Authors: Sandro Linder (AAU, Austria), Samira Afzal (AAU, Austria), Christian Bauer  (AAU, Austria), Hadi Amirpour (AAU, Austria), Radu Prodan (AAU, Austria)and Christian Timmerer (AAU, Austria)

Venue: The 15th ACM Multimedia Systems Conference (Open-source Software and Datasets)

Abstract: Video streaming constitutes 65 % of global internet traffic, prompting an investigation into its energy consumption and CO2 emissions. Video encoding, a computationally intensive part of streaming, has moved to cloud computing for its scalability and flexibility. However, cloud data centers’ energy consumption, especially video encoding, poses environmental challenges. This paper presents VEED, a FAIR Video Encoding Energy and CO2 Emissions Dataset for Amazon Web Services (AWS) EC2 instances. Additionally, the dataset also contains the duration, CPU utilization, and cost of the encoding. To prepare this dataset, we introduce a model and conduct a benchmark to estimate the energy and CO2 emissions of different Amazon EC2 instances during the encoding of 500 video segments with various complexities and resolutions using Advanced Video Coding (AVC)
and High-Efficiency Video Coding (HEVC). VEED and its analysis can provide valuable insights for video researchers and engineers to model energy consumption, manage energy resources, and distribute workloads, contributing to the sustainability of cloud-based video encoding and making them cost-effective. VEED is available at Github.


Authors: Christian Bauer  (AAU, Austria),  Samira Afzal (AAU, Austria)Sandro Linder (AAU, Austria), Radu Prodan (AAU,Austria)and Christian Timmerer (AAU, Austria)

Venue: The 15th ACM Multimedia Systems Conference (Open-source Software and Datasets)

Abstract: Addressing climate change requires a global decrease in greenhouse gas (GHG) emissions. In today’s digital landscape, video streaming significantly influences internet traffic, driven by the widespread use of mobile devices and the rising popularity of streaming plat-
forms. This trend emphasizes the importance of evaluating energy consumption and the development of sustainable and eco-friendly video streaming solutions with a low Carbon Dioxide (CO2) footprint. We developed a specialized tool, released as an open-source library called GREEM , addressing this pressing concern. This tool measures video encoding and decoding energy consumption and facilitates benchmark tests. It monitors the computational impact on hardware resources and offers various analysis cases. GREEM is helpful for developers, researchers, service providers, and policy makers interested in minimizing the energy consumption of video encoding and streaming.

Authors: Seyedehhaleh Seyeddizaji, Joze Martin Rozanec, Reza Farahani, Dumitru Roman and Radu Prodan

Venue: The 2nd Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems Co-located with ICPE 2024

Abstract: While graph sampling is key to scalable processing, little research has tried to thoroughly compare and understand how it preserves features such as degree, clustering, and distances dependent on the graph size and structural properties. This research evaluates twelve widely adopted sampling algorithms across synthetic and real datasets to assess their qualities in three metrics: degree, clustering coefficient (CC), and hop plots. We find the random jump algorithm to be an appropriate choice regarding degree and hop-plot metrics and the random node for CC metric. In addition, we interpret the algorithms’ sample quality by conducting correlation analysis with diverse graph properties. We discover eigenvector centrality and path-related features as essential features for these algorithms’ degree quality estimation, node numbers (or the size of the largest connected component) as informative features for CC quality estimation and degree entropy, edge betweenness and path-related features as meaningful features for hop-plot metric. Furthermore, with increasing graph size, most sampling algorithms produce better-quality samples under degree and hop-plot metrics.





Authors: Reza Farahani, Frank Loh, Dumitru Roman, and Radu Prodan
Venue: The 2nd Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems Co-located with ICPE 2024
Abstract: The growing desire among application providers for a cost model
based on pay-per-use, combined with the need for a seamlessly
integrated platform to manage the complex workflows of their
applications, has spurred the emergence of a promising comput-
ing paradigm known as serverless computing. Although serverless
computing was initially considered for cloud environments, it has
recently been extended to other layers of the computing continuum,
i.e., edge and fog. This extension emphasizes that the proximity of
computational resources to data sources can further reduce costs
and improve performance and energy efficiency. However, orches-
trating the computing continuum in complex application workflows,
including a set of serverless functions, introduces new challenges.
This paper investigates the opportunities and challenges introduced
by serverless computing for workflow management systems (WMS)
on the computing continuum. In addition, the paper provides a
taxonomy of state-of-the-art WMSs and reviews their capabilities.

”Fictional Practices of Spirituality” provides critical insight into the implementation of belief, mysticism, religion, and spirituality into (digital) worlds of fiction. This first volume focuses on interactive, virtual worlds – may that be the digital realms of video games and VR applications or the imaginary spaces of life action role-playing and soul-searching practices. It features analyses of spirituality as gameplay facilitator, sacred spaces and architecture in video game geography, religion in video games and spiritual acts and their dramaturgic function in video games, tabletop, or larp, among other topics. The contributors offer a first-time ever comprehensive overview of play-rites as spiritual incentives and playful spirituality in various medial incarnations.

