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. 2021 Sep 2;21(17):5906.
doi: 10.3390/s21175906.

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

Roxana-Gabriela Stan  1 , Lidia Băjenaru  1   2 , Cătălin Negru  1 , Florin Pop  1   2
Affiliations

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

Roxana-Gabriela Stan et al. Sensors (Basel). .

Abstract

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge-cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge-cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

Keywords: heterogeneous computing; hybrid edge–cloud environments; performance evaluation framework; task scheduling.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scheduling in an edge–cloud collaborative computing environment.
Figure 2
Figure 2
Execution flow of a read task on a computational resource.
Figure 3
Figure 3
Execution flow of a write task on a computational resource.
Figure 4
Figure 4
Percentage of failed tasks for Experiment 1 (2000 tasks).
Figure 5
Figure 5
Percentage of failed tasks for Experiment 2 (8000 tasks).
Figure 6
Figure 6
Datacenters’ resource utilization for Experiment 1 (2000 tasks).
Figure 7
Figure 7
Datacenters’ resource utilization for Experiment 2 (8000 tasks).
See this image and copyright information in PMC

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