Journal articles: 'Heterogenous scheduling'

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Patil, Shailesh. "Measurement-Based Opportunistic Scheduling for Heterogenous Wireless Systems." IEEE Transactions on Communications 57, no. 9 (September 2009): 2745–53. http://dx.doi.org/10.1109/tcomm.2009.09.0800902.

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Al-Saqabi, Khaled, Syed Sarwar, and Kassem Saleh. "Distributed gang scheduling in networks of heterogenous workstations." Computer Communications 20, no. 5 (July 1997): 338–48. http://dx.doi.org/10.1016/s0140-3664(97)00020-0.

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Sun, Donglai, Yang Liu, Jianhua Li, and Yue Wu. "Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/414689.

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We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations.

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Ponsy. "Balanced Scheduling of Independent File-Sharing Tasks in Heterogenous Environment." Journal of Computer Science 7, no. 12 (December 1, 2011): 1793–97. http://dx.doi.org/10.3844/jcssp.2011.1793.1797.

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Lai, Shouwen, Binoy Ravindran, and Hyeonjoong Cho. "Heterogenous Quorum-Based Wake-Up Scheduling in Wireless Sensor Networks." IEEE Transactions on Computers 59, no. 11 (November 2010): 1562–75. http://dx.doi.org/10.1109/tc.2010.20.

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Chraibi, Amine, Said Ben Alla, and Abdellah Ezzati. "An efficient cloudlet scheduling via bin packing in cloud computing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 3226. http://dx.doi.org/10.11591/ijece.v12i3.pp3226-3237.

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In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).

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Incrocci, Luca, Paolo Marzialetti, Giorgio Incrocci, Andrea Di Vita, Jos Balendonck, Carlo Bibbiani, Serafino Spagnol, and Alberto Pardossi. "Substrate water status and evapotranspiration irrigation scheduling in heterogenous container nursery crops." Agricultural Water Management 131 (January 2014): 30–40. http://dx.doi.org/10.1016/j.agwat.2013.09.004.

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Hamdi, S., E. Bouazizi, and S. Faiz. "QOS MANAGEMENT IN REAL-TIME SPATIAL BIG DATA USING FEEDBACK CONTROL SCHEDULING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 243–48. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-243-2015.

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Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).

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Kaur, Sumanpreet, and Mr Navtej Singh Ghumman. "ALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 3 (June 5, 2017): 6240–46. http://dx.doi.org/10.24297/ijct.v16i3.6177.

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Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

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Lin, Na, Huimin Yang, Ya Li, and Xuping Wang. "Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search." Journal of Physics: Conference Series 2425, no. 1 (February 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2425/1/012006.

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Abstract This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.

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Van der Hem, Klaas G., Gerrit Jan Schuurhuis, Angelika M. Dräger, Joan H. Odding, and Peter C. Huijgens. "Heterogenous effects of bryostatin on human myeloid leukemia clonogenicity: Dose and time scheduling dependency." Leukemia Research 20, no. 9 (September 1996): 743–50. http://dx.doi.org/10.1016/0145-2126(96)00031-8.

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Scherr, Simon André, Steffen Hupp, and Frank Elberzhager. "Establishing Continuous App Improvement by Considering Heterogenous Data Sources." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 10 (May 25, 2021): 66. http://dx.doi.org/10.3991/ijim.v15i10.20613.

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Mobile apps have penetrated the market and are being used everywhere. Companies developing apps face increasing challenges such as short time to market or demand for high quality. Furthermore, the success of an app also depends on how users perceive its quality. Feedback provided by users influences other potential users and provides new opportunities for identifying features. Consequently, it is a valuable source of input for app developers with respect to product improvements. One form is textual feedback. This kind of feedback is usually distributed across various data sources. Therefore, it must be captured from these sources and put into one single pool of data before it can be analyzed. The analysis must take into account the peculiarities of the short release cycles and high change rate of features for mobile apps. In this article, we present User Echo Service (UES), which was built to address heterogeneous data sources. The aim of UES is to allow product managers to be able to be always up to date with the latest feedback data. Therefore, we have created an extensible architecture aimed at supporting different data sources and present the feedback collection scheduling system. This forms the prerequisite for subsequent analyses of the collected data. We discuss our solution and provide ideas for future development.

