Energy Efficient Workflow Scheduling Algorithm for Latency-Sensitive Applications using Cloud-Fog Collaboration

The objectives of most studies are aimed at employing fog computing (FC) as an effective support to cloud computing (CC) in order to monitor sensor data and their associated necessities via the rapid advancements in Internet of Things (IoT). FC is completely dedicated to IoT sensor systems and deliv...

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Vydáno v:International Symposium on Advanced Networks and Telecommunication Systems s. 252 - 257
Hlavní autoři: Shukla, Prashant, Pandey, Sudhakar
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.12.2023
Témata:
Cloud computing
Cloud-Fog Collaboration
Collaboration
Hybrid-metaheuristic
Internet of Things
Multi-Objective Optimization
Scheduling algorithms
Scientific Workflows
Sensors
Task Scheduling Algorithms
Telecommunication network reliability
Telecommunications
ISSN:2153-1684
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Shrnutí:The objectives of most studies are aimed at employing fog computing (FC) as an effective support to cloud computing (CC) in order to monitor sensor data and their associated necessities via the rapid advancements in Internet of Things (IoT). FC is completely dedicated to IoT sensor systems and delivers CC based computing, networking, and storage services. The cloud-Fog Collaboration (CFC) Environment will be more suitable for real-time IoT data sensing and workflow applications where fast and reliable internet connectivity is available. Workflow Scheduling means efficiently assigning the interdependent tasks to available computing resources. The workflow scheduling techniques play a significant role in minimizing the latency in Heterogeneous Computing systems (HCS). In this paper, we present a novel Hybrid-metaheuristics Multi-objective based Workflow Scheduling Algorithm (HMWSA) by combining Harmony Search (HS) and Genetic Algorithm (GA) for optimizing workflow scheduling in heterogeneous CFC. The proposed minimization objective function takes into account goals like makespan, energy usage, and cost. To preserve the precedence relationship among tasks, a topologically sorted execution sequence is constructed. Extensive testing results show that our proposed approach yields substantially better cost-makespan trade-offs while significantly reducing energy consumption than other existing algorithms.
ISSN:2153-1684
DOI:10.1109/ANTS59832.2023.10469573