• 제목/요약/키워드: Task assigning algorithm

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Compromise Scheme for Assigning Tasks on a Homogeneous Distributed System

  • Kim, Joo-Man
    • Journal of information and communication convergence engineering
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    • 제9권2호
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    • pp.141-149
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    • 2011
  • We consider the problem of assigning tasks to homogeneous nodes in the distributed system, so as to minimize the amount of communication, while balancing the processors' loads. This issue can be posed as the graph partitioning problem. Given an undirected graph G=(nodes, edges), where nodes represent task modules and edges represent communication, the goal is to divide n, the number of processors, as to balance the processors' loads, while minimizing the capacity of edges cut. Since these two optimization criteria conflict each other, one has to make a compromise between them according to the given task type. We propose a new cost function to evaluate static task assignments and a heuristic algorithm to solve the transformed problem, explicitly describing the tradeoff between the two goals. Simulation results show that our approach outperforms an existing representative approach for a variety of task and processing systems.

Managing Deadline-constrained Bag-of-Tasks Jobs on Hybrid Clouds with Closest Deadline First Scheduling

  • Wang, Bo;Song, Ying;Sun, Yuzhong;Liu, Jun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권7호
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    • pp.2952-2971
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    • 2016
  • Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we studied the management of deadline-constrained bag-of-tasks jobs on hybrid clouds. We presented a binary nonlinear programming (BNP) problem to model the hybrid cloud management which minimizes rent cost from the public cloud while completes the jobs within their respective deadlines. To solve this BNP problem in polynomial time, we proposed a heuristic algorithm. The main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline. When there is no available core, the algorithm adds an available physical machine (PM) with most capacity or rents a new virtual machine (VM) with highest cost-performance ratio. As there may be a workload imbalance between/among cores on a PM/VM after task assigning, we propose a task reassigning algorithm to balance them. Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm, and that our task reassigning algorithm improves the makespan of tasks up to 47.6%.

Scate: A Scalable Time and Energy Aware Actor Task Allocation Algorithm in Wireless Sensor and Actor Networks

  • Sharifi, Mohsen;Okhovvat, Morteza
    • ETRI Journal
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    • 제34권3호
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    • pp.330-340
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    • 2012
  • In many applications of wireless sensor actor networks (WSANs) that often run in harsh environments, the reduction of completion times of tasks is highly desired. We present a new time-aware, energy-aware, and starvation-free algorithm called Scate for assigning tasks to actors while satisfying the scalability and distribution requirements of WSANs with semi-automated architecture. The proposed algorithm allows concurrent executions of any mix of small and large tasks and yet prevents probable starvation of tasks. To achieve this, it estimates the completion times of tasks on each available actor and then takes the remaining energies and the current workloads of these actors into account during task assignment to actors. The results of our experiments with a prototyped implementation of Scate show longer network lifetime, shorter makespan of resulting schedules, and more balanced loads on actors compared to when one of the three well-known task-scheduling algorithms, namely, the max-min, min-min, and opportunistic load balancing algorithms, is used.

통계적 모형의 업무부하 균일화를 통한 비즈니스 프로세스의 효율화 (Workload Balancing on Agents for Business Process Efficiency based on Stochastic Model)

  • 하병현;설현주;배준수;박용태;강석호
    • 산업공학
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    • 제16권spc호
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    • pp.76-81
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    • 2003
  • BPMS (Business Process Management Systems) is aninformation system that systematically supports designing, administrating, and improving the business processes. It can execute the business processes by assigning tasks to human or computer agents according to the predefined definitions of the processes. In this research we developed a task assignment algorithm that can maximize overall process efficiency under the limitation of agents' capacity. Since BPMS manipulates the formal and predictable business processes, we can analyze the processes using queuing theory to achieve overall process efficiency. We first transform the business processes into queuing network model in which the agents are considered as servers. After that, workloads of agents are calculated as server utilization and we can determine the task assignment policy by balancing the workloads. This will make the workloads of all agents be minimized, and the overall process efficiency is achieved in this way. Another application of the results can be capacity planning of agents in advance and business process optimization in reengineering context. We performed the simulation analysis to validate the results and also show the effectiveness of the algorithm by comparing with well known dispatching policies.