The anthology was edited by Felix Schniz and Leonardo Marcato. It is now available as a printed copy, or for download via Open Access. Published by transcript 2023.

book: Fictional Practices of Spirituality I

Cluster Computing

DFARM: A deadline-aware fault-tolerant scheduler for cloud computing

Authors: Ahmad Awan, Muhammad Aleem, Altaf Hussain, Radu Prodan


Cloud computing has become popular for small businesses due to its cost-effectiveness and the ability to acquire necessary on-demand services, including software, hardware, network, etc., anytime around the globe. Efficient job scheduling in the Cloud is essential to optimize operational costs in data centers. Therefore, scheduling should consider assigning tasks to Virtual Machines (VMs) in a Cloud environment in such a manner that could speed up execution, maximize resource utilization, and meet users’ SLA and other constraints such as deadlines. For this purpose, the tasks can be prioritized based on their deadlines and task lengths, and the resources could be provisioned and released as needed. Moreover, to cope with unexpected execution situations or hardware failures, a fault-tolerance mechanism could be employed based on hybrid replication and the re-submission method. Most of the existing techniques tend to improve performance. However, their pitfall lies in certain aspects such as either those techniques prioritize tasks based on a singular value (e.g., usually deadline), only utilize a singular fault tolerance mechanism, or try to release resources that cause more overhead immediately. This research work proposes a new scheduler called the Deadline and fault-aware task Adjusting and Resource Managing (DFARM) scheduler, the scheduler dynamically acquires resources and schedules deadline-constrained tasks by considering both their length and deadlines while providing fault tolerance through the hybrid replication-resubmission method. Besides acquiring resources, it also releases resources based on their boot time to lessen costs due to reboots. The performance of the DFARM scheduler is compared to other scheduling algorithms, such as Random Selection, Round Robin, Minimum Completion Time, RALBA, and OG-RADL. With a comparable execution performance, the proposed DFARM scheduler reduces task-rejection rates by $2.34 – 9.53$ times compared to the state-of-the-art schedulers using two benchmark datasets.

Journal of Grid Computing

Authors: Zeinab Bakhshi, Guillermo Rodriguez-Navas, Hans Hansson, Radu Prodan


This paper analyzes a persistent storage method’s timing performance for distributed container-based architectures in industrial control applications. The method focuses on ensuring data availability and consistency while accommodating faults. The analysis considers four aspects: placement strategy, design options, data size, and evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Moreover, the method is applicable for evaluating timing constraints in other container-based critical applications that require persistent storage.Further comparison results reveal that, while the method may underperform current centralized solutions under fault-free conditions, it outperforms the centralized solutions in failure scenarios.

ACM Mile High Video 2024 (mhv), Denver, Colorado, February 11-14, 2024

Authors: Vignesh V Menon, Prajit T Rajendran, Reza Farahani, Klaus Schoffmann, Christian Timmerer

Abstract: The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. This paper proposes a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF) model for video streaming applications that determines the visual quality of the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the frames from the original and reconstructed videos and fuses them using a long short-term mem- ory (LSTM)-based model to estimate the visual quality. Experimental results show that VQ-TIF estimates the visual quality with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy consumption, assuming an Ultra HD (2160p) display resolution.

ACM Mile High Video 2024 (mhv), Denver, Colorado, February 11-14, 2024

Authors: Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt, Austria), Minh Nguyen (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT’s carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device’s battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.

Keywords: HTTP adaptive streaming, Energy, Adaptive Bitrate (ABR), DASH


IEEE Transactions on Network and Service Management

Authors: Reza Farahani, Ekrem Cetinkaya, Christian Timmerer, Mohammad Shojafar, Mohammad Ghanbari, and Hermann Hellwagner

Abstract: Recent years have witnessed video streaming demands evolve into one of the most popular Internet applications. With the ever-increasing personalized demands for high-definition and low-latency video streaming services, network-assisted video streaming schemes employing modern networking paradigms have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context. The emergence of such techniques addresses long-standing challenges of enhancing users’ Quality of Experience (QoE), end-to-end (E2E) latency, as well as network utilization. However, designing a cost-effective, scalable, and flexible network-assisted video streaming architecture that supports the aforementioned requirements for live streaming services is still an open challenge. This article leverage novel networking paradigms, i.e., edge computing and Network Function Virtualization (NFV), and promising video solutions, i.e., HAS, Video Super-Resolution (SR), and Distributed Video Transcoding (TR), to introduce A Latency- and cost-aware hybrId P2P-CDN framework for liVe video strEaming (ALIVE). We first introduce the ALIVE multi-layer architecture and design an action tree that considers all feasible resources (i.e., storage, computation, and bandwidth) provided by peers, edge, and CDN servers for serving peer requests with acceptable latency and quality. We then formulate the problem as a Mixed Integer Linear Programming (MILP) optimization model executed at the edge of the network. To alleviate the optimization model’s high time complexity, we propose a lightweight heuristic, namely, Greedy-Based Algorithm (GBA). Finally, we (i) design and instantiate a large-scale cloud-based testbed including 350 HAS players, (ii) deploy ALIVE on it, and (iii) conduct a series of experiments to evaluate the performance of ALIVE in various scenarios. Experimental results indicate that ALIVE (i) improves the users’ QoE by at least 22%, (ii) decreases incurred cost of the streaming service provider by at least 34%, (iii) shortens clients’ serving latency by at least 40%, (iv) enhances edge server energy consumption by at least 31%, and (v) reduces backhaul bandwidth usage by at least 24% compared to baseline approaches.

Keywords: HTTP Adaptive Streaming (HAS); Edge Com- puting; Network Function Virtualization (NFV); Content Deliv- ery Network (CDN); Peer-to-Peer (P2P); Quality of Experience (QoE); Video Transcoding; Video Super-Resolution.