DataCloud project partners met at KTH Kista (Stockholm) to discuss the domain-specific language and the architecture for specifying and running Big Data pipelines.
https://twitter.com/RaduProdanAAU/status/1461352859920785418/photo/1
Distributed and Parallel Systems
Title: Autotuning of exascale applications with anomalies detection
Authors: Dragi Kimovski, Roland Mathá, Gabriel Iuhasz, Fabrizio Marozzo, Dana Petcu, Radu Prodan
Abstract: The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application run-time parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and run-time parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning of exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objectives functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.
Keywords: Exascale computing, Autotuning, Events and Anomalies Detection, Multi-objective Optimisation
Congratulations to Negin Ghamsarian et al., who got their paper “ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos” accepted at the International Conference on Neural Information Processing (ICONIP 2021).
Abstract: Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as ReCal module, which can calibrate the feature maps by employing region intra-and-inter-dependencies and channel-region cross-dependencies. This calibration strategy can effectively enhance semantic representation by correlating different representations of the same semantic label, considering a multi-angle local view centering around each pixel. Thus the proposed module can deal with distant visual characteristics of unique objects as well as cross-similarities in the visual characteristics of different objects. Moreover, we propose a novel network architecture based on the proposed module termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net compared to rival state-of-the-art approaches for all relevant objects in cataract surgery. Moreover, ablation studies reveal the effectiveness of the ReCal module in boosting semantic segmentation accuracy.
On 10 September 2021, ADAPT project was represented at the GOODIT´21 conference in Rome, Italy by Vladislav Kashanskii.
Here you find his presentation about “The ADAPT Project: Adaptive and Autonomous Data Performance Connectivity and Decentralized Transport Decision-Making Network” as pdf and video.
The project “Kärntner Fog” has been accepted in the BRIDGE funding call of FFG.
Abstract: Kärntner Fog aims to contribute with advanced technologies for the distributed optimized provisioning and operation of 5G applications in Austria. For this purpose, it researches and develops a unique infrastructure testbed called the Carinthian Computing Continuum (C3), consisting of heterogeneous Cloud, Fog, and 5G‐Edge devices orchestrated through novel benchmarking, monitoring, analysis, and provisioning services. The project will validate its results using modern virtual reality and smart city use cases in the 5G Playground Carinthia. The results will give companies a competitive technological advantage in exploring 5G‐compliant applications in preparation for the deployment of an Austrian‐wide 5G network by 2025.
Partners:
Alpen-Adria Universität Klagenfurt, ITEC
Project duration: 36 months
The manuscript “Mobility-Aware IoT Application Placement in the Cloud — Edge Continuum” has been accepted for publication in the A* (IF: 5.823) Journal – IEEE Transactions on Services Computing (TSC).
Autors: Dragi Kimovski, Narges Mehran, Christopher Kerth, Radu Prodan
Abstract: The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices’ mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud – Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28% and decreases the completion time by 80% while maintaining a stable energy consumption.
Authors: Shajulin Benedict, Prateek Agrawal, Radu Prodan
Link: Advanced Informatics for Computing Research, CCIS-Springer, 4th ICAICR 2020, Vol. 1393
Abstract: The push for agile pandemic analytic solutions has attained development-stage software modules of applications instead of functioning as full-fledged production-stage applications – i.e., performance, scalability, and energy-related concerns are not optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. This article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze the programs. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 J.
Authors: Nikita Karandikar, Rockey Abhishek, Nishant Saurabh, Zhiming Zhao, Alexander Lercher, Ninoslav Marina, Radu Prodan, Chunming Rong, Antorweep Chakravorty
DOI: https://doi.org/10.1016/j.bcra.2021.100016
Abstract: Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.
Conference: https://escience2021.org/
Title: Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances
Authors: Roland Mathá, Dragi Kimovski, Anatoliy Zabrovskiy, Christian Timmerer and Radu Prodan
Abstract: Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm based instances that promise up to 40% better cost performance ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to 33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.