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Automated Calibration of Parallel and distributed computing Simulators: A Case study

Automated Calibration of Parallel and Distributed Computing ...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： McDonald, Jesse Horzela, Maximilian Suter, Frederic Casanova, Henri Univ Hawaii Manoa Honolulu HI 96822 USA Karlsruhe Inst Technol Karlsruhe Germany Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9798350364613;9798350364606

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or "calibrated," based on ground truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.

关键词： Simulation of distributed computing platforms and applications Simulation accuracy and scalability Simulation calibration

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Comparative Analysis of Load Balancing Algorithm using Cloud Analyst 1

Comparative Analysis of Load Balancing Algorithm using Cloud...

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1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Singhal, Sankit Vats, Tushar Solanki, Durgesh Singh, Meeta Manav Rachna International Institute of Research and Studies Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350356816

Cloud computing is becoming a key player in distributing hardware, platforms & software via the internet. There are both upsides & downsides to using computing today. Its main features include lower costs, constant accessibility, easy backup retrieval, good throughput, & storage capabilities. However, we also must consider data integrity, confidentiality issues, cloud architecture, supervision, and load balancing as significant *** effectively manage resources from the vendor, we must optimize the load of submitted jobs. Load balancing is crucial to avoid performance decline from a single centralized server. Many algorithms have been suggested to tackle this challenge. This paper explores load balancing strategies to enhance resource performance and evenly distribute workloads among servers. © 2024 IEEE.

关键词： Cloud platforms

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LASOT: An Effective Traffic based TDLC Algorithm for Location Aware Security on Things with FoG computing 1

LASOT: An Effective Traffic based TDLC Algorithm for Locatio...

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1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Tushar Sankar Singh, Ajay Galgotia University Dept of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

internet of Things in which network technology is participating vital roles to providing wide space for online Services, automation oriented and Networking Applications to both types of networks (Wired and Wireless). Nowadays IOT is one of the trending area for maximum research people. IOT is making true of all envisioned things in real time with helps of sensors, computation methodology, various networks, etc. This rapid growth of research area still facing very huge challenging issues on privacy and security regards. This proposed research work, suggesting a LASOT (Location Aware Secure of Things) methodology with FoG computing for minimizing privacy and security issues on IOT. Also introducing an TDLC algorithm which is helps to find the level of attacks possibility on that routing, data quality level and variation on time to arrive the destination. we analysed different research on this topic and made practical experiments maximum data drop and attacks happens on network traffic and crossing the awarded time for communication. Herewith simulation result shows significant changes and benefits difference between current security and privacy models. Also this simulation results provide the space to validate our proposed methodology and algorithm efficiencies. © 2024 IEEE.

关键词： Fog computing

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Performance Analysis and Measurement of Geospatial Fog-Cloud computing System for Data storage, Visualizations and Analysis 1

Performance Analysis and Measurement of Geospatial Fog-Cloud...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Goswami, Veena Kandpal, Meenakshi Behera, Satyaban Qurashi, Shahazad N. Mund, G.B. Barik, Rabindra K. School of Computer Applications KIIT Deemed to be University Bhubaneshwar India OUTR Department of CSE Bhubaneshwar India School of IST GM University Sambalpur India Jazan University Kingdom of Saudi Arabia Department of Health Informatics College of PHTM Saudi Arabia

ISBN: (纸本)9798350361780

Geospatial Cloud and fog computing have experienced significant growth due to advanced Information and Communication technology (ICT) that enables geospatial web services. These technologies are known for their robust computing infrastructure, which supports various services for smart city, smart health, smart grid, and smart transportation. To meet Quality of Service (QoS) needs, distributed computing technologies like geospatial cloud and fog computing are used together to provide more specific degrees of granularity. Lately, researchers have been investigating the use of fog in geospatial cloud systems to enhance the efficiency of data storage, visualizations and analysis. This research paper enhances the performance of a Geospatial Fog-Cloud system by doing a thorough investigation and evaluation of the performance of models used for data storage, visualizations and analysis. This work provides a compre-hensive system model and utilizes suitable M / M /1 queueing with balking models to analyze the performance. The effectiveness of the proposed analytical queueing model is demonstrated by simulation experiments conducted using MAPLE 18 tool. These tests analyse multiple scenarios and assess the average number of requests in the system, as well as the projected cycle time that a request spends in the system for different parameters. © 2024 IEEE.

关键词： Fog computing

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Digital In-Memory computing to Accelerate Deep Learning Inference on the Edge

Digital In-Memory Computing to Accelerate Deep Learning Infe...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Perri, stefania Zambelli, Cristian Ielmini, Daniele Silvano, Cristina Univ Calabria Arcavacata Di Rende Italy Univ Ferrara Ferrara Italy Politecn Milan Milan Italy

ISBN: (纸本)9798350364613;9798350364606

Deploying Deep Learning (DL) models on edge devices presents several challenges due to the limited set of processing and memory resources, and the bandwidth constraints while ensuring performance and energy requirements. In-memory computing (IMC) represents an efficient way to accelerate the inference of data-intensive DL tasks on the edge. Recently, several analog, digital, and mixed digital-analog memory technologies emerged as promising solutions for IMC. Among them, digital SRAM IMC exhibits a deterministic behavior and compatibility with advanced technology scaling rules making it a viable path for integration with hardware accelerators. This work focuses on discussing the potentially powerful aspects of digital IMC (DIMC) on edge System-on-Chip (SoC) devices. The limitations and ()pen challenges of DIMC are also discussed.

