data centers.

Modern information technology (IT) infrastructures are becoming more complex to meet the diverse demands of emerging technology paradigms such as edge, cloud, and the industrial internet of things (IIoT). The intricacy of these infrastructures grows further when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Fine-grained measurements and associated visualization are essential for these IT infrastructures’ operation observability. However, most existing monitoring tools operate independently, and they are not yet capable of systematically covering the entire observability workflow. Thus, this paper presents an integrated design for multi-stage observability workflows, denoted as AIDA DistributEd obServability frameworK (DESK). The proposed framework aims to improve observability workflows for measurement, collection, fusion, storage, visualization, and notification. As a proof of concept, we deployed an initial-stage implementation of the framework on a Kubernetes-based testbed to demonstrate the successful integration of various components., AIDA

The deployment of the next generation of cellular networks (5G) is expanding to enable new services and improve the quality of existing ones. Despite the benefits of 5G networks, they also present new challenges for the performance of classical and recently-proposed congestion control algorithms (CCAs), e.g., Bottleneck bandwidth and round-trip propagation time (BBR) and Copa. Despite their successful adoption in the Internet, BBR has been shown to overestimate the bottleneck bandwidth in cellular networks, and Copa has not been independently tested on a similar scale and detail as BBR in cellular networks. In this work, we compare the performance of these fairly recent CCAs as well as the widely deployed CUBIC CCA, and a modification to BBR for cellular networks (RBBR) at 5G rates. The evaluation is performed using the emerging QUIC protocol and uses both emulations and live experiments. Our results show that in 5G networks, CUBIC, BBR, and Copa suffer from significant bufferbloat, longer packet delays, and lower throughput, respectively. We also observe that in cases where the bottleneck is largely in the 5G link, RBBR can offer a significant delay reduction compared to BBR and CUBIC.

Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.

Cellular networks have evolved to support high peak bitrates with low loss rates as observed by the higherlayers. However, applications and services running over cellular networks are now facing other difficult congestion-related challenges, most notably a highly variable link capacity and bufferbloat. To overcome theseissues and improve performance of network traffic in 4G/5G cellular networks, a number of in-network and end-to-end solutions have been proposed. Fairness between interacting congestion control algorithms (CCAs) has played an important role in the type of CCAs considered for research and deployment. The placement of content closer to the user and the allocation of per-user queues in cellular networks has increased the likelihood of a cellular access bottleneck and reduced the extent of flow interaction between multiple users. This has resulted in renewed interest in end-to-end CCAs for cellular networks by opening up room for researchand exploration. In this work, we present end-to-end CCAs that target a high throughput and a low latency over highly variable network links, and classify them according to the way they address the congestion control. The work also discusses the deployability of the algorithms. In addition, we provide insights into possible future research directions, such as coping with a higher degree of variability, interaction of CCAs in as hared bottleneck, and avenues for synergized research, such as CCAs assisted by software defined networking and network function virtualization. We hope that this work will serve as a starting point for systematically navigating through the expanding number of cellular CCAs., 5th Generation End-to-end Network, Experimentation, System Integration, and Showcasing (5GENESIS)

The fifth generation (5G) cellular network aims at providing very high data rates, ultra reliable low latency communications, and a vast increase of connection density. As one of the design trends towards these objectives, 5G exploits multi-connectivity, i.e., the concurrent use of multiple access networks. The Access Traffic Steering, Switching, and Splitting (ATSSS) architecture has recently been proposed to enable 5G multi-connectivity, and multipath transport protocols have emerged as a key ATSSS technology enabler. Within this context, this survey presents a detailed review of multipath transport protocols, identifies their existing and potential exploitation in ATSSS, and suggests their applicability for enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) services. To this end, we first review 5G background and current standardization activities around multi-connectivity and the ATSSS architecture. We then provide an in-depth review of multipath transport protocols, covering four core functionalities, i.e., path management, scheduling, congestion control, and reliable transfer. Based on the reviewed literature, we further discuss the integration of multipath transport into ATSSS to achieve eMBB and URLLC service requirements. Finally, we also point out major open research issues and discuss possible future directions.

QC 20210609

We introduce rQUIC, an integration of the QUIC protocol and a coding module. rQUIC has been designed to feature different coding/decoding schemes and is implemented in go language. We conducted an extensive measurement campaign to provide a thorough characterization of the proposed solution. We compared the performance of rQUIC with that of the original QUIC protocol for different underlying network conditions as well as different traffic patterns. Our results show that rQUIC not only yields a relevant performance gain (shorter delays), especially when network conditions worsen, but also ensures a more predictable behavior. For bulk transfer (long flows), the delay reduction almost reached 70% when the frame error rate was 5%, while under similar conditions, the gain for short flows (web navigation) was approximate to 55%. In the case of video streaming, the QoE gain (p1203 metric) was, approximately, 50%.

The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-reliable low latency, and massive connection density. As one of the fundamental design trends toward these objectives, 5G exploits multi-connectivity (i.e., the concurrent use of multiple access networks), where multipath transport protocols have emerged as key technology enablers. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths and has a critical impact on the protocol performance. Within this context, we present in this article the first empirical evaluation of state-of-the-art multipath schedulers based on real 5G data, for both static and mobile scenarios. Furthermore, we introduce M-Peekaboo, which builds on a state-of-the-art learning-based multipath scheduler and extends its usage to 5G networks. Our results illustrate the benefits of employing a learning-based multipath scheduler for 5G networks and motivate further studies of advanced learning schemes that can adapt more quickly to the path conditions, and take into account the emerging features and requirements of 5G and beyond networks.

The concept of a fully connected world in the fifth generation technology envisions the massive rollout of small and constrained Internet of Things (IoT) devices. In such IoT environments, machineto-machine (M2M) communications is key for allowing these devices (e. g., sensors, actuators, smart meters/monitors) to collect data toward a remote server. We address this particular scenario by building and evaluating an IoT multi-access reference architecture, with a multipath TCP ( MPTCP) gateway connected to multiple cellular networks to offload M2M data traffic. Experimental results from CoAP-emulated traffic indicate that the our proposed system together with the benefits of MPTCP improve IoT application performance. More specifically, multipath- based M2M data traffic offloading increases throughput and significantly reduces latency of CoAP requests as the number of sensor networks increases.