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Resource scheduling of concurrency based applications in IoT based cloud environment

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期14卷 6817-6828页

作者： Aron, Rajni Aggarwal, Deepak. K. NMIMS Mukesh Patel Sch Technol Management & Engn Dept Data Sci Mumbai Maharashtra India Concordia Univ Montreal PQ Canada

IoT presently being one of the most pertinent application domains for cloud computing. However, even though most of the traditional PaaS solutions such as Google App Engine, Microsoft Azure, Cloud Foundry enable provisioning and scheduling of IoT applications, but all of them do not support the provisioning of concurrent applications efficiently. The main reason not to support the execution of the concurrent applications is the absence of direct pipe-lining between two applications processes. There is a need to design a resource scheduling algorithm to achieve the target of the completion of the application's execution with the external domain process. In this paper, we presented an IoT-based PaaS architecture and cuckoo search based resource scheduling algorithm for concurrent applications. The proposed approach caters the communication interface among processes by uniquely allocating network interface to a particular container. An implementation of the proposed approach is also demonstrated. The performance of the proposed algorithm is evaluated in the simulation environment. Cuckoo search based algorithm outperformed in comparison to the existed algorithms, as shown in the performance evaluation section.

关键词： Cloud computing Concurrent applications Resource provisioning Internet of Things Platform as a Service scheduling algorithms

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Iterative Widen Heuristic Beam Search Algorithm for scheduling Problem of Flexible Assembly Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第11期17卷 7348-7358页

作者： Li, Xiaoling Xing, Keyi Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Syst Engn Inst Xian 710049 Peoples R China

scheduling of assembly manufacturing systems has received much attention in recent years, but the scheduling problem of deadlock-prone flexible assembly systems (FASs) is rarely studied. Based on the Petri net models of FASs, this article studies the scheduling problem of deadlock-prone FASs, and the heuristic beam search (HBS) algorithm is proposed to minimize the makespan. In HBS, the state space of the FAS is searched level by level, and two types of evaluation functions are proposed to ensure that only the most promising states are explored. To find a better solution in an acceptable time, the HBS algorithm is integrated with the iterative policy, and the iterative widen heuristic beam search (IWHBS) algorithm is obtained. Experimental and comparison results validate the effectiveness and superiority of the IWHBS algorithm over other heuristic search algorithms.

关键词： System recovery Job shop scheduling scheduling algorithms Manufacturing systems Informatics Search problems Process control Flexible assembly system (FAS) heuristic beam search (HBS) Petri net (PN) scheduling

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G-PASTA: GPU-Accelerated Partitioning Algorithm for Static Timing Analysis 24

G-PASTA: GPU-Accelerated Partitioning Algorithm for Static T...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Zhang, Boyang Lin, Dian-Lun Chang, Che Chiu, Cheng-Hsiang Wang, Bojue Lee, Wan-Luan Chang, Chih-Chun Fang, Donghao Huang, Tsung-Wei University of Wisconsin-Madison United States Rutgers University United States Texas A&m University United States

ISBN: (纸本)9798400706011

Recent static timing analysis (STA) engines have leveraged task dependency graph (TDG) parallelism to accelerate various STA algorithms, including graph-based analysis and path-based analysis. Despite the promising speedup via task parallelism, the scheduling cost of a TDG has become dominant when handling large TDGs. To overcome this challenge, we propose G-PASTA, a simple and fast TDG partitioning algorithm to reduce the scheduling cost of large task-parallel STA algorithms. By harnessing the power of GPU computing, G-PASTA incurs minimal cost of partitioning while bringing significant runtime improvement to task-parallel STA algorithms. Compared to a state-of-the-art CPU-based TDG partitioner, G-PASTA is up to 41.8× faster in partitioning runtime and can improve the overall STA performance by 43% on large designs. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： scheduling algorithms

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Feeder Link Handover Strategy Based on Hybrid Clonal Selection Algorithm in the Dense LEO Constellation 14th

Feeder Link Handover Strategy Based on Hybrid Clonal Selecti...