关键词： Deep Learnig Hardware accelerators In-memory computing System-on-chip devices

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DiDit 2024 - Proceedings of the 1st international Workshop on distributed Digital Twins, co-located with DisCoTec 2024 - 19th international Federated conference on distributed computing Techniques, DisCoTec 2024

DiDit 2024 - Proceedings of the 1st International Workshop o...

引用

1st international Workshop on distributed Digital Twins, DiDit 2024

The proceedings contain 6 papers. The topics discussed include: realizing distributed digital twins within federated digital infrastructures;cybersecurity in distributed industrial digital twins: threats, defenses, and key takeaways;development and evaluation of a FIWARE-based digital twin prototype for road system;an implementation model for correct audit logging in cyber-physical systems;reliability-by-design for digital twins: value creation and trust throughout the whole lifecycle;and science-twins: digital twins for interactive lecture demonstrations.

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Teaching Performance Metrics in Parallel computing Courses

Teaching Performance Metrics in Parallel Computing Courses

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Vargas-Perez, Sandino Kalamazoo Coll Dept Comp Sci Kalamazoo MI 49007 USA

ISBN: (纸本)9798350364613;9798350364606

As high-performance computing technologies advance, the significance of parallel programming in various domains is becoming increasingly evident since it allows us to harness the power of heterogeneous computing and solve complex problems more efficiently. However, for students to master this type of computation and be able to apply it in different contexts, it requires understanding how measuring and optimizing parallel code impacts its performance. This paper presents an approach to enhancing students' comprehension of parallel performance metrics through an interactive exercise that complements lectures on parallel performance and improves assessment.

关键词： Parallel computing HPC Undergraduate Education

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Enhancing a Random Forest Model Based on Single Rule Reduction for Tax Evasion Depends on the Values of K in K-Fold Validation Technique 1

Enhancing a Random Forest Model Based on Single Rule Reducti...

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1st international conference on Emerging Technologies for Dependable internet of Things, ICETI 2024

作者： Abdullah, Abeer Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Al-Gaphari, Ghaleb Al-Badani, Abdulkader M. Alsabry, Ayman Shujaaddeen Computer Science Faculty of Computer and Information Technology Sana’a University Sana’a Yemen Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen

ISBN: (纸本)9798331533557

This paper developed a new model called Single Rule Random Forest (SrRF) that enhances the performance of the Fandom Forest technique(RF)and reduces its rules, then compared the performance of this model with a set of ML models as follows (DT, RF, GPT) of the datasets provided by the Tax Authority of Yemen which consists of 1083 records using two values for k in K-Fold Cross Validation). The results showed that by using the value k=10, the GPT Classifier gave the highest result of 100 % but this result will cause overfitting, then the SRI-RF Classifier gave the best result with 99.89%, while RF gave the worst result. By using the value k=5, the GPT Classifier also gave the highest result of 100 % but this result also caused overfitting, then the SrRF Classifier gave the best result, while RF gave the worst result. From the above, the researchers also note that training the data using K-fold cross-validation with the value K=10 gave better results. On the other side, our proposed model SRIRF with the value of k=10 gave the best result. © 2024 IEEE.

关键词： Decision trees

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Optimization of Electric Vehicle Charge Scheduling in Multiple Parking Lots: A Method Based on Metaheuristics and High Performance computing 1

Optimization of Electric Vehicle Charge Scheduling in Multip...

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1st international conference on computing, internet of Things and Microwave Systems, ICCIMS 2024

作者： Brooks, Katerina Roberge, Vincent Tarbouchi, Mohammed Royal Military College of Canada Department of Electrical and Computer Engineering Kingston Canada

ISBN: (纸本)9798350351736

This paper presents a centralized model based on metaheuristics to solve the problem of optimal Electric Vehicle (EV) charge scheduling in multiple parking lots. A centralized optimization model using two-level particle swarm optimization was developed, to find the optimal allocation of power between parking lots by a central aggregator, and to find the optimal EV charging schedule for each parking lot within the system, in order to minimize the cost to charge all EVs. High Performance computing (HPC) techniques are used to combat the high resource requirements and long simulation time associated with the problem size, to increase the scalability of the solution, and to complete the optimization in an acceptable real-time interval. The parallelized centralized optimization model was validated against its sequential version and a decentralized model on an HPC cluster, where it provided more optimal, lower cost solutions than the decentralized model, and provided average computation speedups of up to 139 times faster than the sequential model. The model was tested with scenarios from 3 to 27 parking lots, on the MATPOWER 18, 33, 69, and 141-bus distribution systems. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Exploring Sensor-Based Smart Farming Technologies in the internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE international conference on computing and Data Science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： internet of things

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