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Technological Engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： S. Jagan G. Murugan K. Rajasathiya Arti Badhoutiya Hemalatha S Kabulov Ilyos Laith H. Alzubaidi Tabitha Indupalli CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Business System E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Computer Science and Engineering P.S.R.R College of Engineering Sivakasi Department of Electrical Engineering GLA University Mathura EEE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan The Islamic University Najaf Iraq GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

This research proposes a novel approach that integrates distinct Artificial Intelligence (AI) methodologies to address the issue of energy demand planning in Smart Homes (SH). The novel technique is designed to address a multi-objective scheduling issue, aiming to strike a balance between energy consumption and user convenience. The AI-based Power scheduling issue in a Smart Home (AI-PSSH) involves efficiently scheduling intelligent home equipment within specified constraints and flexible pricing schemes. Various methods have been suggested to tackle the problem of AI-PSSH effectively, including both optimal and non-optimal strategies. This study presents a novel formulation for the Intelligent Home Batteries (IHB) in the context of AI-PSSH, which mitigates the impact of constraints in achieving the optimum or near-optimum outcomes. IHB may optimize the scheduling of intelligent home devices by saving excess power when it is not needed and using saved power when it is more suited to achieve the goals of PSSH. PSSH is a multi-objective optimization problem aimed at concurrently achieving all goals. Incorporating AI into smart homes has facilitated the development of sophisticated energy management techniques in scheduling and power optimization. The approach utilizes Particle Swarm Optimization (PSO) to improve the effectiveness of scheduling algorithms. Integrating PSO into the scheduling process enhances flexibility and enables smart decision-making based on real-time adjustments. The simulation results assess the proposed multi-objective AI-PSSH utilizing the IHB (MO-AI-PSSH) strategy by considering five possibilities of energy use and flexible pricing strategies. The efficiency of the suggested MO-AI-PSSH technique is compared to that of existing cutting-edge algorithms using the suggested databases utilizing the provided datasets. The MO-AI-PSSH technique, as suggested, demonstrates superior performance compared to the other algorithms in almost all instances.

关键词： Energy consumption scheduling algorithms Simulation Smart homes Pricing Real-time systems Artificial intelligence

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A Heterogeneous Multicore Co-scheduling Algorithm Based on Multi-characteristic Fuzzy Cluster 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Chen, Xinyu Chen, Xueyi Chen, Yixiang National Engineering Research Center of Trustworthy Embedded Software East China Normal University Shanghai200062 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Heterogeneous multicore system integrated with FPGA has been shown to yield superior performance when executing real-time applications. In this paper, we propose a heterogeneous multicore co-scheduling algorithm based on multi-characteristic fuzzy cluster that utilizes fuzzy cluster to optimize task scheduling. Experimental results demonstrate that our algorithm achieves better scheduling results which is 8.85% faster than ESHCS, 6.03% faster than MARCO, and 9.56% faster than HSPA. The effectiveness and superiority of our algorithm make it a suitable choice for real-time applications with demanding requirements, particularly in the domain of autonomous driving’s environmental perception tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： scheduling algorithms

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On the Complexity of scheduling Problems with a Fixed Number of Parallel Identical Machines

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SSRN 2023年

作者： Jansen, Klaus Kahler, Kai Department of Computer Science Kiel University Christian-Albrechts-Platz 4 Kiel24118 Germany

In parallel machine scheduling, we are given a set of jobs, together with a number of machines and our goal is to decide for each job, when and on which machine(s) it should be scheduled in order to optimize some objective function. Different machine models, job characteristics and objective functions result in a multitude of scheduling problems and many of them are NP-hard, even for a fixed number of identical machines. In this work, we give conditional running time lower bounds for a large number of scheduling problems, indicating the optimality of some classical algorithms. We show that the algorithm by Lawler and Moore for $1||\sum w_jU_j$ and $Pm||C_{\max}$, as well as the algorithm by Lee and Uzsoy for $P2||\sum w_jC_j$ are probably optimal. On the algorithmic side, we improve the dynamic programs for $P2|any|C_{\max}$ and $P3|any|C_{\max}$ by Du and Leung, obtaining an optimal running time in the two-machine case. © 2023, The Authors. All rights reserved.

