TORSCHE Scheduling toolbox for Matlab

Šůcha, Přemysl; Kutil, Michal; Sójka, Michał; Hanzálek, Zdeněk

doi:10.1109/cacsd-cca-isic.2006.4776810

Cited by 18 publications

(5 citation statements)

References 20 publications

(17 reference statements)

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“…We used Time Optimization of Resources SCHEduling (TORSCHE) for task scheduling simulations, which is intended as a tool to handle task scheduling problems. 38 A task is defined as a data structure, including all scheduling process parameters of task as processing, arrival, starting, release, deadline, due date, etc. The following scheduling time has also been considered where each task is defined by specific constraints which are grouped as a set of tasks within precedence or successor constraints according to the application under consideration.…”

Section: Problem Definitionmentioning

confidence: 99%

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Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

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Section: Problem Definitionmentioning

confidence: 99%

“…We used Time Optimization of Resources SCHEduling (TORSCHE) for task scheduling simulations, which is intended as a tool to handle task scheduling problems . A task is defined as a data structure, including all scheduling process parameters of task as processing, arrival, starting, release, deadline, due date, etc.…”

Section: Problem Definitionmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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“…4. The visualization was generated using TORSCHE toolset [17]. To enable an optional wireless transmission, the task T-MS was defined, in which the payload for the wireless transceiver, and therefore its active time, is set to zero.…”

Section: B Tasksmentioning

confidence: 99%

Towards a comprehensive power consumption model for wireless sensor nodes

Hesse

Adams

Hörmann

et al. 2016

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Abstract-Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the system's power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.

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“…Section 5, describes the conclusion and future scope. The Performance of a system has been determined by using the scheduling algorithm with task parameters like start time (sj), processing time (pj), release time (rj), deadline (dj), due date (d~j), completion time (Cj=sj+pj), waiting time (wj=sj-rj), flow time (Fj=Cj-rj), lateness (Lj=Cj+dj) and tardiness (Dj=max{Cj-dj,0}) as shown in Fig 1 .1 [8]. Where 'j' represents the corresponding task-related information.…”

Section: Introductionmentioning

confidence: 99%

Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm

Sreenath¹,

Vijaya²

2021

JUSST

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The scheduling algorithms have been examined by the process of task execution in a system to achieve maximum utilization of multiprocessors. Consequently, the research attempted to propose a new real-time scheduling algorithm to support a multiprocessor platform. The proposed scheduling algorithm, List Mcnaughton’s amalgamation (LiMca) scheduling algorithm has been developed for an optimum solution with the features of List and Mcnaughton’s scheduling algorithms to overcome the individual drawbacks (pre-emption and Precedence constraints). In LiMca, Workload has been distributed to the processors in a system by sorting the tasks in decreasing order with their precedence constraints including due dates, pre-emption, and context switching. In this arena, the LiMca scheduling algorithm has been developed on a traditional avionic mission system and also simulated on a real-time application. The extensive simulations were carried out on the time optimization of resources scheduling (TORSCHE) simulation toolbox. LiMca scheduling algorithm performed superior as compared to recently reported scheduling algorithms like list, hu’s, McNaughton’s, Brucker’s, and Hodgson’s in terms of computational performance.

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TORSCHE Scheduling toolbox for Matlab

Cited by 18 publications

References 20 publications

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Towards a comprehensive power consumption model for wireless sensor nodes

Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm

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