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Hossain, Md, Mohamed-Slim Alouini, and Vijay Bhargava. "Two-user opportunistic scheduling using hierarchical modulations in wireless networks with heterogenous average link gains." IEEE Transactions on Communications 58, no. 3 (March 2010): 880–89. http://dx.doi.org/10.1109/tcomm.2010.03.080238.

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Kim, Su Min, Bang Chul Jung, Wan Choi, and Dan Keun Sung. "Effects of Heterogenous Mobility on Rate Adaptation and User Scheduling in Cellular Networks With HARQ." IEEE Transactions on Vehicular Technology 62, no. 6 (July 2013): 2735–48. http://dx.doi.org/10.1109/tvt.2013.2250533.

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Nevrlý, Vlastimír, Radovan Šomplák, and Pavel Popela. "Heuristics for Waste Collection Arc Routing Problem." MENDEL 25, no. 1 (June 24, 2019): 15–22. http://dx.doi.org/10.13164/mendel.2019.1.015.

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Waste management is still an expanding eld which needs to be constantly enhanced so that waste transportation and treatment is as eective as possible. An important part of this process is a waste collection at the municipal level. Decision-making about daily routing for all vehicles from a heterogenous eet substantially in uences the expenses of technical services. The need of route scheduling comes also from the newly separated fractions. Transportation features include the capacity of vehicles, number and type of containers on the route, traffic light delays and many others. The mathematical model that properly describes the real practice of servicing containers has not been published yet. Moreover, routing problemsare generally not solvable by exact methods, so the appropriate heuristic algorithm has been developed. A case study with obtained results is discussed. This solution serves not only to improve the current operational situation, but also to create new route schedules for increasing number of collected commodities.

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Wang, Jingjing, Hongji Meng, Jian Yang, and Zhi Xie. "GPU-Based Cellular Automata Model for Multi-Orient Dendrite Growth and the Application on Binary Alloy." Crystals 13, no. 1 (January 6, 2023): 105. http://dx.doi.org/10.3390/cryst13010105.

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To simulate dendrite growth with different orientations more efficiently, a high-performance cellular automata (CA) model based on heterogenous central processing unit (CPU)+ graphics processing unit (GPU) architecture has been proposed in this paper. Firstly, the decentered square algorithm (DCSA) is used to simulate the morphology of dendrite with different orientations. Secondly, parallel algorithms are proposed to take full advantage of many cores by maximizing computational parallelism. Thirdly, in order to further improve the calculation efficiency, the task scheduling scheme using multi-stream is designed to solve the waiting problem among independent tasks, improving task parallelism. Then, the present model was validated by comparing its steady dendrite tip velocity with the Lipton–Glicksman–Kurz (LGK) analytical model, which shows great agreement. Finally, it is applied to simulate the dendrite growth of the binary alloy, which proves that the present model can not only simulate the clear dendrite morphology with different orientations and secondary arms, but also show a good agreement with the in situ experiment. In addition, compared with the traditional CPU model, the speedup of this model is up to 158×, which provides a great acceleration.

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Morici, Kat E., and John D. Bailey. "Long-Term Effects of Fuel Reduction Treatments on Surface Fuel Loading in the Blue Mountains of Oregon." Forests 12, no. 10 (September 25, 2021): 1306. http://dx.doi.org/10.3390/f12101306.

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Fire exclusion and a lengthening fire season has resulted in an era of megafires. Fuel reduction treatments in forested ecosystems are designed to guard against future extreme wildfire behavior. Treatments create a heterogenous landscape and facilitate ecosystem function and resilience in fire-adapted forests of the western United States. Despite widespread recognition that repeated fuel treatments are needed to maintain desired stand characteristics over time, few field studies have evaluated treatment longevity. The Blue Mountains Fire and Fire Surrogate site in northeastern Oregon presented an opportunity to investigate woody fuel loading 15–17 years after four treatments: mechanical thin, prescribed burn, both thin and burn, and no treatment control. The principal findings were: (1) fine fuel load 15 years post-burn remained slightly below pre-treatment values; (2) rotten coarse fuel load was reduced post-burn, but sound coarse fuel was not altered by any active treatment; and (3) total woody fuel load 15–17 years post-treatment was similar to pre-treatment values. Understanding surface fuel loading is essential for predicting fire behavior. Overall, the effects of fuel reduction treatments on woody surface fuels were transitory in dry mixed conifer forests. Frequent maintenance treatments are recommended to protect values at risk in areas with high fire hazards. Quantifying the persistence of changes in forest conditions aids in the planning and analysis of future fuel treatments, along with scheduling maintenance of existing treated areas.

