Energy-makespan optimization of workflow scheduling in fog–cloud computing

Ijaz, Samia; Munir, Ehsan Ullah; Ahmad, Saima; Rafique, M. Mustafa; Rana, Omer

doi:10.1007/s00607-021-00930-0

Cited by 54 publications

(29 citation statements)

References 35 publications

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“…Compared to two-tier model, this model also uses CSs at the highest-most layer to support edge resources (i.e., Fog computing). Several works considered three-tier model in the literature such as [59,65,108,116].…”

Section: Tiering Modelmentioning

confidence: 99%

“…The current literature based on the CSs' number can be divided into 1) single and 2) multiple categories. Majority of works only consider one CDC as resource provider (either as a central entity with aggregated resources or different number of VMs) to support FSs such as [14,50,59,65]. In real-world environment, different CDCs are available which can provide services with different QoS for multiple applications.…”

Section: Cloud Servers (Css)mentioning

confidence: 99%

“…Second, from the system perspective, the energy consumption of FSs should be minimized to reduce carbon emissions. This category contains any proposals considered energy consumption as an optimization parameter either from IoT devices or system perspectives such as [13,54,59,110].…”

Section: Parametersmentioning

confidence: 99%

“…Hence, cooperative Fog computing, associated protocols, and constraints require further investigation for different IoT application scenarios. Many-Tier Multiple General Hetero Multiple General Hetero ND Single [117] Two-Tier Multiple General Hetero Multiple General Hetero ND ND [90] Three-Tier Multiple General Hetero Multiple General Hetero ND Multiple [14] Three-Tier Multiple General Hetero Multiple General Hetero Single [58] Three-Tier Multiple General Hetero Multiple General Hetero Single [114] Two-Tier Multiple Vehicle Hetero Multiple General, Vehicle Hetero Intra [6] Three-Tier Multiple General Hetero Multiple General Hetero Single [16] Three-Tier Multiple General ND Multiple General Hetero Intra Single [22] Two-Tier Multiple General Homo Multiple General Hetero Intra [4] Three-Tier Multiple General Hetero Multiple General Hetero Single [50] Three-Tier Multiple General ND Multiple General Hetero Single [59] Three-Tier Multiple General Hetero Multiple General Hetero Single [61] Two-Tier Multiple General ND Multiple General Hetero Intra [65] Three-Tier Multiple General Hetero Multiple General Hetero Single [69] Two-Tier Multiple General Hetero Multiple General Hetero Intra [82] Three-Tier Multiple General Hetero Multiple BS Hetero Single [95] Three-…”

mentioning

confidence: 99%

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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

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Section: Tiering Modelmentioning

confidence: 99%

Section: Cloud Servers (Css)mentioning

confidence: 99%

Section: Parametersmentioning

confidence: 99%

mentioning

confidence: 99%

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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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“…Sampaio et al [99] introduced an autonomic system to perform management of energy consumption in IoT devices and FC, including an advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. Ijaz et al [100] investigated workflow scheduling in fog-cloud environments to provide an energy efficient task schedule within acceptable application completion times. Lai et al [101] aimed to minimize the total power consumption of a dense small cell network while offering the quality of service to all its user equipment.…”

Section: Miscellaneous Algorithmsmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multisink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multisink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

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Multi‐objectivetask scheduling in fog computing using improved gaining sharing knowledge based algorithm

Krishnan¹,

Thiyagarajan²

2022

Concurrency and Computation

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Summary In the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

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Energy-makespan optimization of workflow scheduling in fog–cloud computing

Cited by 54 publications

References 35 publications

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

Multi‐objectivetask scheduling in fog computing using improved gaining sharing knowledge based algorithm

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