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| 1 | Scheduling of scientific workflows in the ASKALON grid environment显示文摘 | Marek Wieczorek Radu Prodan Thomas Fahringer | 2005 | ACM SIGMOD Record2005，，3： | 1 |
| 2 | Scheduling of scientific workflows in the ASKALON grid environment显示文摘 | Marek Wieczorek Radu Prodan Thomas Fahringer | 2005 | SIGMOD Record2005，34，3： | 1 |
| 3 | Scheduling of scientific workflows in the ASKALON grid environment显示文摘 | Marek Wieczorek Radu Prodan Thomas Fahringer | 2005 | ACM SIGMOD Record2005，，3： | 1 |
| 4 | Scheduling of scientific workflows in the ASKALONgrid environment显示文摘 | Marek Wieczorek Radu Prodan Thomas Fahringer | 2005 | SIGMOD Record2005，34，： | 1 |
| 5 | The ARTICONF approach to decentralized car-sharing显示文摘Social media applications are essential for next-generation connectivity.Today,social media are centralized platforms with a single proprietary organization controlling the network and posing critical trust and governance issues over the created and propagated content.The ARTICONF project funded by the European Union's Horizon 2020 program researches a decentralized social media platform based on a novel set of trustworthy,resilient and globally sustainable tools that address privacy,robustness and autonomy-related promises that proprietary social media platforms have failed to deliver so far.This paper presents the ARTICONF approach to a car-sharing decentralized application(DApp)use case,as a new collaborative peer-to-peer model providing an alternative solution to private car ownership.We describe a prototype implementation of the car-sharing social media DApp and illustrate through real snapshots how the different ARTICONF tools support it in a simulated scenario. | Nishant Saurabh Carlos Rubia Anandakumar Palanisamy Spiros Koulouzis Mirsat Sefidanoski Antorweep Chakravorty Zhiming Zhao Aleksandar Karadimce Radu Prodan | 2021 | Blockchain(Research and Applications)2021，2，3： | 0 |
| 6 | FSpot:Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances显示文摘HTTP Adaptive Streaming(HAS)of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic.Video compression technology plays a vital role in efficiently utilizing network channels,but encoding videos into multiple representations with selected encoding parameters is a significant challenge.However,video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds.In turn,the public clouds,such as Amazon elastic compute cloud(EC2),provide hundreds of computing instances optimized for different purposes and clients’budgets.Thus,there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations.Additionally,the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content.In this paper,we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple×264 codec encoding parameters and video sequences of varying complexity.Then,we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs.Furthermore,we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost.The results show that our approach,on average,can reduce the encoding costs by at least 15.8%and up to 47.8%when compared to a random selection of EC2 spot instances. | Anatoliy Zabrovskiy Prateek Agrawal Vladislav Kashansky Roland Kersche Christian Timmerer Radu Prodan | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 7 | Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering显示文摘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. | Nikita Karandikar Rockey Abhishek Nishant Saurabh Zhiming Zhao Alexander Lercher Ninoslav Marina Radu Prodan Chunming Rong Antorweep Chakravorty | 2021 | Blockchain(Research and Applications)2021，2，2： | 0 |
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