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Chu, Myeonghun, Sangmi Moon, Hun Choe, and Intae Hwang. "Performance Analysis of CoMP Using Scheduling and Precoding Techniques in the Heterogeneous Network." International Journal of Information and Electronics Engineering 6, no. 3 (2016): 155–60. http://dx.doi.org/10.18178/ijiee.2016.6.3.615.

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Sukhija, Nitin, Brandon Malone, Srishti Srivastava, Ioana Banicescu, and Florina M. Ciorba. "A Learning-based Selection for Portfolio Scheduling of Scientific Applications on Heterogeneous Computing Systems." Parallel & Cloud Computing 3, no. 4 (October 31, 2014): 66–81. http://dx.doi.org/10.14511/pcc.2014.030401.

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Yang, Bomi, Hyunjae Park, and Young-June Choi. "Sojourn Time Analysis Using SRPT Scheduling for Heterogeneous Multi-core Systems." Journal of KIISE 44, no. 3 (March 15, 2017): 223–31. http://dx.doi.org/10.5626/jok.2017.44.3.223.

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Marson, Justin, Graham Litchman, and Darrell Rigel. "Differential Impact of COVID-19 on Urban Versus Rural Dermatologic Practice Logistics and Recovery: A Cross-Sectional Investigation of the First Wave." SKIN The Journal of Cutaneous Medicine 5, no. 2 (March 6, 2021): 118–24. http://dx.doi.org/10.25251/skin.5.2.6.

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Background: COVID-19 materially delayed patient visits and potential skin cancer biopsies/diagnoses among US dermatology practices. However, given a likely heterogenous impact across the US, this study sought to determine COVID-19’s effect on urban versus rural dermatology practices. Methods: Data were analyzed from the first 1000 responses to 3 pre-validated surveys of 9891 practicing US dermatologists comparing outpatient volumes and scheduling issues for the week of February 17th to the week of March 16th (Survey 1), April 13th (Survey 2) and May 18th, 2020 (Survey 3). First 3 US zip-code digits were compared to US Census Bureau data to determine “Urban/Rural” status. Representativeness with AAD membership was confirmed. Statistical significance was calculated using chi-square with Marascuilo procedure and two-tailed independent t-test/ANOVA with post-hoc Tukey-Kramer testing. Results: In April 2020 urban practices reported more closed practices (21.4% vs 5.8%, p<0.0001) and predicted significantly larger patient volume decreases (-45.2% vs -31.4%, p<0.0001) and practice closures (11.9% vs. 2.5% p<0.0001) in the following 2 weeks. In May 2020, urban areas saw significantly fewer patients/week (90.9 vs 142.4 p<0.0001), larger decrease in patient volume relative to May 2019 (-49.4% vs -35.1%, p<0.0001), and conducted more telemedicine visits (27.0% vs 15.1%, p<0.0001). Significantly more rural practices reported already being at baseline volume (Mean Difference 6.2%, 95% CI 2.7%-9.8%) while urban practices predicted return to baseline volume by August (5.7, 95% CI 2.1%-9.3%) or were unsure (5.6, 95% CI 1.6%-9.7%). Conclusion: The initial COVID-19 pandemic differentially affected urban dermatology practices. The effects of the pandemic were mitigated in part by increased use telemedicine. Future studies may further elucidate COVID-19’s effect on clinical practice and highlight areas for improvement in practice logistics and patient care.

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Wang, Guan, Yuxin Wang, Hui Liu, and He Guo. "HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3676149.

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High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more reasonable; (2) an entry task duplication selection policy to make the makespan shorter; and (3) an improved idle time slots (ITS) insertion-based optimizing policy to make the task scheduling more efficient. We evaluate our proposed algorithm on randomly generated DAGs, using some real application DAGs by comparison with some classical scheduling algorithms. According to the experimental results, our proposed algorithm appears to perform better than other algorithms in terms of schedule length ratio, efficiency, and frequency of best results.

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Ghose, Anirban, Lokesh Dokara, Soumyajit Dey, and Pabitra Mitra. "A Framework for OpenCL Task Scheduling on Heterogeneous Multicores." Parallel Processing Letters 27, no. 03n04 (December 2017): 1750008. http://dx.doi.org/10.1142/s0129626417500086.

