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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Medishetti, Santhosh Kumar Tamma, Charan Reddy Venigalla, Rakesh Meka, Pranideep Reddy Addepalli, Phani Vishnu Shamsheer, Shafreen VIT-AP University Amaravathi India Nalla Narasimha Reddy Education Society's Group of Institution India George Mason University VA United States George Washington University VA United States

ISBN: (纸本)9798350368413

The Butterfly Lion Optimization Algorithm (BLOA) is proposed as an innovative hybrid metaheuristic approach for Task scheduling (TS) in Cloud Computing (CC) environments. Proposed algorithm is combination of mating and foraging behaviour of Butterfly Optimization Algorithm (BOA) and hunting strategy of Lion Optimization Algorithm (LOA). TS is a critical challenge in CC, where the goal is to efficiently allocate resources while optimizing multiple objectives such as minimizing makespan, reducing execution time and response time while maximizing the throughput. The BLOA combines the dynamic exploration capabilities of butterfly behavior with the strategic hunting techniques of lions, creating a robust algorithm that balances global exploration and local exploitation. This hybrid algorithm leverages the strengths of both butterflies and lions to navigate complex, high-dimensional search spaces typical of CC environments. By integrating adaptive mechanisms, the BLOA is capable of dynamically adjusting to varying workload demands and resource availability. Comparative evaluations against other leading scheduling algorithms demonstrate that BLOA achieves superior performance in terms of reducing makespan time by 18.15%, execution time by 11.23%, and response time by 14.78%, while maximizing the throughput by 21.11%. The algorithm is especially useful for CC environments that are heterogeneous and distributed, which makes it ideal for cloud service providers who intend to improve resource utilization as well as decrease energy consumption and operational costs. © 2024 IEEE.

关键词： scheduling algorithms

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Realization of Computer Course scheduling Algorithm in Higher Vocational Colleges Based on ML 3rd

Realization of Computer Course Scheduling Algorithm in Highe...

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3rd EAI International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wu, Ping Heilongjiang Polytechnic Heilongjiang Harbin150025 China Chongqing University of Arts and Sciences Chongqing402160 China

ISBN: (纸本)9783031631382

Today, with the rapid development of message technique, all higher vocational colleges have established perfect message management systems, among which educational administration management system is the core of the whole message system. In the educational administration, course arrangement is the first step to ensure the teaching quality. It arranges the time and place for the courses set by the school, so that the whole teaching can be carried out in a planned and orderly way. With the continuous deepening of education reform, the reform of course scheduling is also underway, arranging more scientific and humanized school courses, and the individual needs and individual development of students and teachers are also the focus of current curriculum arrangements. Implementing the comprehensive management of school teaching through computer teaching management system is an effective means to improve the level of school teaching management and promote the progress of school management. This paper uses ML algorithm to establish computer course scheduling algorithm. ML (machine learning) uses computer simulation technique to study the learning process of human knowledge acquisition, and from it, it innovates existing knowledge, promotes analysis and solves problems. Through the research of this paper, it is concluded that the algorithm in this paper has remarkable effect and is suitable for being widely used. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： scheduling algorithms

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Research and Validation of scheduling Mechanism Based on Smart IoT Meter Operating System 4

Research and Validation of Scheduling Mechanism Based on Sma...

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4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Jiang, Fan Liang, Zhaoqing Xu, Jing Bai, Xuesong Fu, Lili Wang, Zhiqiang Qu, Shengbo Shi, Yujie Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing China State Grid Corporation of China Beijing China

ISBN: (纸本)9798350359589

The purpose of this paper is to carry out an in-depth study on the scheduling mechanism based on the operating system of smart IoT meter and prove its effectiveness through experiments. Firstly, the characteristics and demands of smart IoT meter are analyzed, and the problems of traditional scheduling mechanism are discussed. Second, a scheduling mechanism based on smart IoT operating system is proposed to achieve efficient scheduling of smart IoT through reasonable task allocation and optimization algorithm. Finally, the performance data of the proposed scheduling mechanism is experimentally derived and analyzed in comparison with the traditional scheduling mechanism. The experimental results show that the designed scheduling mechanism of the smart IoT meter operating system meets the real-time requirements in terms of function and performance. Meanwhile, this study will also provide useful references for academic research and practical applications in the field of smart measurement device operating system. © 2024 IEEE.

