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Journal article accepted: Cost modelling and Optimisation for Cloud: A Graph-based Approach

Authors: Akif Quddus Khan, Mihhail Matskin, Radu Prodan, Christoph Bussler, Dumitru Roman, Ahmet Soylu

Journal of Cloud Computing: https://journalofcloudcomputing.springeropen.com/

Abstract: Cloud computing has become popular among individuals and enterprises due to its convenience, scalability, and flexibility. However, a major concern for many cloud service users is the rising cost of cloud resources. Since cloud computing uses a pay-per-use model, costs can add up quickly, and unexpected expenses can arise from a lack of visibility and control. The cost structure gets even more complicated when working with multi-cloud or hybrid environments. Businesses may spend much of their IT budget on cloud computing, and any savings can improve their competitiveness and financial stability. Hence, an efficient cloud cost management is crucial. To overcome this difficulty, new approaches and tools are being developed to provide greater oversight and command over cloud computing expenses. In this respect, this article, presents a graph-based approach for modelling cost elements and cloud resources and a potential way to solve the resulting constraint problem of cost optimisation. In this context, we primarily consider utilisation, cost, performance, and availability. The proposed approach is evaluated on three different user scenarios, and results indicate that it could be effective in cost modelling, cost optimisation, and scalability. This approach will eventually help organisations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.

Paper accepted: SEAL-CC: Scalable Latency Evaluation Methodology for Internet-of-Things Services

Authors: Kurt Horvath, Dragi Kimovski, Radu Prodan, Bernd Spiess, Oliver Hohlfeld

Venue: 14th International Conference on Internet of Things (IoT 2024); Oulu, Finland, 19-22 November, https://iot-conference.org/iot2024

Abstract: Traditional network measurement campaigns suffer from the lack of control over network infrastructure and the inability to evaluate communication performance directly, especially for the placement of highly distributed Internet of Things (IoT) services. In response, we propose a novel Scalable Latency Evaluation Methodology for the Computing Continuum (SEAL-CC). SEAL-CC extends beyond short-term evaluations by capturing the long-term responsiveness of networks supporting IoT services on the computing continuum. It organizes and evaluates a network of nodes, offering insights for optimized IoT service placement in urban and international settings. Our contributions include a novel evaluation methodology tailored for IoT services over the computing continuum, a comprehensive framework for transparent network evaluation using distributed Internet measurement platforms, and a real-life case-study validation with recommendations for IoT service placement.

Paper accepted: Graph Sampling Quality Prediction for Algorithm Recommendation

Authors: Haleh Dizaji, Reza Farahani, Dragi Kimovski, Joze Rozanec, Ahmet Soylu, Radu Prodan

Venue: 31st IEEE International Conference on High Performance Computing, Data, and Analytics; Bengaluru, India,  18-21 December

https://www.hipc.org

Abstract: The increasing size of graph structures in real-world applications, such as distributed computing networks, social media, or bioinformatics, requires appropriate sampling algorithms that simplify them while preserving key properties. Unfortunately, predicting the outcome of graph sampling algorithms is challenging due to their irregular complexity and randomized properties. Therefore, it is essential to identify appropriate graph features and apply suitable models capable of estimating their sampling outcomes. In this paper, we compare three machine learning (ML) models for predicting the divergence of five metrics produced by twelve node, edge, and traversal-based graph sampling algorithms: degree distribution (D3), clustering coefficient distribution (C2D2), hop-plots distribution (HPD2) (including the largest connected component (HPD2C)), and execution time. We use these prediction models to recommend suitable sampling algorithms for each metric and conduct mutual information analysis to extract relevant graph features. Experiments on six large real-world graphs from three categories (scale-free, power-law, binomial) demonstrate an accuracy under 20% in C2D2 and HPD2 prediction for most algorithms despite the relatively high similarity displacement. Sampling algorithm recommendations on ten real-world graphs show higher hits@3 for D3 and

C2D2 and comparable results for HPD2 and HPD2C compared to the K-best baseline method accessing true empirical data. Finally, ML models show superior runtime recommendations compared to baseline methods, with

hits@3 over 86% for synthetic and real graphs and hits@1 over 60% for small graphs. These findings are promising for algorithm recommendation systems, particularly when balancing quality and runtime preferences.