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We present an intelligent scheduling framework which takes as input a set of OpenCL kernels and distributes the workload across multiple CPUs and GPUs in a heterogeneous multicore platform. The framework relies on a Machine Learning (ML) based frontend that analyzes static program features of OpenCL kernels and predicts the ratio in which kernels are to be distributed across CPUs and GPUs. The framework provides such static analysis information along with system state information like runtime availability details of computing cores using well defined programming interfaces. Such interfaces are to be utilized by a user specified scheduling strategy. Given such a scheduling strategy, the framework generates device specific binaries and dispatches them across multiple devices in the heterogeneous platform as per the strategy. We test our scheduling framework extensively using different OpenCL task mixes of varying sizes and computational nature. Along with the scheduling framework, we propose a set of novel partition-aware scheduling strategies for heterogeneous multicores. Our proposed approach yields considerably better results in terms of schedule makespan when compared with the current state of the art ML based methods for scheduling of OpenCL workloads across heterogeneous multicores.

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Wang, Zhangquan, Yourong Chen, Banteng Liu, Haibo Yang, Ziyi Su, and Yunkai Zhu. "A sensor node scheduling algorithm for heterogeneous wireless sensor networks." International Journal of Distributed Sensor Networks 15, no. 1 (January 2019): 155014771982631. http://dx.doi.org/10.1177/1550147719826311.

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To improve the regional coverage rate and network lifetime of heterogeneous wireless sensor networks, a sensor node scheduling algorithm for heterogeneous wireless sensor networks is proposed. In sensor node scheduling algorithm, heterogeneous perception radius of sensor node is considered. Incomplete coverage constraint and arc coverage interval are analyzed. Regional coverage increment optimization model, arc coverage increment optimization model, and residual energy optimization model are proposed. Multi-objective scheduling model is established using weight factors and integrated function. Furthermore, the heuristic method is proposed to solve the multi-objective optimization model, and scheduling scheme of heterogeneous sensor nodes is obtained. When the network is in operation for a period of time, some sensor nodes are invalid and relevant regions are uncovered. The repair method is proposed to wake up sleep sensor nodes and repair the coverage blind area. The simulation results show that if keeping the same regional coverage rate, sensor node scheduling algorithm improves network lifetime, increases number of living sensor nodes, and keeps average node energy consumption at a low level. Under certain conditions, sensor node scheduling algorithm outperforms DGREEDY, two-tiered scheduling, and minimum connected cover.

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Wu, Dian Hong. "Task Optimization Scheduling Algorithm in Embedded System Based on Internet of Things." Applied Mechanics and Materials 513-517 (February 2014): 2398–402. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2398.

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Embedded system has been widely used in the network, server, etc., and it has a good application prospect with the development of Internet of things. In the embedded heterogeneous computing system, task scheduling is the key to deciding the system performance. For multi-task scheduling, the current scheduling algorithm is mostly based on task duplication, without a full consideration of the correlation between the predecessor task and its subsequent tasks. Based on modeling the multi-frame task scheduling problem in the heterogeneous embedded system, this paper analyzes the availability of tasks through the design of genetic algorithm, so as to verify the algorithm's feasibility, which is of important guiding significance for the multi-task scheduling in the embedded heterogeneous computing system.

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Stan, Roxana-Gabriela, Lidia Băjenaru, Cătălin Negru, and Florin Pop. "Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments." Sensors 21, no. 17 (September 2, 2021): 5906. http://dx.doi.org/10.3390/s21175906.

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This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

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Wang, Xingkai, Weimin Wu, and Zichao Xing. "Multi-decision points model to solve coupled-task scheduling problem with heterogeneous multi-AGV in manufacturing systems." International Journal of Industrial Engineering Computations 14, no. 1 (2023): 49–64. http://dx.doi.org/10.5267/j.ijiec.2022.10.003.