关键词： operating system priority scheduling algorithms smart IoT meter time slices

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SLA-Based scheduling of Spark Jobs in Hybrid Cloud Computing Environments

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IEEE TRANSACTIONS ON COMPUTERS 2022年第5期71卷 1117-1132页

作者： Islam, Muhammed Tawfiqul Wu, Huaming Karunasekera, Shanika Buyya, Rajkumar Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Parkville Vic 3010 Australia Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China

Big data frameworks such as Apache Spark is becoming prominent to perform large-scale data analytics jobs in various domains. However, due to limited resource availability, the local or on-premise computing resources are often not sufficient to run these jobs. Therefore, public cloud resources can be hired on a pay-per-use basis from the cloud service providers to deploy a Spark cluster entirely on the cloud. Nevertheless, using only cloud resources can be costly. Hence, both local and cloud resources nowadays are used together to deploy a hybrid cloud computing cluster. However, scheduling jobs in a cluster deployed on hybrid clouds is challenging in the presence of various Service-Level Agreement (SLA) demands such as cost minimization and job deadline guarantee. Most of the existing works either consider a public or a locally deployed cluster and mainly focus on improving job performance in the cluster. In this article, we propose efficient scheduling algorithms that leverage from different VM instance pricing in a hybrid cloud deployed cluster to optimize the Virtual Machine (VM) usage cost for both local and cloud resources and maximize the job deadline met percentage. We have conducted extensive simulation-based experiments to compare our proposed algorithms with the baseline approaches. In addition, we have developed a prototype system on top of Apache Mesos cluster manager and performed real experiments to evaluate the applicability of our proposed approaches in a real platform with benchmark applications. The results show that our proposed algorithms are highly scalable and reduce the cost of VM usage of a hybrid cluster for up to 20 percent.

关键词： Cloud computing Sparks Clustering algorithms Big Data Task analysis scheduling algorithms Cluster computing Spark hybrid cloud cluster-scheduling SLA big data applications deadline cost-minimization

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A Hybrid Fault-Tolerant scheduling for Deadline-Constrained Tasks in Cloud Systems

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第3期15卷 1371-1384页

作者： Yao, Guangshun Ren, Qian Li, Xiaoping Zhao, Shenghui Ruiz, Ruben Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China Chuzhou Univ Coll Comp & Informat Engn Chuzhou 239000 Anhui Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 211189 Jiangsu Peoples R China Univ Politecn Valencia Grp Sistemas Optimizat Aplicada Camino Vera S-N Valencia 46022 Spain

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors.

关键词： Task analysis Fault tolerance Fault tolerant systems Cloud computing Resource management Heuristic algorithms scheduling algorithms Cloud systems deadline fault-tolerant resubmission replication

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scheduling Dependent Batching Tasks 23

Scheduling Dependent Batching Tasks

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52nd International Conference on Parallel Processing (ICPP)

作者： Shi, Hehuan Chen, Lin Lin, Ming Phan, Raphael C-W Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Monash Univ Sch Informat Technol Selangor Malaysia

ISBN: (纸本)9798400708435

We formulate and analyze the following problem of scheduling dependent batching tasks on a common resource. Each task is associated with an execution time window. Some can be executed simultaneously in batch, while others need exclusively use of the resource. A task may depend on a set of tasks such that it becomes executable only if all its ancestor tasks are completed. We look for a task scheduling policy maximizing the system reward. We investigate both the offline and online settings by focusing on a typical scenario where the task dependency forms a tree or a forest. In both settings, we formally establish the hardness of the scheduling problem by showing that the offline scheduling is NP-hard and the online counterpart admits no scheduling policy with finite competitive ratio. We then develop approximation scheduling algorithms for both cases with deterministic worst-case performance guarantee in terms of system utility. We further conduct numeric experiments to evaluate our algorithms under a variety of parameter settings to demonstrate the effectiveness of our scheduling algorithms.