关键词： scheduling algorithms

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An Adaptive User scheduling Algorithm for 6G Massive MIMO Systems

An Adaptive User Scheduling Algorithm for 6G Massive MIMO Sy...

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International Conference on Advanced Communication Technology (ICACT)

作者： Robin Chataut Robert Akl Utpal Kumar Dey Computer Science Department Fitchburg State University Fitchburg MA USA College of Engineering University of North Texas Denton Texas USA

Massive Multiple-input Multiple-output (MIMO) is a sub-6GHz wireless access technology and is one of the key enabling technology for current 5G and upcoming 6G networks. Massive MIMO technology is also considered crucial for realizing the potential capacity of upcoming 6G and beyond networks. However, with hundreds of antenna terminals, user scheduling during downlink communication is one of the major challenges in massive MIMO system deployment. In this paper, we propose a novel scheduling algorithm that improves the area throughput and error performance and ensures fairness among all users. Our scheduling method uses the average channel rate as the scheduling criteria, which is obtained from the channel state information provided by the users during the uplink communication. The results from the Matlab simulations convey that the proposed algorithm based on channel rate is fair and better than the conventional scheduling algorithms as it provides better sumrate, throughput, and bit error performance.

关键词： 6G mobile communication Wireless communication Adaptation models Adaptive systems scheduling algorithms Massive MIMO Throughput

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An Improved Particle Swarm Optimization Algorithm for scheduling Tasks in Cloud Environment

SSRN

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SSRN 2023年

作者： Wang, Ziren Hu, Xiao Xiang Wei, Peng School of Automation Northwestern Polytechnical University Xi’an710000 China

Cloud computing is a parallel and distributed system. It can provide services dynamically according to the contract between service providers and users. However, as more tasks arrive, inappropriateness of scheduling task on VMs can lead huge resource waste and load unbalance, which becomes a seriously challenging problem. Therefore in order to improve users’ satisfaction with reasonable cost, tasks should be scheduled efficiently. Conventional scheduling algorithms have been widely adopted in cloud system and Particle Swarm Optimization is an efficient way. However existing works using PSO (particle swarm optimization) sometimes search the optima without analyzing task scheduling situations *** this paper, we propose SNSK-IPSO algorithm, which develops as a two-phases algorithm: enumerating all distributed solutions between VMs and tasks, finding the optimal solution through IPSO. It not only minimizes the execution time, but also improves resource utilization and load balance. Several experiments demonstrate that our novel algorithm outperforms others in terms of achieving load balance, higher resource utilization and lower execution times. © 2023, The Authors. All rights reserved.

关键词： scheduling algorithms

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Adaptive Dwell scheduling for Simultaneous Multi-Beam Radar System Based on Array Element Selection with Different Polarization Characteristics

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SSRN 2023年

作者： Heng, Siyu Cheng, Ting He, Zishu Wang, Yuanqing Li, Zhongzhu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

Adaptive dwell scheduling is a key for simultaneous multi-beam radar system to give full play to its tactical performance. The dwell scheduling for such radar sysytem based on the selection of array elements with different polarization characteristics is first investigated in this manuscript. The optimal dwell scheduling model whose goal is to achieve the maximal scheduling gain for the system is established under the time, array element, frequency and electromagnetic compatibility constraints of the system. The solution of this optimization problem results in our proposed dwell scheduling algorithm, where the dwell tasks are selected to be analyzed sequentially according to synthetic priority at each scheduling analysis time point, and the element sets are chosen for dwell tasks under all kinds of constraints to realize the simultaneous execution of multiple tasks. Simulation results demonstrate that the proposed algorithm can realize real-time dwell scheduling and achieve better scheduling performance for the multi-beam radar system compared with existing algorithm. © 2023, The Authors. All rights reserved.

关键词： scheduling algorithms

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A General Analytical Framework of scheduling algorithms for Network Navigation

A General Analytical Framework of Scheduling Algorithms for ...