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Automated guided vehicle (AGV) is widely used in automated manufacturing systems as a material handling tool. Although the task scheduling problem with isomorphic AGV has remained a very active research field through the years, too little work has been devoted to the task scheduling problems with heterogeneous AGVs. A coupled task with heterogeneous AGVs is a complex task that needs the cooperation of more than one type of AGVs. In this paper, a manufacturing system with two types of AGVs and three types of tasks is studied. To solve the coupled task scheduling problem with heterogeneous AGVs in this manufacturing system, we introduce two new methods based on the established mathematical model, namely, the decoupled scheduling strategy and coupled scheduling strategy with multi-decision model. The decoupled scheduling strategy is widely used in coupled task scheduling problems. However, there are some situations that the decoupled scheduling strategy cannot solve the problem well. To overcome the problem, the multi-decision point model solves the coupled task scheduling problem without decomposition. In order to ensure the searching speed and searching accuracy, a novel hybrid heuristic algorithm based on simulated annealing algorithm and tabu search algorithm is developed. The simulation experiment results show the proposed coupled scheduling algorithm has priority in coupled task scheduling problems.

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Lu, You Wei, Zhen Zhen Xu, and Feng Xia. "Prediction-Based Independent Task Scheduling for Heterogeneous Distributed Computing Systems." Advanced Materials Research 457-458 (January 2012): 1039–46. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1039.

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Independent task scheduling algorithms in distributed computing systems deal with three main conflicting factors including load balance, task execution time and scheduling cost. In this paper, the problem of scheduling tasks arriving at a low rate and with long execution time in heterogeneous computing systems is studied, and a new scheduling algorithm based on prediction is proposed. This algorithm evaluates the utility of task scheduling based on statistics and prediction to solve the influence of heterogeneous computing systems. The experimental results reveal that the proposed algorithm adequately balances the conflicting factors, and thus performs better than some classical algorithms such as MCT and MET when the parameters are well selected.

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Li, Yaofang, and Bin Wu. "Software-Defined Heterogeneous Edge Computing Network Resource Scheduling Based on Reinforcement Learning." Applied Sciences 13, no. 1 (December 29, 2022): 426. http://dx.doi.org/10.3390/app13010426.

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With the rapid development of wireless networks, wireless edge computing networks have been widely considered. The heterogeneous characteristics of the 6G edge computing network bring new challenges to network resource scheduling. In this work, we consider a heterogeneous edge computing network with heterogeneous edge computing nodes and task requirements. We design a software-defined heterogeneous edge computing network architecture to separate the control layer and the data layer. According to different requirements, the tasks in heterogeneous edge computing networks are decomposed into multiple subtasks at the control layer, and the edge computing node alliance responding to the tasks is established to perform the decomposed subtasks. In order to optimize both network energy consumption and network load balancing, we model the resource scheduling problem as a Markov Decision Process (MDP), and design a Proximal Policy Optimization (PPO) resource scheduling algorithm based on deep reinforcement learning. Simulation analysis shows that the proposed PPO resource scheduling can achieve low energy consumption and ideal load balancing.

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Gupta, Anupam, Ravishankar Krishnaswamy, and Kirk Pruhs. "Nonclairvoyantly scheduling power-heterogeneous processors." Sustainable Computing: Informatics and Systems 1, no. 3 (September 2011): 248–55. http://dx.doi.org/10.1016/j.suscom.2011.05.007.

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Albers, Susanne, Evripidis Bampis, Dimitrios Letsios, Giorgio Lucarelli, and Richard Stotz. "Scheduling on power-heterogeneous processors." Information and Computation 257 (December 2017): 22–33. http://dx.doi.org/10.1016/j.ic.2017.09.013.

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Righter, Rhonda, and Susan XU. "Scheduling jobs on heterogeneous processors." Annals of Operations Research 29, no. 1 (December 1991): 587–601. http://dx.doi.org/10.1007/bf02283615.

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Xie, Hui, Li Wei, Dong Liu, and Luda Wang. "Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (May 11, 2020): 2051012. http://dx.doi.org/10.1142/s021800142051012x.

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Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

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Jamadagni, Nagendra Kumar, Aniruddh M, Dr Govinda Raju M, and Dr Usha Rani K. R. "Comparative Study of Heterogeneous Multicore Scheduling Algorithms on Media Codecs." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 18, 2021): 840–49. http://dx.doi.org/10.51201/jusst/21/05355.

