Failure-resilient DAG task scheduling in edge computing

Cai, Lingfeng; Wei, Xianglin; Xing, Changyou; Zou, Xia; Zhang, Guomin; Wang, Xiulei

doi:10.1016/j.comnet.2021.108361

Cited by 28 publications

(21 citation statements)

References 25 publications

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“…The CCR defines whether an application on average is more 1) computation-intensive [42,101,132] or 2) communication-intensive [13,49,116]. Besides, some works consider a range of applications to cover both computationintensive and communication-intensive applications, to which we refer as 3) hybrid [16,40].…”

Section: Communication-computation Ratio (Ccr)mentioning

confidence: 99%

“…So, application components faced with failure should be re-executed. Some works consider failure recovery mechanisms in their decision engines such as [16,38,40].…”

Section: Advanced Featuresmentioning

confidence: 99%

“…It directly affect the service startup time and the decision overhead of each technique. Based on the current literature, we classify the TC into 1) Low the solution of optimization problem can be obtained in polynomial time where the maximum power of variable is equal or less than two (i.e., 𝑂 (𝑛 2 )) [9,16,39], 2) medium where the time complexity is polynomial and the maximum power is less than or equal to 3 (i.e., 𝑂 (𝑛 3 )) [13,70,104], and 3) High for exponential TC and polynomials with high maximum power [18,25,128].…”

Section: Time Complexity (Tc)mentioning

confidence: 99%

“…Almost 60% of works consider the three-tier model and many-tier models for the organization of end-users and resources. Not only do these works consider real-time applications, but also some of them assume both real-time and computation-intensive applications, such as [16,39,124]. This is mainly because these works use CSs as a backup plan for more computation-intensive applications or when the number of incoming IoT requests increases and the FSs cannot solely manage the incoming requests.…”

mentioning

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%

See 4 more Smart Citations

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.

show abstract

Section: Communication-computation Ratio (Ccr)mentioning

confidence: 99%

“…So, application components faced with failure should be re-executed. Some works consider failure recovery mechanisms in their decision engines such as [16,38,40].…”

Section: Advanced Featuresmentioning

confidence: 99%

Section: Time Complexity (Tc)mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

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

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

View full text Add to dashboard Cite

show abstract

Modeling the Green Cloud Continuum: integrating energy considerations into Cloud–Edge models

Patel,

Townend,

Singh

et al. 2024

Cluster Comput

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The energy consumption of Cloud–Edge systems is becoming a critical concern economically, environmentally, and societally; some studies suggest data centers and networks will collectively consume 18% of global electrical power by 2030. New methods are needed to mitigate this consumption, e.g. energy-aware workload scheduling, improved usage of renewable energy sources, etc. These schemes need to understand the interaction between energy considerations and Cloud–Edge components. Model-based approaches are an effective way to do this; however, current theoretical Cloud–Edge models are limited, and few consider energy factors. This paper analyses all relevant models proposed between 2016 and 2023, discovers key omissions, and identifies the major energy considerations that need to be addressed for Green Cloud–Edge systems (including interaction with energy providers). We investigate how these can be integrated into existing and aggregated models, and conclude with the high-level architecture of our proposed solution to integrate energy and Cloud–Edge models together.

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A hybrid genetic-based task scheduling algorithm for cost-efficient workflow execution in heterogeneous cloud computing environment

Khademi Dehnavi,

Broumandnia,

Hosseini Shirvani

et al. 2024

Cluster Comput

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Failure-resilient DAG task scheduling in edge computing

Cited by 28 publications

References 25 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

Modeling the Green Cloud Continuum: integrating energy considerations into Cloud–Edge models

A hybrid genetic-based task scheduling algorithm for cost-efficient workflow execution in heterogeneous cloud computing environment

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