关键词： scheduling algorithms

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Job scheduling for big data analytical applications in clouds: A taxonomy study

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 129-145页

作者： Kang, Youyou Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan Peoples R China

Cloud environments have provided great help for the development of big data. Today, cloud environments have become the most active platform for big data analysis applications (BDAAs). Cloud products provide convenience for BDAA providers to provision big data analysis services to their users at a more cost-effective way. Rational utilization of cloud resources can bring higher efficiency and lower cost to BDAAs. Therefore, how to better process big data jobs in cloud environments has become a hot research topic in recent years. Efficient job scheduling schemes can make full use of cloud resources, and then achieve different goals, such as saving energy, minimizing cost and maximizing job execution efficiency. So designing suitable scheduling schemes for different problem scenarios has become the most common and effective method to optimize the execution of big data analysis jobs. To explore practical and efficient job scheduling algorithms in different scenarios, references of existing scheduling algorithms are often needed. Therefore, it is necessary to survey the research work of BDAA job scheduling in cloud environments in recent years, which can provide detailed guidance and help for future research in this field. This paper summarizes the work related to BDAA job scheduling in cloud environments, selects articles related to the topic of this survey from conferences and journals from 2016 to 2021, and divides scheduling algorithms with different research focuses into several categories. On this basis, the advantages and disadvantages of existing research work are compared, and the directions and challenges for future research are pointed out. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Systematic review Survey Big data Cloud computing Job scheduling scheduling algorithms

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Minimizing the Age-of-Critical-Information: An Imitation Learning-Based scheduling Approach Under Partial Observations

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第9期21卷 3225-3238页

作者： Wang, Xiaojie Ning, Zhaolong Guo, Song Wen, Miaowen Poor, H. Vincent Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users' decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

关键词： Roads Servers Heuristic algorithms scheduling algorithms Mobile computing Wireless networks Dynamic scheduling Age of information imitation learning mobile edge networks scheduling policy critical levels

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Structure-Free Broadcast scheduling for Duty-Cycled Multihop Wireless Sensor Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第12期21卷 4624-4641页

作者： Chen, Quan Cai, Zhipeng Cheng, Lianglun Gao, Hong Li, Jianzhong Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Broadcasting is an essential operation in wireless networks for disseminating the message from the source node to all other nodes. Unfortunately, the problem of Minimum Latency Broadcast scheduling (MLBS) in duty-cycled wireless sensor networks is not well studied. In existing works, the construction of broadcast tree and the scheduling of transmissions are conducted separately, where a tree-based structure is used as the input of the scheduling algorithm. Relying on a pre-determined tree may result in a much large latency even using the optimal scheduling method. Thus, the MLBS problem in duty-cycled WSNs without the above limitation is investigated in this paper. First, to avoid relying on a pre-determined structure, a two-step scheduling algorithm is proposed to construct the broadcast tree and compute a collision-free schedule simultaneously. To the best of our knowledge, this is the first work that can integrate these two kinds of operations together. Second, a novel transmission mode, i.e., concurrent broadcasting, is first introduced for wireless networks and several techniques are designed to further improve the broadcast latency. Third, the multiple messages broadcasting and all-to-all broadcasting algorithms, which can generate a series of broadcast schedules independently without a pre-determined tree, are also proposed by taking care of the collisions in both the current and the previous broadcast schedules. Finally, the theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in terms of latency.

关键词： Schedules Broadcasting Wireless sensor networks Optimal scheduling Approximation algorithms scheduling algorithms Electronic mail Broadcast scheduling minimum latency structure-free duty-cycled wireless sensor networks

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An Optimized Collaborative scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

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MOBILE NETWORKS & APPLICATIONS 2022年第4期27卷 1444-1460页

作者： Amer, Amira A. Talkhan, Ihab E. Ahmed, Reem Ismail, Tawfik Nile Univ Wireless Intelligent Networks Ctr WINC Giza 12677 Egypt Cairo Univ Fac Engn Dept Comp Engn Giza 12613 Egypt Cairo Univ Fac Sci Dept Math Giza 12613 Egypt Cairo Univ Natl Inst Laser Enhanced Sci Giza 12613 Egypt

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

关键词： Mobile edge computing scheduling algorithms Quality of service Power consumption Optimization Cloud computing

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