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All modern-day computers and smartphones come with multi-core CPUs. The multicore architecture is generally heterogeneous in nature to maximize computational throughput. These multicore systems exploit thread-level parallelism to deliver higher performance, but they are limited by the requirement of good scheduling algorithms that maximize CPU utility and minimize wasted and idle cycles. With the rise in streaming services and multimedia capabilities of smartphones, it is necessary to have efficient heterogeneous cores which are capable of performing multimedia processing at a fast pace. It is also needed that they utilize efficient scheduling algorithms to achieve this task. This paper compares some heterogeneous multi-core scheduling algorithms available and determines which is the most optimal scheduling algorithm given various codecs.

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Zhai, Wenzheng, Yue-Li Hu, and Feng Ran. "CQPSO scheduling algorithm for heterogeneous multi-core DAG task model." Modern Physics Letters B 31, no. 19-21 (July 27, 2017): 1740050. http://dx.doi.org/10.1142/s0217984917400504.

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Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

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Liu, Peng Fei, and Shou Bin Dong. "Multi-Objective Scheduling for Parallel Jobs on Grid." Key Engineering Materials 439-440 (June 2010): 1281–86. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1281.

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Focused on the complexity of the parallel job scheduling on heterogeneous Grid, the paper proposes a multi-objective optimization based scheduling algorithm. The algorithm first splits the parallel job up into a series of independent processes with constraints, and then adopts particles to represent the mapping of job-resource. Multi-objective PSO is employed to simultaneously optimize the scheduling objectives of throughput and average turnaround time. Experimental result indicates that the proposed approach is effective while dealing with large scale parallel jobs scheduling on heterogeneous Grid and outperforms other conventional algorithms.

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Guo, Hong, Jiayin Zhou, and Haonan Gu. "Limited Duplication-Based List Scheduling Algorithm for Heterogeneous Computing System." Micromachines 13, no. 7 (July 3, 2022): 1067. http://dx.doi.org/10.3390/mi13071067.

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Efficient scheduling algorithms have been a leading research topic for heterogeneous computing systems. Although duplication-based scheduling algorithms can significantly reduce the total completion time, they are generally accompanied by an exorbitant time complexity. In this paper, we propose a new task duplication-based heuristic scheduling algorithm, LDLS, that can reduce the total completion time and maintains a low time complexity. The scheduling procedure of LDLS is composed of three main phases: In the beginning phase, the maximum number of duplications per level and per task is calculated to prevent excessive duplications from blocking regular tasks. In the next phase, the optimistic cost table (OCT) and ranking of tasks are calculated with reference to PEFT. In the final phase, scheduling is conducted based on the ranking, and the duplication of each task is dynamically determined, enabling the duplicated tasks to effectively reduce the start execution time of its successor tasks. Experiments of algorithms on randomly generated graphs and real-world applications indicate that both the scheduling length and the number of better case occurrences of LDLS are better than others.

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Wang, Jingjing, Y. F. Zhang, L. Geng, J. Y. H. Fuh, and S. H. Teo. "A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs." Unmanned Systems 03, no. 03 (July 2015): 205–19. http://dx.doi.org/10.1142/s2301385015500132.

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This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.

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BEAUMONT, OLIVIER, ARNAUD LEGRAND, LORIS MARCHAL, and YVES ROBERT. "STEADY-STATE SCHEDULING ON HETEROGENEOUS CLUSTERS." International Journal of Foundations of Computer Science 16, no. 02 (April 2005): 163–94. http://dx.doi.org/10.1142/s0129054105002930.

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This paper considers steady-state scheduling techniques for heterogeneous systems, such as clusters and grids. The use of steady-state scheduling is advocated to solve a variety of important problems, which would be too difficult to tackle with the objective of makespan minimization. Several examples are given, namely master-slave tasking, mixed task and data parallelism, and pipelined macro-communications (scatter, broadcast, multicast). For each example, both the advantages and the limitations of the approach are discussed.

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Bibal, J. V. Benifa, and D. Dejey. "An Auto-Scaling Framework for Heterogeneous Hadoop Systems." International Journal of Cooperative Information Systems 26, no. 04 (November 14, 2017): 1750004. http://dx.doi.org/10.1142/s0218843017500046.