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Yu, Tianqi Zhang, Qiang Meng, Weixiao Chen, Shuyi Godoy, Sebastián E. Saavedra, Gabriel School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Beijing Institute of Spacecraft System Engineering Beijing China Department of Electrical Engineering Universidad de Concepcón Concepcón Chile

ISBN: (纸本)9783031862021

In order to extend the connection time of the feeder link for dense LEO constellation and make full use of the resources of the gateway station, this article proposes a feeder link handover strategy for dense LEO constellation based on Hybrid Clonal Selection Algorithm. Firstly, the satellite-earth visible window is defined as an independent task, the link handover problem is transformed into a task assignment problem, and the corresponding mathematical model is established. Then, the task execution time window is calculated using the Conflict Resolution Algorithm, and the associated satellite coincidence time window is reassigned. Then, the Clonal Selection Algorithm is used for global search to complete the whole link handover. In order to enhance the local optimization ability of the algorithm, a Hybrid Clonal Selection Algorithm is proposed by combining the local search algorithm of adaptive neighborhood selection. Finally, the model is set up and the performance comparative analysis experiment is carried out. The experimental results show that the Hybrid Clonal Selection Algorithm can improve the task scheduling rate and extend the link connection time without sacrificing the link switching times. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： scheduling algorithms

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Learning-Augmented Energy-Aware List scheduling for Precedence-Constrained Tasks

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ACM TRANSACTIONS ON MODELING AND PERFORMANCE EVALUATION OF COMPUTING SYSTEMS 2024年第4期9卷 1-24页

作者： Su, Yu Anand, Vivek Yu, Jiannie Tan, Jian Wierman, Adam CALTECH Pasadena CA 91125 USA Georgia Inst Technol Atlanta GA USA Alibaba Inc Sunnyvale CA USA

We study the problem of scheduling precedence-constrained tasks to balance between performance and energy consumption. We consider a system with multiple servers capable of speed scaling and seek to schedule precedence-constrained tasks to minimize a linear combination of performance and energy consumption. Inspired by the single-server setting, we propose the concept of pseudo-size for individual tasks, which is a measure of the externalities of a task in the precedence graph and is learned from historical workload data. We then propose a two-stage scheduling framework that uses a learned pseudo-size approximation and achieves a provable approximation bound on the linear combination of performance and energy consumption for both makespan and total weighted completion time, where the quality of the bound depends on the approximation quality of pseudo-sizes. We show experimentally that learning-based approaches consistently perform near optimally.

关键词： Theory of computation scheduling algorithms Computer systems organization Cloud computing

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Network Policy Enforcement With Commodity Multiqueue NICs for Multitenant Data Centers

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IEEE INTERNET OF THINGS JOURNAL 2022年第8期9卷 6252-6263页

作者： Kim, Gyuyeong Lee, Wonjun Korea Univ Sch Cybersecur Network & Secur Res Lab Seoul 02841 South Korea

Data centers are the fundamental component in the Internet of Things (IoT) system architecture. Data center servers where IoT services are co-located require hierarchical network policy enforcement to ensure fair bandwidth sharing among tenants and to prioritize latency-sensitive traffic within a tenant simultaneously. Meanwhile, emerging network interface cards (NICs) in servers make use of multiple hardware queues to drive increasing line rates. Unfortunately, multiqueue NICs make it hard to enforce hierarchical policies because the NIC packet scheduler dequeues packets in a static round-robin (RR) fashion for per-flow fairness. In this article, we enable hierarchical network policy enforcement with existing commodity multiqueue NICs. We design TONIC, a multiqueue NIC packet scheduling solution that approximates hierarchical packet scheduling by manipulating the packet dequeueing sequence of the NIC scheduler through dynamic packet enqueueing decisions. Specifically, TONIC leverages multiple hardware queues and the double-ended queue structure of qdiscs to express different tenant weights and application priorities without hardware modifications. We implement a TONIC prototype as a Linux kernel module and evaluate it on a testbed with commodity multiqueue NICs. Our experiment results show that TONIC can enforce hierarchical policies consisting of weighted fair sharing and traffic prioritization while maintaining robustness to various network conditions.

