• Journal of Information Processing Systems
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• v.10 no.2
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• pp.193-214
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• 2014

Load balancing is the major benefit of any distributed system. To facilitate this advantage, task duplication and migration methodologies are employed. As this paper deals with dependent tasks (DAG), we used duplication. Task duplication reduces the overall schedule length of DAG along-with load balancing. This paper proposes a new task duplication algorithm at the time of tasks assignment on various processors. With the intention of conducting proposed algorithm performance computation; simulation has been done on the Netbeans IDE. The mesh topology of a distributed system is simulated at this juncture. For task duplication, overall schedule length of DAG is the main parameter that decides the performance of a proposed duplication algorithm. After obtaining the results we compared our performance with arbitrary task assignment, CAWF and HEFT-TD algorithms. Additionally, we also compared the complexity of the proposed algorithm with the Duplication Based Bottom Up scheduling (DBUS) and Heterogeneous Earliest Finish Time with Task Duplication (HEFT-TD).

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.19-51
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• 1996

We suggest a distributed framework for task assignment in the computer-controlled shop floor where each of the resource agents and part agents acts like an independent profit maker. The job allocation problem is formulated as a linear programming problem. The LP formulation is analyzed to provide a rationale for the distributed task assignment procedure. We suggest an auction based negotiation procedure including a price-based bid construction and a price revising mechanism. The performance of the suggested procedure is compared with those of an LP formulation and conventional dispatching procedures by simulation experiments.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.215-220
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• 2021

As the role of robots expands, flexible task planning methods are attracting attention from various domains. Many task planning frameworks are introduced to efficiently work in a wide range of areas. In order to work well in a broad region with multiple robots, various communication conditions should be controlled by task planning frameworks. However, few methods are proposed. In this paper, we propose mission planning methods according to the communication status of robots. The proposed method was verified through experiments assuming different communication states with a multi-robot system.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.219-221
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• 1995

This paper proposes a model for simulation and performance evaluation of distributed computer systems(DCS). The model is composed of operating system(OS), resource, task, environment submodel. Task Flow Graph(TFG) is suggested to describe the relation between tasks. This paper considers task response time, the scheduler's ready queue length, utilization of each resource as performance indices. The distributed system of Continuous Annealing Line(CAL) in iron process is simulated with the proposed model.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of information and communication convergence engineering
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• v.9 no.2
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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.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.1-11
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• 2019

The purpose of this study is to examine the effects of different group incentive type on performance under two types of interdependence in a task structure. One hundred twenty college and graduate students were recruited and asked to perform group typing task. Each typing groups organized three members. Participants were randomly assigned to one of four experimental conditions: equally-distributed incentive and differentially-distributed under two different task interdependence. In this study, the dependent variable was the number of correctly typed characters. We found the significant interaction effects between group incentive type and task interdependence. Specifically, under interdependent task, the work performance of participants in equally-distributed group incentive condition was higher than the performance in differentially-distributed group incentive condition.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.153-162
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• 2017

Advances in communication technologies and the decreasing cost of computers have made distributed computer systems an attractive alternative for satisfying the information needs of large organizations. This paper presents a distributed algorithm for performance improvement through load balancing and file migration in distributed systems. We employed a sender initiated strategy for task migration and used learning automata with several internal states for file migration. A task can be migrated according to the load information of a computer. A file is migrated to the destination processor when it is in the right boundary state. We also described an analytical model for load balancing with file migration to verify the proposed algorithm. Analytical and simulation results show that our algorithm is very well-suited for distributed system environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.988-996
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• 2012

This paper presents a distributed task assignment algorithm for the suppression of enemy air defense (SEAD) mission of heterogeneous UAVs, based on the consensus-based bundle algorithm (CBBA). SEAD mission can be modeled as a task assignment problem of multiple UAVs performing multiple air defense targets, and UAVs performing SEAD mission consist of the weasel for destruction of enemy's air defense system and the striker for the battle damage assessment (BDA) or other tasks. In this paper, a distributed task assignment algorithm considering path-planning in presence of terrain obstacle is developed for heterogeneous UAVs, and then it is applied to SEAD mission. Through numerical simulations the performance and the applicability of the proposed method are tested.