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

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

Abstract:

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.

March 5, 2024

DPS, Publication

Paper accepted: Evaluation of Storage Placement in Computing Continuum for a Robotic Application: A simulation-based performance analysis

Journal of Grid Computing

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

Abstract:

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.

March 4, 2024

Announcement

The 1st IEEE ICME Workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES’24)

5-19 July, 2024, Niagra Falls, Canada

The first workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES 2024) aims to bring together researchers and developers to satisfy the data-intensive processing requirements and QoE challenges of live video streaming applications through leveraging heuristic and learning-based approaches.

Delivering video content from a video server to viewers over the Internet is time-consuming in the streaming workflow and has to be handled to offer an uninterrupted streaming experience. The end-to-end latency, i.e., from the camera capture to the user device, particularly problematic for live streaming. Some streaming-based applications, such as virtual events, esports, online learning, gaming, webinars, and all-hands meetings, require low latency for their operation. Video streaming is ubiquitous in many applications, devices, and fields. Delivering high Quality-of-Experience (QoE) to the streaming viewers is crucial, while the requirement to process a large amount of data to satisfy such QoE cannot be handled with human-constrained possibilities. Satisfying the requirements of low latency video streaming applications require the streaming workflow to be optimized and streamlined all together, that includes: media provisioning (capturing, encoding, packaging, an ingesting to the origin server), media delivery (from the origin to the CDN and from the CDN to the end users), media playback (end user video player).

Please click here for more information.

February 23, 2024

Project

Graph-Massivizer project meeting & Joint workshop

Hosted by SINTEF AS, the project meeting of Graph-Massivizer took place from February 07-09, 2024, in Trysil, Norway.
On February 09, a Joint Workshop of the projects UPCAST, enRichMyData and Graph-Massivizer took place to share knowledge across the projects related to data challenges and approaches, find synergies in technology and data sharing, and identify future collaborations.

February 13, 2024

Award, MMIS

ITEC Video Retrieval System Awarded at International Video Search Challenge

The diveXplore video retrieval system, by Klaus Schoeffmann and Sahar Nasirihaghighi, was awarded as the best ‘Video Question-Answering-Tool for Novices’ at the 13th Video Browser Showdown (VBS 2024), which is an international video search challenge annually held at the International Conference on Multimedia Modeling (MMM 2024), which took place this year in Amsterdam, The Netherlands. VBS 2024 was a 6-hours long challenge with many search tasks of different types (known-item search/KIS, ad-hoc video search/AVS, question-answering/QA) in three different datasets, amounting for about 2500 hours of video content, some performed by experts and others by novices recruited from the conference audience.

diveXplore teaser:

https://www.youtube.com/watch?v=Nlt7w0pYWYE

diveXplore demo paper:

https://link.springer.com/chapter/10.1007/978-3-031-53302-0_34

VBS info:

https://videobrowsershowdown.org/

February 6, 2024

MMIS, Research Areas

13th Video Browser Showdown (VBS 2024) held in Amsterdam with 12 International Teams

The 13th Video Browser Showdown (VBS 2024) was held on 29th January, 2024, in Amsterdam, The Netherlands, at the International Conference on Multimedia Modeling (MMM 2024). 12 international teams (from Austria, China, Czech Republic, Germany, Greece, Iceland, Ireland, Italy, Singapore, Switzerland, The Netherlands, Vietnam) competed over about 6 hours for quickly and accurately solving many search tasks of different types (known-item search/KIS, ad-hoc-video search/AVS, question-answering/QA) in three datasets with about 2500 hours of video content. Like in previous years, this large-scale international video retrieval challenge was an exciting event that demonstrated the state-of-the-art performance of interactive video retrieval systems.

February 6, 2024

Announcement, DPS

Sahar Nasirihaghighi on Event Recognition with Hybrid Transformers at MMM 2024

On February 1st, 2024, Sahar Nasirihaghighi presented our work on ‘Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers’ at this year’s International Conference on Multimedia Modeling (MMM 2024) in Amsterdam, The Netherlands.

Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein, Klaus Schoeffmann

Abstract: Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction, and post-operative surgical assessment. Identifying crucial events within these videos is a significant prerequisite in a majority of these applications. In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos. Our dataset includes annotations for critical events associated with major intra-operative challenges and post-operative complications. To validate the precision of our annotations, we assess event recognition performance using several CNN-RNN architectures. Furthermore, we introduce and evaluate a hybrid transformer architecture coupled with a customized training-inference framework to recognize four specific events in laparoscopic surgery videos. Leveraging the Transformer networks, our proposed architecture harnesses inter-frame dependencies to counteract the adverse effects of relevant content occlusion, motion blur, and surgical scene variation, thus significantly enhancing event recognition accuracy. Moreover, we present a frame sampling strategy designed to manage variations in surgical scenes and the surgeons’ skill level, resulting in event recognition with high temporal resolution. We empirically demonstrate the superiority of our proposed methodology in event recognition compared to conventional CNN-RNN architectures through a series of extensive experiments.

February 6, 2024

Announcement, DPS

Radu Prodan at COST Action CERCIRAS Joint Meeting in Montpellier, France

An EU funding programme enabling researchers to set up their own interdisciplinary research networks in Europe and beyond. #COSTactions

Representing Ireland with Prof. Horacio González-Vélez of National College of Ireland at the partner meeting of the Cost Action Cerciras – Connecting Education and Research Communities for an Innovative Resource Aware Society in Montpellier today.

Great alignment with several EU skills projects like ARISA – AI Skills, ESSA Software Skills Digital4Business and Digital4Security by facilitating transversal insights.

January 31, 2024

MMC, Research Areas

Paper accepted: DIGITWISE: Digital Twin-based Modeling of Adaptive Video Streaming Engagement

ACM MMSys 2024, Bari, Italy, Apr. 15-18, 2024

Authors: Emanuele Artioli (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: As the popularity of video streaming entertainment continues to grow, understanding how users engage with the content and react to its changes becomes a critical success factor for every stakeholder. User engagement, i.e., the percentage of video the user watches before quitting, is central to customer loyalty, content personalization, ad relevance, and A/B testing. This paper presents DIGITWISE, a digital twin-based approach for modeling adaptive video streaming engagement. Traditional adaptive bitrate (ABR) algorithms assume that all users react similarly to video streaming artifacts and network issues, neglecting individual user sensitivities. DIGITWISE leverages the concept of a digital twin, a digital replica of a physical entity, to model user engagement based on past viewing sessions. The digital twin receives input about streaming events and utilizes supervised machine learning to predict user engagement for a given session. The system model consists of a data processing pipeline, machine learning models acting as digital twins, and a unified model to predict engagement. DIGITWISE employs the XGBoost model in both digital twins and unified models. The proposed architecture demonstrates the importance of personal user sensitivities, reducing user engagement prediction error by up to 5.8% compared to non-user-aware models. Furthermore, DIGITWISE can optimize content provisioning and delivery by identifying the features that maximize engagement, providing an average engagement increase of up to 8.6 %.