 

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Paper accepted for 20th International Conference on Network and Service Management (CNSM 2024)

Title: High Complexity and Bad Quality? Efficiency Assessment for Video QoE Prediction Approaches

Authors: Frank Loh, Gülnaziye Bingöl, Reza Farahani, Andrea Pimpinella, Radu Prodan, Luigi Atzori, Tobias Hoßfeld

Venue: 20th International Conference on Network and Service Management (CNSM 2024)

Abstract:  In recent years, video streaming has dominated Internet data traffic, prompting network providers to ensure high-quality streaming experiences to prevent customer churn. However, due to the encryption of streaming traffic, extensive network monitoring by providers is required to predict the streaming quality and improve their services. Several such prediction approaches have been studied in recent years, with a primary focus on the ability to determine key video quality degradation factors, often without considering the required resources or
energy consumption. To address this gap, we consider existing methods to predict key Quality of Experience (QoE) degradation factors from the literature and quantify the data that have to be monitored and processed for video streaming applications. Based on this, we assess the efficiency of different QoE degradation factor prediction approaches and quantify the ratio between efficiency and the achieved prediction quality. In this context, we identify significant disparities in the efficiency, influenced by data requirements and the specific prediction approach, and finally by the resulting quality. Consequently, we provide insights for network providers to choose the most appropriate method tailored to their specific requirements.

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Adaptivity in Video Streaming through the Transition Lens

Published in: From Multimedia Communication to the Future Internet: Essays Dedicated to the Retirement of Prof. Dr. Dr. h.c. Ralf Steinmetz

Authors: Amr Rizk (Leibniz Universität Hannover, Germany), Hermann Hellwagner (AAU, Austria), Christian Timmerer (AAU, Austria), and Michael Zink (University of Massachusetts Amherst, MA, USA)

Abstract: Adaptivity is a cornerstone concept in video streaming. Equipped with the concept of Transitions, we review in this paper adaptivity mechanisms known from classical video streaming scenarios. We specifically highlight how these mechanisms emerge in a specific context, such that their performance finally depends on the deployment conditions. Using multiple examples we highlight the strength of the concept of adaptivity at runtime for video streaming.

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Paper accepted at ACM TOMM: COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth Utilization

Authors: Michael Seufert (University of Augsburg, Germany), Marius Spangenberger (University of Würzburg, Germany), Fabian Poignée (University of Würzburg, Germany), Florian Wamser (Lucerne University of Applied Sciences and Arts, Switzerland), Werner Robitza (AVEQ GmbH, Austria), Christian Timmerer (Christian Doppler-Labor ATHENA, Alpen-Adria-Universität, Austria), Tobias Hoßfeld (University of Würzburg, Germany)

Journal: ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM)

Abstract: Reaching close-to-optimal bandwidth utilization in Dynamic Adaptive Streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the Quality of Experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm MinOff, which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding (JITE), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our JITE DASH system, which can offer a continuous bit rate slide, in terms of bandwidth utilization and video QoE for different ABR algorithms.

Paper accepted: HEFTLess: A Bi-Objective Serverless Workflow Batch Orchestration on the Computing Continuum

