DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments

Sriraghavendra, Meeniga; Chawla, Priyanka; Wu, Huaming; Gill, Sukhpal Singh; Buyya, Rajkumar

doi:10.1007/978-981-16-3448-2_2

Cited by 27 publications

(23 citation statements)

References 33 publications

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“…The Memetic algorithm is presented for solving the container/ service placement in their proposed architecture. Sriraghavendra et al [43] investigate QoS-aware service placement in a dynamic Fog-Cloud environment. They propose a deadline-oriented service placement (DoSP) strategy which uses a genetic algorithm to host IoT services on suitable Fog and Cloud nodes.…”

Section: Related Workmentioning

confidence: 99%

“…-The Minimum Delay Provider First (MD1): This algorithm selects the provider which offers the minimum delay for each service placement request. -Genetic Algorithm (GA): GA is one of the most used meta-heuristic techniques for the SPP in Fog and Cloud environments, e.g., [39,28,32,43]. Here, we have implemented our custom GA according to the OF provided in eq.…”

Section: Baseline Algorithmsmentioning

confidence: 99%

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A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Azizi

Farzin

Shojafar

et al. 2023

Preprint

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Provisioning services for Internet of Things (IoT) devices leads to severalchallenges: heterogeneity of IoT devices, varying Quality of Services (QoS)requirements, and increasing availability of both Cloud and Fog resources. Thelast of these is most significant to cope with the limitations of Cloud infrastructureproviders (CIPs) for latency-sensitive services. Many Fog infrastructure providers(FIPs) have recently emerged and their number is increasing continually. FLEX isproposed in this work as a platform for selecting a location for service placementin a multi-Fog and multi-Cloud environment. For each service, FLEX broadcastsservice requirements to the resource managers (RMs) of the available Fog andCloud service providers and then selects the most suitable provider for that service.FLEX is scalable and flexible as it leaves it up to the RMs to have their ownpolicy for the placement of submitted services. Service placement and resource selectionhas been formulated as an optimization problem and an efficient heuristicalgorithm is proposed to solve it. Results show that the proposed algorithm canbe used across both Cloud and Fog-based providers.

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Section: Related Workmentioning

confidence: 99%

Section: Baseline Algorithmsmentioning

confidence: 99%

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Azizi

Farzin

Shojafar

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Information details and the patient's status are sent to this layer for needed actions. The fog layer can retrieve the information stored in the cloudayer to be studied and analyzed [15].…”

Section: ) Fog Computing (Fc)mentioning

confidence: 99%

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Shanshool¹,

Abbas²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The Internet-of-Things (IoT) is an important technology and is considered the future of the Internet. Healthcare is described as one of the important areas in IoT used for remote patient monitoring. Real-time remote monitoring health applications are important as delays in data transfer between the cloud, and the application may be unacceptable. Fog computing refers to a geographically distributed computing system with several devices connected to the same network to achieve flexible and collaborative computation, storage, and communication services. Fog computing is mainly used for efficient data processing between sensors and cloud computing as it reduces the volume of data exchanged between sensors and the cloud, thereby improving the whole system's efficiency. Wireless sensor networks (WSN) are also used in health monitoring systems to simultaneously transfer huge data volumes (of different priority levels and length values) to the fog computing system. Hence, there is a need to appropriately implement a task scheduling mechanism that can accurately prioritize tasks irrespective of their length. This study aims to systematically review the existing fog computing technologies in the Internet of things HealthCare (IoTH) systems and improve the performance of the available static task scheduling algorithms using the Tasks Classification (TC) method where task importance is paramount. The performance of the suggested approach was evaluated based on the Max-Min scheduling algorithm (SA).

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“…Deadlines and priority of applications are considered for fog placement in Reference 17. Sriraghavendra et al 18 address service placement in low cost fog‐cloud without degrading response time by maintaining the deadline constraints. A rank based service deployment is proposed by Munusamy et al, 19 where tasks are analyzed by risk based classification strategy and given ranks to the tasks by maintaining user satisfaction ratio.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical fuzzy‐based Quality of Experience (QoE)‐aware application placement in fog nodes

et al. 2022

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Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in-time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE-aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic.The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.

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DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments

Cited by 27 publications

References 33 publications

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Hierarchical fuzzy‐based Quality of Experience (QoE)‐aware application placement in fog nodes

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