Multi-objective Optimization of Real-Time Task Scheduling Problem for Distributed Environments

Salimi, Maghsood; Majd, Amin; Loni, Mohammad; Seceleanu, Tiberiu; Seceleanu, Cristina; Sirjani, Marjan; Daneshtalab, Masoud; Troubitsynå, Elena

doi:10.1145/3352700.3352713

Cited by 9 publications

(11 citation statements)

References 16 publications

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“…A meta-heuristic approach for real-time task scheduling problems is employed in [24], guaranteeing end-to-end tasks' deadlines in distributed environments. Two different exploration scenarios are analyzed, single (looking for the minimal number of processing units for all the tasks) and multi-objective exploration (considering the total number of processing units and the end-to-end finishing time for all the jobs).…”

Section: Related Workmentioning

confidence: 99%

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Multi-constrained network occupancy optimization

Halilovic

Zaimovic

Seceleanu

et al. 2023

ComSIS

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The greater the number of devices on a network, the higher load in the network, the more chance of a collision occurring, and the longer it takes to transmit a message. The size of load can be identified by measuring the network occupancy, hence it is desirable to minimize the latter. In this paper, we present an approach for network occupancy minimization by optimizing the packing process while satisfying multiple constraints. We formulate the minimization problem as a bin packing problem and we implement a modification of the Best-Fit Decreasing algorithm to find the optimal solution. The approach considers grouping signals that are sent to different destinations in the same package. The analysis is done on a medium-sized plant model, and different topologies are tested. The results show that the proposed solution lowers the network occupancy compared to a reference case.

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Section: Related Workmentioning

confidence: 99%

“…Another direction is to investigate the use of various artificial intelligence approaches (e.g. genetic algorithms [24]), or more sophisticated approaches. These approaches can be applied to large-scale optimization problems, specifically if problem-specific algorithms do not yield satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Multi-constrained network occupancy optimization

Halilovic

Zaimovic

Seceleanu

et al. 2023

ComSIS

Self Cite

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“…Scheduling algorithms, one of the most important issues of real-time systems, can be set in single-processor scheduling, centralized multi-processor scheduling, and distributed scheduling, depending on the system environment [ 5 ]. Among the algorithms discussed in real-time scheduling are RMS, EDF, and LLF for single-processor systems, thinking and strategy scheduling for multiprocessor systems, and GRMS and DSr for distributed real-time scheduling algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…A practical task can be divided into several tasks requiring a particular computation capability. There are two duty-allocation sections, i.e., allocating duties to processing units and scheduling tasks, i.e., task prioritizing work performance and data transmission arrangement between the processing units [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Scheduling in IoT Applications: A Systematic Review

Khajeh

Saberikamarposhti

Rahmani

2022

Sensors

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The Internet of Things (IoT) is a telecommunication network in the next generation of applications with the rapid progress of wireless sensor network techniques that have touched many spheres of life today. Hardware, telephony, communications, storage, secure platforms, software and services, and data processing platforms are all part of the IoT environment. IoT sensors collect data from their environment and share it by connecting to the Internet gateway. These sensors often perform tasks without human intervention. This article aims to review real-time scheduling in the IoT to fully understand the issues raised in this area published from 2018 to 2022. A classification for IoT applications based on practical application is provided for selected studies. Selected studies include healthcare, infrastructure, industrial applications, smart city, commercial applications, environmental protection, and general IoT applications. Studies are sorted into groups based on related applications and compared based on indicators such as performance time, energy consumption, makespan, and assessment environments depending on the provided classification. Finally, this paper discusses all reviewed studies’ main concepts, disadvantages, advantages, and future work.

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“…In [20], the authors present three price performance-driven heuristic algorithms for hardware cost minimization of embedded systems, which consider both real-time and reliability requirement. In [21], the authors present a multi-population genetic algorithm towards optimizing both operation time and the number of required processing units for distributed real-time systems. In [22], the authors try to reduce the hardware cost by minimize the number of required processors to schedule an application, where considerably memory requirements and application latency are reduced by comparing with related approaches while meeting the same throughput constraint.…”

Section: Related Workmentioning

confidence: 99%

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Xie

Guo

Yang

et al. 2021

Sensors

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The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.

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Multi-objective Optimization of Real-Time Task Scheduling Problem for Distributed Environments

Cited by 9 publications

References 16 publications

Multi-constrained network occupancy optimization

Multi-constrained network occupancy optimization

Real-Time Scheduling in IoT Applications: A Systematic Review

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

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