IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment

Khaledian, Navid; Khamforoosh, Keyhan; Azizi, Sadoon; Maihami, Vafa

doi:10.1016/j.suscom.2022.100834

Cited by 14 publications

(8 citation statements)

References 50 publications

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“…In this section, the performance of IETIF is evaluated in terms of energy consumption, response time, speedup, efficiency, and SLR. IETIF will be compared to recent studies such as HEFT-U, HEFT-D, HEFT-L [43], RandomHEFT [44], IWO-CA [42], and IKH-EFT [30]. Random DAGs are created in experiments to examine the performance of algorithms under various scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Due to resource constraints, IoT node mobility, and service quality limitations, scheduling has become a complex challenge for distributing resources correctly in the fog environment [17,18]. Many applications such as intelligent transportation, emergency services, online gaming, telecommunications, and digital signal processing require quick processing, low latency, and high reliability [19,20]. Therefore, we need a suitable scheduling algorithm for resource allocation in the fog environment.…”

Section: Related Workmentioning

confidence: 99%

“…The simulation results suggest that the proposed scheduling algorithm is more cost-effective than the primary method and can outperform it. Khaledian et al [30] presented a method based on the krill herd algorithm for scheduling tasks in a fog environment, which takes into account the financial cost in addition to energy and time. They performed a very comprehensive simulation for different conditions and showed that their method reduces energy consumption and response time.…”

Section: Related Workmentioning

confidence: 99%

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IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

Nazari,

Sohrabi,

Mohammadi

et al. 2023

Journal of Sensors

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Nowadays, with the advent of various communication technologies such as the internet of things (IoT), a large volume of data is produced that needs to be processed in real-time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consist of a set of dependent tasks that can be separately processed in a heterogeneous fog environment. Scheduling these tasks in a fog environment is an NP-hard problem that needs a vast amount of time and computation resources to solve, making it infeasible for real-time applications. In addition, reducing response time and energy consumption in fog computing is an essential issue that should be taken into account in task scheduling algorithms. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and leverages the benefits of simulated annealing and NSGA-III algorithms. Simulation results show that IETIF outperforms the state-of-the-art methods in terms of energy consumption, response time, and speedup.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

Nazari,

Sohrabi,

Mohammadi

et al. 2023

Journal of Sensors

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“…Thus, the amount of power consumed by virtual machines must be controlled and their efficiency must be ensured. This calculation is carried out according to Equation 14 as in [40]. In order to calculate this parameter correctly, the busy and idle states of the machines must be taken into account (Equations 15 and 16).…”

Section: 14energy Consumption Parameter Definitionmentioning

confidence: 99%

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

Anka

2024

Preprint

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Real-time processing demands are also increasing significantly with the proliferation of Internet of Things (IoT)-based smart systems. At the same time, the volume of data generated, and computational workload are also increasing significantly. In this regard, fog-cloud architectures are proposed to alleviate the excessive load on cloud servers. However, since the resources of fog nodes and the virtual machines on them are limited, efficient management mechanisms are required. As the volume and amount of data increases, computational and processing costs increase, and delays are inevitable. However, these requirements need to be resolved to increase QoS and customer satisfaction. One of the most important criteria to achieve this goal is accurate and effective task allocation and scheduling. Since the scheduling is a type of NP-hard problem, the metaheuristic approach is used. In this study, an Enhanced variant of the Sand Cat Swarm Optimization algorithm (ESCSO) is proposed to efficient scheduling according to tasks priorities and a suitable fitness function based on completion (makespan) time, energy consumption and execution cost parameters is defined. In the proposed algorithm, global search ability and population diversity of the SCSO is improved based on the chaotic map. Also, its exploration and exploitation mechanisms are enhanced based on Brownian and Levy motion strategies. Besides, the control mechanism of the phases transition is redefined to increase balancing performance. The proposed method is compared with SCSO, Improved Artificial Hummingbird Algorithm (IAHA), Discrete Moth Flame Optimization (DMFO), Enhanced Particle Swarm Optimization (EPSO), Chaotic-Whale Optimization Algorithm (CWOA), Modified Harris-Hawks Optimization (MHHO), and Hybrid Artificial Ecosystem Optimization and Salp Swarm Algorithm (AEOSSA) algorithms and analyzed on the three parameters in two different scenarios. The obtained results present that the ESCSO algorithm outperform others in all cases.

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“…Efficient scheduling would help both cloud user and service provider. Commonly used scheduling algorithms include round robin, first come first served, as well as meta-heuristic and evolutionary algorithms [15][16][17][18]. Since there are number of factors associated with scheduling, the meta-heuristic based algorithms are most prominent than other, which have the ability to work on multi-objective criterion.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective priority aware task scheduling in Fog–Cloud environment using improved meta-heuristic algorithm

Hussain,

Begh

2024

Preprint

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Internet of Things (IoT) tasks have a variety of quality of service (QoS) needs, wherein the fog-cloud computing has emerged as a promising platform for handling the tasks. As a result of its proximity to IoT devices, the fog environment offers minimal latency, but it also faces resource limitations, which is not present in cloud environment. The key obstacle of the fog-cloud setting is effectively executing tasks delegated from IoT devices, by making use of the available resources in the fog-cloud infrastructure. Hence, this research introduces a novel task scheduling approach based on the improved meta-heuristic algorithm. An improved zebra algorithm (ImZP) is proposed for performing the priority aware task scheduling. The zebra algorithm is hybridized with the mutation operation of the differential evolution algorithm (DE) for enhancing the exploration criteria to accomplish the global best solution. Besides, the acquisition of non-dominant solutions while considering the multi-objective fitness function, pareto optimal front is considered. Here, the multi-objective function based on priority, cost and execution time are considered in scheduling the task optimally. The assessment of priority aware task scheduling based on priority, availability, makespan, energy consumption, cost and success rate acquired the values of 0.9787, 0.8236, 0.1049, 0.0244, 0.1095 and 0.8238 respectively.

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IKH-EFT: An improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment

Cited by 14 publications

References 50 publications

IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

A multi-objective priority aware task scheduling in Fog–Cloud environment using improved meta-heuristic algorithm

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