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ردیف | عنوان | نوع |
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1 |
Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling
روش جستجوی ارگانیسم های همزیستی گسسته برای حل مسئله تجارت هزینه زمان در مقیاس بزرگ زمانبندی ساخت-2020 Construction projects are becoming increasingly larger and more complex in terms of size and cost. An optimization tool is necessary for the construction management system to develop the desired construc- tion schedule to save time and cost. However, only a few effort s have been made to deal with the time-cost trade-offproblem (TCTP) in the large-scale construction projects, and the existing optimiza- tion methods are slightly limited by the trouble of parameter tuning. As TCTP is known to be an NP-hard problem, this paper aims to introduce a new variant of Symbiotic Organisms Search (SOS) algorithm that does not contain control parameters, called DSOS (Discrete Symbiotic Organisms Search) which gener- ates the parasite organism using a heuristic rule based on the network levels. This enhancement helps to improve the exploration phase and avoid premature stagnation. Performances are evaluated on project instances with different numbers of activities varying from 180 to 6300, as well as nine newly gener- ated project instances with 720 activities but different network structures. The obtained results show a good performance of DSOS in terms of robustness and deviation from optimum in comparison with other meta-heuristics and variants of DSOS without using the heuristic rule. The good performance implies that DSOS is sufficient to serve as an effective tool to generate an optimized construction schedule. Keywords: Large-scale construction project | Deadline constraint | Time-cost trade-off | Discrete symbiotic organisms search |
مقاله انگلیسی |
2 |
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 |
3 |
Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds
بهینه سازی هزینه برای زمانبندی دقیق پردازش داده های بزرگ کارها در ابرها-2018 Cloud computing has been widely regarded as a capable solution for big data processing. Nowadays cloud
service providers usually offer users virtual machines with various combinations of configurations and
prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination
under a deadline constraint is becoming more complex for users. The complexity of determining the cost
minimized combination may be resulted from different causes: the characteristics of user applications,
and providers’ setting on the configurations and pricing of virtual machine. In this paper, we proposed
a variety of algorithms to help the users to schedule their big data processing workflow applications on
clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed
algorithms were evaluated by extensive simulation experiments with diverse experimental settings.
Keywords: Big-data ، Scheduling ، Cost-efficient ، Cloud computing |
مقاله انگلیسی |
4 |
A benchmark approach and its toolkit for online scheduling of multiple deadline-constrained workflows in big-data processing systems
یک رویکرد معیار و ابزار آن برای برنامه ریزی آنلاین چندین جریان کار محدود شده در سیستم های پردازش داده های بزرگ-2018 As distributed systems such as clouds get increasingly popular in the use for big-data processing, there
is a need of shifting research attention from minimizing the workflow completion time to satisfying the
deadline constraints specified by the users and boosting the benefit of the resource providers. This paper
focuses on the online deadline-constrained workflow scheduling problem of how to schedule a set of
sequentially submitted workflows with deadline constraints to maximize the resource utilization as well
as the success rate of meeting the deadlines. A discrete-event based simulator with a novel benchmark
approach is proposed to ease the analysis of the problem. Extensive evaluation has been done to exhibit
the effectiveness and significance of the proposed benchmark approach and the developed simulator.
Keywords: Big-data ،Workflow ، Online scheduling ، Simulator ، Benchmark |
مقاله انگلیسی |
5 |
الگوریتم متعادل کننده بار با محدودیت مهلت منعطف برای محاسبات ابری
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 19 در دهه اخیر، الگوریتم های متعادل کننده بار زیادی برای رایانش ابری ارائه شده است، اما هیچ یک از این الگوریتم ها انعطاف لازم را با تعادل بار برقرار نکرده اند. ما به ارائه یک نوع معماری ابری پرداخته ایم که توانایی اداره حداکثر درخواست کاربر را پیش از مواجهه با مهلت زمانی (deadline ) در اختیار داشته و یک مکانیزم انعطاف پذیر را با کمک حداقل مقدار جریان با تکیه بر استراتژی ماشه ، فراهم می کند. نتایج رایانش ( جدول 1 و شکل های 2-5) نشان می دهند که این الگوریتم توسعه داده شده، زمان makespan را کاهش داده و نرخ پذیرش task را بیش از 10% در مقایسه با الگوریتم min-min، 30% در مقایسه با الگوریتم دریافت خدمات به ترتیب ورود ( FCFS) و کوتاهترین کار نخست (SJF)، در کلیه شرایط افزایش می دهد.
کلمات کلیدی: مقیاس پذیری | زمان مصرف شده | ماشین مجازی | انعطاف پذیری | زمانبندی وظیفه |
مقاله ترجمه شده |
6 |
Energy-efficient mapping of large-scale workflows under deadline constraints in big data computing systems
نگاشت انرژی کارای جریان کار در مقیاس وسیع تحت محدودیت های مهمی در سیستم های محاسباتی داده های بزرگ-2017 Large-scale workflows for big data analytics have become a main consumer of energy in data centers
where moldable parallel computing models such as MapReduce are widely applied to meet high com
putational demands with time-varying computing resources. The granularity of task partitioning in each
moldable job of such big data workflows has a significant impact on energy efficiency, which remains
largely unexplored. In this paper, we analyze the properties of moldable jobs and formulate a workflow
mapping problem to minimize the dynamic energy consumption of a given workflow request under
a deadline constraint in big data systems. Since this problem is strongly NP-hard, we design a fully
polynomial-time approximation scheme (FPTAS) for a special case with a pipeline-structured workflow
on a homogeneous cluster and a heuristic for the generalized problem with an arbitrary workflow on
a heterogeneous cluster. The performance superiority of the proposed solution in terms of dynamic
energy saving and deadline missing rate is illustrated by extensive simulation results in comparison with
existing algorithms, and further validated by real-life workflow implementation and experimental results
in Hadoop/YARN systems.
Keywords: Big data | Workflow mapping | Green computing |
مقاله انگلیسی |
7 |
A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments
یک رویکرد برنامه ریزی گردش کار پیش پردازش برای برنامه های داده بزرگ در محیط های چند ابری-2016 The rapid development of the latest distributed
computing paradigm, i.e., cloud computing, generates a highly
fragmented cloud market composed of numerous cloud providers
and offers tremendous parallel computing ability to handle big
data problems. One of the biggest challenges in multiclouds is
efficient workflow scheduling. Although the workflow scheduling
problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the
existing research works either fail to satisfy the quality of service
(QoS) requirements, or do not consider some fundamental features
of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called
multiclouds partial critical paths with pretreatment (MCPCPP),
for big data workflows in multiclouds is presented. This algorithm
incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined
deadline constraint. Our approach takes into consideration the
essential characteristics of multiclouds such as the charge per time
interval, various instance types from different cloud providers,
as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation
purpose and our experimental results show that the MCPCPP is
promising.
Index Terms: Big data| cloud computing | multiclouds | scheduling | scientific workflow |
مقاله انگلیسی |