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نتیجه جستجو - scheduling algorithm

تعداد مقالات یافته شده: 36
ردیف عنوان نوع
1 Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms
ارزیابی عملکرد ماشین به ماشین از وسایل نقلیه برقی شبکه یکپارچه با استفاده از الگوریتم های مختلف برنامه ریزی-2020
For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollution-less behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth-generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers.
Keywords: DLS | Electric vehicle | Energy management system | EXP | M2M communication | M-LWDF | PF | PLR
مقاله انگلیسی
2 A task scheduling algorithm considering game theory designed for energy management in cloud computing
یک الگوریتم برنامه ریزی کار با توجه به تئوری بازی طراحی شده برای مدیریت انرژی در محاسبات ابری-2020
With the increasing popularity of cloud computing products, task scheduling problem has become a hot research topic in this field. The task scheduling problem of cloud computing system is more complex than the traditional distributed system. Based on the analysis of cloud computing in related literature, we established a simplified model for task scheduling system in cloud computing.Different from the previous research of cloud computing task scheduling algorithm, the simplified model in this paper is based on game theory as a mathematical tool. Based on game theory, the task scheduling algorithm considering the reliability of the balanced task is proposed. Based on the balanced scheduling algorithm, the task scheduling model for computing nodes is proposed. In the cooperative game model, game strategy is used for the task in the calculation of rate allocation strategy on the node. Through analysis of experimental results, it is shown that the proposed algorithm has better optimization effect.
Keywords: Task scheduling | Game theory | Cloud computing | Optimization
مقاله انگلیسی
3 MapReduce based tipping point scheduler for parallel image processing
مانبندی نقطه اوج بر اساس MapReduce برای پردازش تصویر موازی-2020
Nowadays, Big Data image processing is very much in need due to its proven success in the field of business information system, medical science and social media. However, as the days are passing by, the computation of Big Data images is becoming more complex which ultimately results in complex resource management and higher task execution time. Researchers have been using a combination of CPU and GPU based computing to cut down the execution time, however, when it comes to scaling of compute nodes, then the combination of CPU and GPU based computing still remains a challenge due to the high commu- nication cost factor. In order to tackle this issue, the Map-Reduce framework has come out to be a viable option as its workflow optimization could be enhanced by changing its underlying job scheduling mech- anism. This paper presents a comparative study of job scheduling algorithms which could be deployed over various Big Data based image processing application and also proposes a tipping point scheduling algorithm to optimize the workflow for job execution on multiple nodes. The evaluation of the proposed scheduling algorithm is done by implementing parallel image segmentation algorithm to detect lung tu- mor for up to 3GB size of image dataset. In terms of performance comprising of task execution time and throughput, the proposed tipping point scheduler has come out to be the best scheduler followed by the Map-Reduce based Fair scheduler. The proposed tipping point scheduler is 1.14 times better than Map- Reduce based Fair scheduler and 1.33 times better than Map-Reduced based FIFO scheduler in terms of task execution time and throughput. In terms of speedup comparison between single node and multiple nodes, the proposed tipping point scheduler attained a speedup of 4.5 X for multi-node architecture.
Keywords: Job scheduler | Workflow optimization | Map-Reduce | Tipping point scheduler | Parallel image segmentation | Lung tumor
مقاله انگلیسی
4 Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø
برنامه ریزی انرژی از یک ریز شبکه هوشمند با پانل های فتوولتائیک مشترک و ذخیره سازی: پرونده مارینا بالن در سامسوی-2020
This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master’s office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach
Keywords: Microgrid | Demand side management | Renewable energy | Energy storage | Energy management | On-line scheduling | Model predictive control | Optimization algorith
مقاله انگلیسی
5 Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
صرفه جویی در وقت و هزینه در برنامه ریزی کاربردهای اینترنت اشیا-مبتنی بر مه با استفاده از روش یادگیری تقویتی عمیق-2020
Due to the rapid growth of intelligent devices and the Internet of Things (IoT) applications in recent years, the volume of data that is generated by these devices is increasing ceaselessly. Hence, moving all of these data to cloud datacenters would be impossible and would lead to more bandwidth usage, latency, cost, and energy consumption. In such cases, the fog layer would be the best place for data processing. In the fog layer, the computing equipment dedicates parts of its limited resources to process the IoT application tasks. Therefore, efficient utilization of computing resources is of great importance and requires an optimal and intelligent strategy for task scheduling. In this paper, we have focused on the task scheduling of fog-based IoT applications with the aim of minimizing long-term service delay and computation cost under the resource and deadline constraints. To address this problem, we have used the reinforcement learning approach and have proposed a Double Deep Q-Learning (DDQL)-based scheduling algorithm using the target network and experience replay techniques. The evaluation results reveal that our proposed algorithm outperforms some baseline algorithms in terms of service delay, computation cost, energy consumption and task accomplishment and also handles the Single Point of Failure (SPoF) and load balancing challenges.
Keywords: Fog computing | Task scheduling | Deep reinforcement learning | Double Q-Learning | Service delay | Computation cost
مقاله انگلیسی
6 Cost-efficient dynamic scheduling of big data applications in apache spark on cloud
برنامه ریزی پویا مقرون به صرفه برنامه های داده های بزرگ در آپاچی اسپارک روی ابر-2020
Job scheduling is one of the most crucial components in managing resources, and efficient execution of big data applications. Specifically, scheduling jobs in a cloud-deployed cluster are challenging as the cloud offers different types of Virtual Machines (VMs) and jobs can be heterogeneous. The default big data processing framework schedulers fail to reduce the cost of VM usages in the cloud environment while satisfying the performance constraints of each job. The existing works in cluster scheduling mainly focus on improving job performance and do not leverage from VM types on the cloud to reduce cost. In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the proposed algorithms can also prioritise jobs based on their given deadlines. Besides, the proposed scheduling algorithms are online and adaptive to clus- ter changes. We have also implemented the proposed algorithms on top of Apache Mesos. Furthermore, we have performed extensive experiments on real datasets and compared to the existing schedulers to showcase the superiority of our proposed algorithms. The results indicate that our algorithms can reduce resource usage cost up to 34% under different workloads and improve job performance.