Authors: Reza Farahani, Narges Mehran, Sashko Ristov, and Radu Prodan

Venue: IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan, 24-27 September

Abstract: Extending cloud computing towards fog and edge computing yields a heterogeneous computing environment known as computing continuum. In recent years, increasing demands for scalable, cost-effective, and streamlined maintenance services have led application and service providers to prefer serverless models over monolithic and serverful processing. However, orchestrating the computing continuum in complex application workflows of serverless functions, each with distinct requirements, introduces new resource management and scheduling
challenges. This paper introduces an orchestration service for concurrent serverless workflow processing across the computing continuum called HEFTLess. HEFTLess uses two deployment modes tailored to serve each workflow function: predeployed and undeployed. We formulate the problem as a Binary Linear Programming (BLP) optimization model, incorporating multiple groups of constraints to minimize the overall completion time and monetary cost of executing workflow batches. Inspired by the Heterogeneous Earliest Finish Time (HEFT) algorithm, we
propose a lightweight serverless workflow scheduling heuristic to cope with the high optimization time complexity in polynomial time. We evaluate HEFTLess using two machine learning-based serverless workflows on a real computing continuum testbed, including AWS Lambda and 325 combined on-promise and cloud instances from Exoscale, distributed across five geographic locations. The experimental results confirm that HEFTLess outperforms state-of-the-art methods in terms of both workflow batch completion time and cost.

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Journal article accepted: The Computing Continuum: From IoT to the Cloud

Autohors: Auday Al-Dulaimy, Matthijs Jansen, Bjarne Johansson, Animesh Trivedi, Alexandru Iosup, Mohammad Ashjaei, Antonino Galletta, Dragi Kimovski, Radu Prodan, Konstantinos Tserpes, George Kousiouris, Chris Giannakos, Ivona Brandic, Nawfal Ali, Andre B. Bondi, Alessandro V. Papadopoulos

Journal “Internet of things”: https://link.springer.com/journal/43926

Abstract:

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum.

The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge-cloud computing models and the other for edge-cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

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Paper accepted: Video Encoding Enhancement via Content-Aware Spatial and Temporal Super-Resolution

Authors: Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria),  Ahmed Telili (INSA Rennes, France), Wassim Hamidouche (INSA Rennes, France), Guo Lu (Shanghai Jiao Tong University, China) and Christian Timmerer (AAU, Austria)

Venue: European Signal Processing Conference (EUSIPCO)

Abstract: Content-aware deep neural networks (DNNs) are trending in Internet video delivery. They enhance quality within bandwidth limits by transmitting videos as low-resolution (LR) bitstreams with overfitted super-resolution (SR) model streams to reconstruct high-resolution (HR) video on the decoder end. However, these methods underutilize spatial and temporal redundancy, compromising compression efficiency. In response, our proposed video compression framework introduces spatial-temporal video super-resolution (STVSR), which encodes videos into low spatial-temporal resolution (LSTR) content and a model stream, leveraging the combined spatial and temporal reconstruction capabilities of DNNs. Compared to the state-of-the-art approaches that consider only spatial SR, our approach achieves bitrate savings of 18.71% and 17.04% while maintaining the same PSNR and VMAF, respectively.

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Paper accepted: Content-aware Reference Frame Synthesis for Enhanced Inter Prediction

Authors: Mohammad Ghasempour (AAU, Austria), Yiying Wei (AAU, Austria), Hadi Amirpour (AAU, Austria),  and Christian Timmerer (AAU, Austria)

Venue: European Signal Processing Conference (EUSIPCO)

Abstract: Video coding relies heavily on reducing spatial and temporal redundancy to enable efficient transmission. To tackle the temporal redundancy, each video frame is predicted from the previously encoded frames, known as reference frames. The quality of this prediction is highly dependent on the quality of the reference frames. Recent advancements in machine learning are motivating the exploration of frame synthesis to generate high-quality reference frames. However, the efficacy of such models depends on training with content similar to that encountered during usage, which is challenging due to the diverse nature of video data. This paper introduces a content-aware reference frame synthesis to enhance inter-prediction efficiency. Unlike conventional approaches that rely on pre-trained models, our proposed framework optimizes a deep learning model for each content by fine-tuning only the last layer of the model, requiring the transmission of only a few kilobytes of additional information to the decoder. Experimental results show that the proposed framework yields significant bitrate savings of 12.76%, outperforming its counterpart in the pre-trained framework, which only achieves 5.13% savings in bitrate.