OPTIMAL PERIOD AND PRIORITY ASSIGNMENT FOR A NETWORKED CONTROL SYSTEM SCHEDULED BY A FIXED PRIORITY SCHEDULING SYSTEM

  • Shin, M.;SunWoo, M.
    • International Journal of Automotive Technology
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    • 제8권1호
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    • pp.39-48
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    • 2007
  • This paper addresses the problem of period and priority assignment in networked control systems (NCSs) using a fixed priority scheduler. The problem of assigning periods and priorities to tasks and messages is formulated as an optimization problem to allow for a systematic approach. The temporal characteristics of an NCS should be considered by defining an appropriate performance index (PI) which represents the temporal behavior of the NCS. In this study, the sum of the end-to-end response times required to process all I/Os with precedence relationships is defined as a PI. Constraints are derived from the task and message deadline requirements to guarantee schedulability. Genetic algorithms are used to solve this constrained optimization problem because the optimization formulation is discrete and nonlinear. By considering the effects of communication, an optimum set of periods and priorities can be holistically derived.

Application and Research of Monte Carlo Sampling Algorithm in Music Generation

  • MIN, Jun;WANG, Lei;PANG, Junwei;HAN, Huihui;Li, Dongyang;ZHANG, Maoqing;HUANG, Yantai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권10호
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    • pp.3355-3372
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    • 2022
  • Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

MPSoC용 임베디드 소프트웨어의 PSM 모델링 및 시뮬레이션 (Modeling and Simulation of Platform Specific Model in MPSoC Environment)

  • 송인권;오기영;홍장의;배두환
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제34권8호
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    • pp.697-707
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    • 2007
  • 임베디드 소프트웨어는 탑재될 하드웨어 아키텍처에 매우 의존적이기 때문에 플랫폼 특성을 고려한 소프트웨어 설계가 이루어져야 한다. 본 연구에서는 MPSoC(Multi Processor System On Chip)용 플랫폼에 탑재될 임베디드 소프트웨어의 PIM(Platform Independent Model)을 PSM(Platform Specific Model)에 매핑하기 위한 기법을 제안하고, 매핑 결과에 대한 시뮬레이션을 통해 매핑 기법의 유효성을 검사하였다. 제안하는 방법은 UML(Unified Modeling Language) 기반의 객체지향 모델로부터 태스크를 도출하여 이 기종의 하드웨어 컴포넌트로 구성된 MPSoC 플랫폼에 할당하기 위한 것으로써, 할당의 정확성 및 신속성과 소프트웨어 병렬성을 극대화 할 수 있는 장점을 제공한다.

도로 네트워크 환경에서 적응적 우선순위 기반의 순차적 경로 처리 기법 (An Adaptive Priority-based Sequenced Route Query Processing Method in Road Networks)

  • 유형철;정성원
    • 정보과학회 컴퓨팅의 실제 논문지
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    • 제20권12호
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    • pp.652-657
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    • 2014
  • 출발지와 도착지, 사용자가 방문해야 하는 POI(Point of Interest)유형들, 이 유형들 중 전체 혹은 부분에 대한 방문순서들이 주어질 때, 출발지에서 여러 방문순서를 만족시키는 순서대로 각 POI 유형별로 적어도 하나의 POI를 방문한 후 목적지에 도착하는 경로를 순차적 경로라 한다. 본 논문에선 이 경로 중 근사 최단 경로(Approximate Shortest Path)를 찾는다. 기존의 두 가지 기법은 방문한 POI(또는 출발지)와 방문해야 할 유형의 POI들과의 근접성 만을 보거나 후보군 선정 시 방문순서 및 네트워크를 고려하지 않고 출발지와 목적지를 이은 직선과 가까운 POI들로 경로를 찾는 문제를 갖고 있었다. 본 논문에서는 네트워크 탐색을 이용하여 출발지와 목적지 간 네트워크 경로에 근접한 POI를 후보군으로 선정하여 경로를 탐색하고, 이 경로 중 방문한 POI유형이 많은 경로에 적응적 우선순위를 주어 향상된 근사 최단 경로를 구하는 기법을 제안한다.