Analytical model for task offloading in a fog computing system with batch-size-dependent service

Nikoui, Tina Samizadeh; Rahmani, Amir Masoud; Balador, Ali; Javadi, Hamid Haj Seyyed

doi:10.1016/j.comcom.2022.04.010

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…In Reference 30, an impatience‐based queuing strategy is introduced to enable users to choose between MEC offloading and local computation to minimize the risk of latency outage. Similarly, Reference 31 proposed a novel analytical model to investigate batch queuing systems and their impact on performance. At the same time, 32 employed queuing model to evaluate job forwarding probability and devise optimal work allocation strategies.…”

Section: Related Workmentioning

confidence: 99%

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Yadav

Vidyarthi

2023

Concurrency and Computation

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In a hierarchical edge‐fog‐cloud architecture, edge devices possess limited resources and energy. To contain with, it can offload some tasks generated by the Internet of Things (IoT) to the fog and cloud. Several factors influence this task‐offloading decision, including hardware features, network conditions, and application characteristics. Most of the research studies, in task offloading systems, have confined to changing parameter values, whereas very few have considered fuzzy‐based dynamic approaches for resource allocation. This work proposes a fuzzy‐based task offloading technique (FBOT) for scheduling the tasks (both compute and data‐intensive) to the appropriate nodes of the fog‐cloud system. The proposed method incorporates a vague logic‐based fuzzy task scheduler for scheduling various tasks at the fog layer. This helps to reduce the waiting time for the compute‐intensive tasks and minimizes the starvation problem of data‐intensive tasks. In addition, the proposed technique decreases the task completion time and selects the best computing nodes for each tasks. To validate the performance of the proposed technique, extensive simulation studies have been carried out on various parameters. Compared with the baseline algorithms, the results show that the proposed method offers an average improvement of 30% in terms of the total time of the task.

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Section: Related Workmentioning

confidence: 99%

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Yadav

Vidyarthi

2023

Concurrency and Computation

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“…It is worth mentioning that a significant contribution to the study of isolated queues with group customer service was made by S. Chakravarthy; see, e.g., [8][9][10][11][12][13]. Mention also the papers [14][15][16][17]. In [14][15][16], the MAP process or its generalizations is supposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [14][15][16], the MAP process or its generalizations is supposed. In [17], dependence of a group service time on its size is examined and applications to fog and cloud computing systems are discussed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tandem Queue with Multi-Server Stages and Group Service at the Second Stage

Dudin,

Dudina,

Dudin

2024

Axioms

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In this paper, we consider a tandem dual queuing system consisting of multi-server stages. Stage 1 is characterized by an infinite buffer, one-by-one service of customers, and an exponential distribution of service times. Stage 2 is characterized by a finite buffer and a phase-type distribution of service times. Service at Stage 2 is provided to groups of customers. The service time of a group depends on the size of the group. The size is restricted by two thresholds. The waiting time of a customer at each stage is limited by a random variable with an exponential distribution, with the parameter depending on the stage. After service at Stage 1, a customer can depart from the system or try to enter Stage 2. If the buffer at this stage is full, the customer is either lost or returns for service at Stage 1. Customer arrivals are described by the versatile Markov arrival process. The system is studied via consideration of a multi-dimensional continuous-time Markov chain. Numerical examples, which highlight the influence of the thresholds on the system performance measures, are presented. The possibility of solving optimization problems is illustrated.

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“…The internet of things (IoT) and cloud environments generate an enormous number of geographical data. The harnessing technology that analyzes geospatial data and provides it to the cloud storage system via fog/mist nodes is referred to as fog and mist computing [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

Panigrahi,

Goswami,

Apat

et al. 2023

Mathematics

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The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

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Analytical model for task offloading in a fog computing system with batch-size-dependent service

Cited by 8 publications

References 23 publications

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Analysis of Tandem Queue with Multi-Server Stages and Group Service at the Second Stage

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

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