Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer
Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity. However, there are significant challenges when it comes to distributing intricate tasks, such as workflows structured in a Directed Acyclic Graph (DAG), across cloud resources. These challenges include increased energy consumption and delays experienced by end-users due to geographical disparities between users and service providers. To address these issues, this research work introduces an innovative solution that utilizes a multi-objective Grey Wolf Optimizer (GWO) algorithm for optimizing the allocation of complex workflows. The primary objective of this workflow optimization is to achieve the highest possible processor utilization ratio while adhering to latency constraints. This approach consists of a two-step process. In the …
Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity.
Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-…