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The scalability of the cloud infrastructure is essential to perform large-scale data processing using MapReduce programming model by automatically provisioning and de-provisioning the resources on demand. The existing MapReduce model shows performance degradation while getting adapted to heterogeneous environments since sufficient techniques are not available to scale the resources on demand and the scheduling algorithms would not cooperate as the resources are configured dynamically. An Auto-Scaling Framework (ASF) is presented in this article to configure the resources automatically based on the current system load in a heterogeneous Hadoop environment. The scheduling of data and task is done in a data-local manner that adapts while new resources are configured, or the existing resources are removed. A monitoring module is integrated with the JobTracker to observe the status of physical machines, compute the system load and provide automated provisioning of the resources. Then, Replica Tracker is utilized to track the replica objects for efficient scheduling of the task in the physical machines. The experiments are conducted in a commercial cloud environment using diverse workload characteristics, and the observations show that the proposed framework outperforms the existing scheduling mechanisms by the performance metrics such as average completion time, scheduling time, data locality, resource utilization and throughput.

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Chaudhuri, Pranay, and Jeffrey Elcock. "Process Scheduling in Heterogeneous Multiprocessor Systems Using Task Duplication." International Journal of Business Data Communications and Networking 6, no. 1 (January 2010): 58–69. http://dx.doi.org/10.4018/jbdcn.2010010104.

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Scheduling tasks in heterogeneous parallel and distributed computing environments continues to be a challenging problem. In this paper, the authors investigate the Heterogeneous Earliest Finish Time (HEFT) algorithm, along with alternative scheduling policies for task prioritising phases and the Critical Path on a Processor (CPOP) for scheduling tasks on a heterogeneous multiprocessor system. The authors show that by combining the HEFT algorithm selection policy with the task duplication strategy, it is possible to further reduce the schedule length produced by both HEFT and CPOP. The process scheduling algorithm presented in this paper compares favourably with other algorithms that use a similar strategy. The proposed algorithm has a time complexity of ?(¦V¦2(p + d)), whererepresents the number of tasks, p represents the number of processors and d the maximum in-degree of tasks.

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Arar, Chafik, and Mohamed Salah Khireddine. "Hybrid Software Redundancy Approach for Building Reliable Communication in Multi-BUS Heterogeneous Systems." International Journal of Reliability, Quality and Safety Engineering 23, no. 04 (August 2016): 1650013. http://dx.doi.org/10.1142/s0218539316500133.

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The paper proposes a new reliable fault-tolerant scheduling algorithm for real-time embedded systems. The proposed algorithm is based on static scheduling that allows to include the dependencies and the execution cost of tasks and data dependencies in its scheduling decisions. Our scheduling algorithm is dedicated to multi-bus heterogeneous architectures with multiple processors linked by several shared buses. This scheduling algorithm is considering only one bus fault caused by hardware faults and compensated by software redundancy solutions. The proposed algorithm is based on both active and passive backup copies to minimize the scheduling length of data on buses. In the experiments, the proposed methods are evaluated in terms of data scheduling length for a set of DSP benchmarks. The experimental results show the effectiveness of our technique.

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Zhou, Naqin, Xiaowen Liao, Fufang Li, Yuanyong Feng, and Liangchen Liu. "List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System." Wireless Communications and Mobile Computing 2021 (December 11, 2021): 1–16. http://dx.doi.org/10.1155/2021/9529022.

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Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.

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Orfanidis, Charalampos, Atis Elsts, Paul Pop, and Xenofon Fafoutis. "TSCH Evaluation under Heterogeneous Mobile Scenarios." IoT 2, no. 4 (October 22, 2021): 656–68. http://dx.doi.org/10.3390/iot2040033.

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Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be tailored to the requirements of specific applications. Several performance aspects of TSCH have been investigated so far, such as the energy consumption, reliability, scalability and many more. However, mobility in TSCH networks remains an aspect that has not been thoroughly explored. In this paper, we examine how TSCH performs under mobility situations. We define two mobile scenarios: one where autonomous agriculture vehicles move on a predefined trail, and a warehouse logistics scenario, where autonomous robots/vehicles and workers move randomly. We examine how different TSCH scheduling approaches perform on these mobility patterns and when a different number of nodes are operating. The results show that the current TSCH scheduling approaches are not able to handle mobile scenarios efficiently. Moreover, the results provide insights on how TSCH scheduling can be improved for mobile applications.

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Mace, Ruth. "The co-evolution of human fertility and wealth inheritance strategies." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1367 (March 29, 1998): 389–97. http://dx.doi.org/10.1098/rstb.1998.0217.