Keywords: Cloud | Apache spark | Scheduling | Cost-efficiency
مقاله انگلیسی
7 Adaptive request scheduling for the I/O forwarding layer using reinforcement learning
زمانبندی درخواست تطبیقی برای لایه انتقال ورودی و خروجی با استفاده از یادگیری تقویتی-2020
In this paper, we propose an approach to adapt the I/O forwarding layer of HPC systems to applications’ access patterns. I/O optimization techniques can improve performance for the access patterns they were designed to target, but they often decrease performance for others. Furthermore, these techniques usually depend on the precise tune of their parameters, which commonly falls back to the users. Instead, we propose to do it dynamically at runtime based on the I/O workload observed by the system. Our approach uses a reinforcement learning technique – contextual bandits – to make the system capable of learning the best parameter value to each observed access pattern during its execution. That eliminates the need of a complicated and time-consuming previous training phase. Our case study is the TWINS scheduling algorithm, where performance improvements depend on the time window parameter, which in turn depends on the workload. We evaluate our proposal and demonstrate it can reach a precision of 88% on the parameter selection in the first hundreds of observations of an access pattern, achieving 99% of the optimal performance. We demonstrate that the system – which is expected to live for years – will be able to adapt to changes and optimize its performance after having observed an access pattern for a few (not necessarily contiguous) minutes.
Keywords: High performance I/O | Parallel I/O | I/O scheduling | I/O forwarding | Reinforcement learning | Auto-tuning
مقاله انگلیسی
8 Price-responsive early charging control based on data mining for electric vehicle online scheduling
کنترل شارژ زودرس پاسخگو به قیمت بر اساس داده کاوی برای برنامه ریزی آنلاین وسایل نقلیه الکتریکی-2019
The uncertainty of electric vehicle (EV) behavior is deemed as a major challenge in online charging scheduling. It may lead to charging congestion to compromise the whole benefits of EV owners and aggregators. Early charging is the most efficient way to tackle the dynamic problem. However, it is very challenging for early charging to achieve the adaptive control and minimize electricity bill. In this paper, a price-responsive early charging adaptive control (PRECC) is proposed. The speedup factor is designed as a subtotal of charging demand categorized by electricity price, and it can be determined with only one offline charging optimization through a data-mining method. Due to the strong correlation with electricity price, PRECC can help online scheduling algorithms minimize early charging cost. Since it is not limited by the states of EVs, it can rapidly respond to the variations of base load and electricity price. Besides, with the independent design, it can well match online scheduling algorithms. Computer simulations are made to verify the proposed control. The results show that PRECC can improve the optimality of online scheduling by an average of 5.4%. Compared with the traditional early charging strategies, it has obvious advantages in terms of optimality, power capacity utilization, and profitability.
Keywords: Charging coordination | Data mining | Dynamic problem | Early charging | Electric vehicle (EV) | Online scheduling algorithm
مقاله انگلیسی
9 الگوریتم زمانبندی گره مبتنی بر توری برای شبکه‎های حسگر بی‌سیم
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 13
نحوه‌ی کاهش مصرف انرژی شبکه و افزایش عمر مفید شبکه‌ی حسگر بی‌سیم یکی از مباحث پژوهشی مهم در حوزه‌ی شبکه‌ی حسگر بی‌سیم است. با پیش‌فرض اطمینان از پوشش شبکه، الگوریتم زمانبندی گره که گره‎های زائد ا به حالت خواب می‌برد، اقدام کارآمدی برای کاهش مصرف انرژی است. در این مقاله، یک سازوکار زمانبندی گره مبتنی بر توری پیشنهاد شده است. این سازوکار، وزن تمامی گره‌های موجود در هر توری را محاسبه نموده و سپس مشخص می‌سازد آیا گره جزو گره‌های با پوشش زائد است یا خیر. نتیجه‎‌ی شبیه‌سازی متلب نشان می‌دهد که این سازوکار می‌توان به خوبی زمان بقای شبکه را افزایش دهد. کلیدواژه ها: شبکه‌ی حسگر بی‌سیم | مش‌بندی | زمانبندی گره | الگوریتم زمانبندی
مقاله ترجمه شده
10 Scheduling workflows with privacy protection constraints for big data applications on cloud
جریان های برنامه ریزی شده با محدودیت های حفاظت از حریم خصوصی برای برنامه های داده بزرگ در ابر-2018
Nowadays, business or scientific processes with massive big data in Cyber-Physical-Social environments are springing up in cloud. Cloud customers’ private information stored in cloud may be easily exposed and lead to serious privacy leakage issues in Cyber-Physical-Social environments. To avoid such issues, cloud customers’ privacy or sensitive data may be restricted to being processed by some specific trusted cloud data centers. Therefore, a new problem is how to schedule workflow with such data privacy protection constraints, while minimizing both execution time and monetary cost for big data applications on cloud. In this paper, we model such problem as a multi-objective optimization problem and propose a Multi Objective Privacy-Aware workflow scheduling algorithm, named MOPA. It can provide cloud customers with a set of Pareto tradeoff solutions. The problem-specific encoding and population initialization are proposed in this algorithm. The experimental results show that our algorithm can obtain higher quality solutions when compared with other ones.
Keywords: Privacy protection ، Workflow scheduling ، Cloud ، Big data ، Multi-objective optimization
مقاله انگلیسی
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