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IEEE International Conference on Communications

作者： Tianheng Wang Yuan Shen Moe Z. Win Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge Massachusetts

Location-awareness plays a key role in various applications in future wireless networks. In GPS-challenged environments, location-awareness can be achieved via wireless navigation networks, which call for an efficient scheduling algorithm to optimize the navigation performance under communication constraints. In this paper, we develop a general framework for the design and analysis of scheduling algorithms for navigation networks with multiple measurement pairs per time slot. In particular, we provide sufficient and necessary conditions for the stability of the error evolution, and derive bounds on the time-averaged network localization errors (NLEs) for opportunistic and random scheduling. Furthermore, we show that the two scheduling algorithms are optimal in terms of the error scaling with respect to the number of agents, and we quantify the performance gain from measurement pair selections exploiting the network states. These results provide guidelines for designing efficient scheduling algorithms for network navigation.

关键词： scheduling algorithms Measurement uncertainty Navigation Stability analysis Performance gain Lead Time measurement

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A Novel Low-Latency and Energy-Efficient Task scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

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SENSORS 2022年第14期22卷 5327-5327页

作者： Alatoun, Kholoud Matrouk, Khaled Mohammed, Mazin Abed Nedoma, Jan Martinek, Radek Zmij, Petr Al Hussein Bin Talal Univ Fac Engn Dept Comp Engn Maan 71111 Jordan Univ Anbar Coll Comp Sci & Informat Technol Ramadi 31001 Iraq VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Telecommun Ostrava 70800 Czech Republic VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn Ostrava 70800 Czech Republic Ind Engn Brose Grp Prumyslovy Pk 302 Koprivnice 74221 Czech Republic

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical;this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

关键词： low-latency Cardiovascular Disease ECG sensors fog computing health monitoring system internet of medical things scheduling algorithms task scheduling

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Work-in-Progress: Measuring Security Protection in Real-time Embedded Firmware 43

Work-in-Progress: Measuring Security Protection in Real-time...

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43rd IEEE Real-Time Systems Symposium (RTSS)

作者： Wu, Yuhao Wang, Yujie Zhai, Shixuan Li, Zihan Li, Ao Wang, Jinwen Zhang, Ning Washington Univ St Louis MO 63110 USA

ISBN: (数字)9781665453462

ISBN: (纸本)9781665453462

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

关键词： Bridges Embedded systems scheduling algorithms Ecosystems Cyber-physical systems Real-time systems Security

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Handling heterogeneous workflows in the Cloud while enhancing optimizations and performance 15

Handling heterogeneous workflows in the Cloud while enhancin...

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15th IEEE International Conference on Cloud Computing (IEEE CLOUD) / IEEE World Congress on Services (IEEE SERVICES)

作者： Cadorel, Emile Coullon, Helene Menaud, Jean-Marc Univ Lille CRIStAL INRIA Spirals Lille France IMT Atlantique INRIA LS2N Nantes France

ISBN: (纸本)9781665481373

The goal of a workflow engine is to facilitate the writing, the deploying, and the execution of a scientific workflow (i.e., graph of coarse-grain and heterogeneous tasks) on distributed infrastructures. With the democratization of the Cloud paradigm, many workflow engines of the state of the art offer a way to execute workflows on distant data centers by using the Infrastructure-as-a-Service (IaaS) or the Function-as a-Service (FaaS) services of Cloud providers. Hence, workflow engines can take advantage of the (presumably) infinite resources and the economical model of the Cloud. However, two important limitations lie in this vision of Cloud-oriented workflow engines. First, by using existing services of Cloud providers, and by managing the workflows at the user side, the Cloud providers are unaware of both the workflows and their user needs, and cannot apply specific resource optimizations to their infrastructure. Second, for the same reasons, handling the heterogeneity of tasks (different operating systems) in workflows necessarily degrades either the transparency for the users (who must provision different types of resources), or the completion time performance of the workflows, because of the stacking of virtualization layers. In this paper, we tackle these two limitations by presenting a new Cloud service dedicated to scientific workflows. Unlike existing workflow engines, this service is deployed and managed by the Cloud providers, and enables specific resource optimizations and offers a better control of the heterogeneity of the workflows. We evaluate our new service in comparison to Argo, a well-known workflow engine of the literature based on FaaS services. This evaluation was made on a real distributed experimental platform with a realistic and complex scenario.

关键词： Cloud computing scheduling algorithms Heuristic algorithms FAA Writing Dynamic scheduling Task analysis

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