Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments
| Autor: | Catalin Negru, Roxana-Gabriela Stan, Lidia Bajenaru, Florin Pop |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Schedule
task scheduling Computer science Distributed computing Symmetric multiprocessor system TP1-1185 Workload performance evaluation framework Biochemistry Turnaround time Article Analytical Chemistry Task (project management) Scheduling (computing) Resource (project management) Computer Simulation Electrical and Electronic Engineering Instrumentation Ecosystem Job shop scheduling Chemical technology Cloud Computing heterogeneous computing Atomic and Molecular Physics and Optics Shortest job next hybrid edge–cloud environments Algorithms |
| Zdroj: | Sensors Volume 21 Issue 17 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 5906, p 5906 (2021) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21175906 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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