Deadline-aware Scheduling in Cloud-Fog-Edge Systems

Postoaca, Andrei-Vlad; Negru, Cătălin; Pop, Florin

doi:10.1109/ccgrid49817.2020.00-21

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…However, the proposed method only minimally addresses saving energy in fog environments. The authors of [21] proposed a deadline-aware mechanism that plays an intermediate role for processing tasks in a fog-cloud environment. In addition, the mechanism focuses on the efficient utilization of resources.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs. This approach is particularly relevant in scenarios where latency, resource constraints, and cost considerations are crucial factors for the deployment of applications. In this study, we propose a hybrid approach that combines a genetic algorithm (GA) with the Flamingo Search Algorithm (FSA) to place application modules while minimizing cost. We consider four cost-types for application deployment: Computation, communication, energy consumption, and violations. The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system. An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches. The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio (TGR) and total cost.

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Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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“…Postoaca et al [15] presented a deadline-aware FOG-Scheduler for cloud edge applications. The job queue is ordered for context based on deadlines.…”

Section: Hadoop Yarn Scheduling Challenges In Resource-constrained Cl...mentioning

confidence: 99%

“…The DQ-DCWS is based on dynamic programming in calculating the length of the edge in the DAG and scheduling tasks along the optimal path. In [15], Postoaca et al presented a deadline-aware fog Scheduler (FOG) for cloud edge applications. The job queue is ordered for context based on deadlines.…”

Section: Introductionmentioning

confidence: 99%

“…These strategies aim to optimize data layout in Hadoop by maximizing parallelism while accommodating the resource constraints of frugal SBC nodes in the Hadoop cluster. To validate the proposed AMS-ERA, we deploy a heterogeneous SBC-based cluster consisting of 11 physical nodes and execute the Hadoop benchmark tests to analyze the performance of the proposed technique against Hadoop-Fair, FOG [15], and IDaPS [16] scheduling strategies. The results showcase a notable enhancement in performance with our proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters

Qureshi

2024

Electronics

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Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is load balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory, and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG, and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4%, and 7.6%, respectively, using terasort and wordcount benchmarks.

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“…We propose BDPS, a delay optimization scheme to run with in-memory Spark processing to reduce the Apache Hadoop [34] extra delay problem for the verification, validation, and execution of files caused by distribution of data processing. "BDPS" can manage data processing [35] for architectures or massive storage modules to fast process scheduling to run different frame intervals in a single frame slot and minimize the end-to-end hop latency problem.…”

Section: Introductionmentioning

confidence: 99%

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

et al. 2021

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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

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Deadline-aware Scheduling in Cloud-Fog-Edge Systems

Cited by 6 publications

References 13 publications

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

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