Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney, Prateeksha; Simmhan, Yogesh

doi:10.1002/spe.2699

Cited by 48 publications

(31 citation statements)

References 164 publications

(781 reference statements)

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“…Varshney and Simmhan [23] surveys the scheduling in the ecosystem by providing a taxonomy on resource allocation. The study shows the taxonomy based on different metrics such as type of scheduling, techniques of scheduling, pricing, and Quality of Service (QoS)…”

Section: Iot and Fog Applications And Architecturesmentioning

confidence: 99%

“…Gil et al [12] IoT applications smart city Naha et al [14] IoT applications and architectures Fog layer taxonomy Mukherjee et al [16] IoT applications ecosystem architecture Perera et al [13] IoT applications ecosystem protocols Al-Fuqaha et al [17] IoT communication network protocols Yassein et al [18] IoT communication MQTT protocol Maag et al [15] air pollution application IoT sensor layer Maheswari et al [19] IoT data process security Hathaliya and Tanwar [20] IoT healthcare application security Markus and Kertesz [21] IoT simulation software simulator Yangui [22] IoT application IoT services Varshney and Simmhan [23] IoT resource allocation scheduling…”

Section: Topic Key Aspectmentioning

confidence: 99%

See 1 more Smart Citation

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Chegini

Naha

Mahanti

et al. 2021

IoT

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The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.

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Section: Iot and Fog Applications And Architecturesmentioning

confidence: 99%

Section: Topic Key Aspectmentioning

confidence: 99%

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Chegini

Naha

Mahanti

et al. 2021

IoT

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“…Ignoring partial processing by tier 2 leads to considerable network delay, application deadline failure, and increased data processing workload in tier 3. Generally, by moving from the bottom to top layers, the hardware resources (processing, [8] memory, cache, bandwidth capacity) increases. As a negative effect, the network link delay rises, too.…”

Section: Introductionmentioning

confidence: 99%

A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

Hasibi¹,

Kashi²

2021

Preprint

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Fog computing brings cloud capabilities closer to the Internet of Things (IoT) devices. IoT devices generate a tremendous amount of stream data towards the cloud via hierarchical fog nodes. To process data streams, many Stream Processing Engines (SPEs) have been developed. Without the fog layer, the stream query processing executes on the cloud, which forwards much traffic toward the cloud. When a hierarchical fog layer is available, a complex query can be divided into simple queries to run on fog nodes by using distributed stream processing. In this paper, we propose an approach to assign stream queries to fog nodes using container technology. We name this approach Stream Queries Placement in Fog (SQPF). Our goal is to minimize end-to-end delay to achieve a better quality of service. At first, in the emulation step, we make docker container instances from SPEs and evaluate their processing delay and throughput under different resource configurations and queries with varying input rates. Then in the placement step, we assign queries among fog nodes by using a genetic algorithm. The practical approach used in SQPF achieves a near-the-best assignment based on the lowest application deadline in real scenarios, and evaluation results are evidence of this goal.

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“…Generally, task and workflow scheduling are already proven to be NP-hard problems in fog and cloud computing environments [20]. Several scheduling frameworks such as [21] have been investigated and introduced to schedule IoT tasks on fog computing's virtual resources. However, only a few scheduling schemes are proposed for fog computing environments that have considered security issues and especially DDoS attacks.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Scientific Workflows in Multi-Fog Environments Using Markov Models and a Hybrid Salp Swarm Algorithm

Ahmed

et al. 2020

IEEE Access

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Security attacks are a nightmare to many computing environments such as fog computing, and these attacks should be addressed. Fog computing environments are vulnerable to various kinds of DDoS attacks, which can keep fog resources busy. Typically in such attacks, fog environments often have less available resources, and this can have a negative impact on the scheduling of Internet of Things (IoT) submitted workflows. However, most of the existing scheduling schemes do not consider the effect of DDoS attacks in the scheduling process, and this can increase the number of deadline missed workflows and offloaded tasks on the cloud. For dealing with these issues, a hybrid optimization algorithm is proposed, comprising both Particle Swarm Optimization (PSO) and Salp Swarm algorithm (SSA), to solve the workflow scheduling problem in multiple fog computing environments. Two discrete-time Markov chain models are proposed for each fog computing environment to address the effects of DDoS attacks on them. Our first Markov model computes the average available network bandwidth for each fog and the second Markov model finds the average number of available virtual machines (VMs) for each fog; the models address different levels of DDoS attacks. Extensive simulations show that by predicting the effects of DDoS attacks on fog environments, the proposed approach can effectively mitigate the number of offloaded tasks on cloud data centers and is able to reduce the number of the deadline missed workflows.

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Characterizing application scheduling on edge, fog, and cloud computing resources

Cited by 48 publications

References 164 publications

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

Scheduling of Scientific Workflows in Multi-Fog Environments Using Markov Models and a Hybrid Salp Swarm Algorithm

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