关键词： Hardware Internet of Things Servers scheduling algorithms Data centers Throughput Linux Data center networks Internet of Things (IoT) system architecture network interface cards (NICs)

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Minimizing Average Job Completion Time for Multi-resource Queuing Computing Systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon EC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Multiple Wirelessly Powered Sensing Platform scheduling Algorithm Based on Dynamic Priority Preemption

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JOURNAL OF SENSORS 2021年第1期2021卷

作者： Xie, Zhijun Zhang, Tao Wang, Chenlu Yu, Jiancheng Zarei, Roozbeh Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Inst Mass Spectrometry Technol & Applicat Ningbo 315211 Peoples R China Ningbo Univ Sch Mat Sci & Chem Engn Ningbo 315211 Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia

The available energy of a wirelessly powered sensing platform is not enough, and there are constant real-time tasks to join the wirelessly powered sensing platform to run. So the wirelessly powered sensing system composed of many wirelessly powered sensing platforms is easy to enter the overloaded state, which may cause some tasks not to be executed on time. Therefore, to obtain as much task value as possible for the wirelessly powered sensing system when it is under the overloaded state, it is essential to design a reasonable task scheduling algorithm to arrange the task execution order. In this paper, we propose a policy named Wirelessly Dynamic Allocation Priority (WDAP) policy suitable for the wirelessly powered sensing system. The proposed WDAP is divided into a dynamic task priority allocation policy and a dynamic node priority allocation policy. Firstly, this paper analyzes the dynamic value density based on task value and execution time, studies the urgency of execution according to the execution time and the remaining idle time, and proposes the energy intensity through the task energy consumption and execution time. Based on the three impact factors of dynamic value density, urgency, and energy intensity, a policy for dynamic task priority allocation is proposed. Then, a policy for dynamic node priority allocation is proposed by combining the available energy and the energy acquisition speed of the nodes. Finally, the algorithm suitable for the wirelessly powered sensing system is proposed named Wirelessly Dynamic Real-time Task scheduling (WDRTS) algorithm based on the WDAP. The algorithm clarifies the execution order of each task, responds to high-priority tasks first, and effectively guarantees task benefits. The experimental results show that compared with the main algorithms used in the literature among which is Generalized Earliest Deadline First, the WDRTS algorithm reduces the number of preemptive tasks by at least 36.49% and increases the successf

关键词： scheduling algorithms

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Federated Imitation Learning: A Cross-Domain Knowledge Sharing Framework for Traffic scheduling in 6G Ubiquitous IoT

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IEEE NETWORK 2021年第5期35卷 136-142页

作者： Yu, Ao Yang, Qingkai Dou, Lihua Cheriet, Mohamed Kuaishou Technol Co Beijing Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China Beijing Inst Technol Control Sci Arid Engn Beijing Peoples R China Ecole Technol Super Montreal PQ Canada

The ubiquitous Internet of Things (IoT) system is a key component of future 6G networks to realize a fully connected world. Extensive efforts have been made to provide on-demand traffic scheduling in IoT networks through machine learning algorithms. However, the current learning approaches are hindered by the heterogeneous information in ubiquitous IoT systems since the data are collected from different domains (e.g., space, air, ground, and ocean). To uncover the complete picture of ubiquitous IoT, this article presents a novel federated imitation learning framework for traffic prediction without compromising privacy. This framework contains a knowledge-sharing module to imitate the status of cross-domain models. After that, we design a distributed resource allocation algorithm, where the IoT devices cooperatively make association decisions using matching theory. Simulation results reveal that our proposed approach outperforms state-of-the-art federated transfer learning and achieves desirable traffic scheduling performance in a cross-domain environment.

关键词： 6G mobile communication Privacy Ubiquitous computing scheduling algorithms Simulation Transfer learning Prediction algorithms Internet of Things Resource management Collaborative work

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A new approach to solving target coverage problem in wireless sensor networks using an effective hybrid genetic algorithm and tabu search

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 6245-6255页

作者： Ajam, Leila Nodehi, Ali Mohamadi, Hosein Islamic Azad Univ Dept Comp Engn Gorgan Branch Gorgan Golestan Iran Islamic Azad Univ Dept Comp Engn Azadshar Branch Azadshar Iran

Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime.

关键词： Wireless sensor networks cover set formation scheduling algorithms genetic algorithm Tabu search

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