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Life history theory concerns the scheduling of births and the level of parental investment in each offspring. In most human societies the inheritance of wealth is an important part of parental investment. Patterns of wealth inheritance and other reproductive decisions, such as family size, would be expected to influence each other. Here I present an adaptive model of human reproductive decision-making, using a state-dependent dynamic model. Two decisions made by parents are considered: when to have another baby, and thus the pattern of reproduction through life; and how to allocate resources between children at the end of the parents life. Optimal decision rules are those that maximize the number of grandchildren. Decisions are assumed to depend on the state of the parent, which is described at any time by two variables: number of living sons, and wealth. The dynamics of the model are based on a traditional African pastoralist system, but it is general enough to approximate to any means of subsistence where an increase in the amount of wealth owned increases the capacity for future production of resources. The model is used to show that, in the unpredictable environment of a traditional pastoralist society, high fertility and a biasing of wealth inheritance to a small number of children are frequently optimal. Most such societies are now undergoing a transition to lower fertility, known as the demographic transition. The effects on fertility and wealth inheritance strategies of reducing mortality risks, reducing the unpredictability of the environment and increasing the costs of raising children are explored. Reducing mortality has little effect on completed family sizes of living children or on the wealth they inherit. Increasing the costs of raising children decreases optimal fertility and increases the inheritance left to each child at each level of wealth, and has the potential to reduce fertility to very low levels. The results offer an explanation for why wealthy families are frequently also those with the smallest number of children in heterogenous, post-transition societies.

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Chen, Ruey-Maw, and Chuin-Mu Wang. "Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid." Abstract and Applied Analysis 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/589862.

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The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

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Lin, Weiwei, Wentai Wu, and James Z. Wang. "A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7040276.

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Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

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Park, Bongsang, Junghyo Nah, Jang-Young Choi, Ick-Jae Yoon, and Pangun Park. "Transmission Scheduling Schemes of Industrial Wireless Sensors for Heterogeneous Multiple Control Systems." Sensors 18, no. 12 (December 5, 2018): 4284. http://dx.doi.org/10.3390/s18124284.

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The transmission scheduling scheme of wireless networks for industrial control systems is a crucial design component since it directly affects the stability of networked control systems. In this paper, we propose a novel transmission scheduling framework to guarantee the stability of heterogeneous multiple control systems over unreliable wireless channels. Based on the explicit control stability conditions, a constrained optimization problem is proposed to maximize the minimum slack of the stability constraint for the heterogeneous control systems. We propose three transmission scheduling schemes, namely centralized stationary random access, distributed random access, and Lyapunov-based scheduling scheme, to solve the constrained optimization problem with a low computation cost. The three proposed transmission scheduling schemes were evaluated on heterogeneous multiple control systems with different link conditions. One interesting finding is that the proposed centralized Lyapunov-based approach provides almost ideal performance in the context of control stability. Furthermore, the distributed random access is still useful for the small number of links since it also reduces the operational overhead without significantly sacrificing the control performance.

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Zhang, Jian Jun, Tian Hong Wang, and Yu Zhuo Wang. "Expected Cost Based Greedy Scheduling Algorithm for Out-Tree Task Graphs." Applied Mechanics and Materials 556-562 (May 2014): 3431–37. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3431.

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Effective task scheduling is crucial for achieving high performance in heterogeneous computing environments. Whiling scheduling Out-Tree task graphs, many previous heterogeneity based heuristic algorithms usually require high scheduling costs and may not deliver good quality schedules with lower costs. Aiming at the characteristics of Out-Tree task graphs and the features of heterogeneous environments and adopting the strategy based on expected costs and task duplications, this paper proposes a greedy scheduling algorithm, which, at each scheduling step, tries to guarantee not to increase the schedule length, schedules the current task onto the used processor which minimizes its execution finish time; meanwhile, takes load balances into account to economize the use of processors. The comparative experimental results show that the proposed algorithm has higher scheduling efficiency and robust performance, which could produce better schedule which has shorter schedule length and less number of used processors.

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Li, Qianmu, Shunmei Meng, Xiaonan Sang, Hanrui Zhang, Shoujin Wang, Ali Kashif Bashir, Keping Yu, and Usman Tariq. "Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing." ACM Transactions on Internet Technology 21, no. 3 (June 9, 2021): 1–33. http://dx.doi.org/10.1145/3408291.

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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

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Journal articles on the topic 'Heterogenous scheduling'

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