Citation:

Sai Sandeep Ogety. "Dependent Tasks Verification-Aware Task Scheduling and Resource Allocation in Cloud Devops Using Obl-Fuzzy and Smrnn" International Research Journal of Economics and Management Studies, Vol. 4, No. 3, pp. 266-281, 2025.

Abstract:

In Cloud Computing (CC), Task Scheduling (TS) involves assigning tasks to suitable resources, while Resource Allocation (RA) focuses on efficiently distributing cloud resources for optimal utilization. Nevertheless, the prevailing studies didn’t perform dependent task verification, which might increase the Continuous Integration/Continuous Deployment (CI/CD) cycles. Thus, Offset Broken Line– Fuzzy (OBL-Fuzzy) and Sinusoidal Maxsig Recurrent Neural Network (SMRNN)-enabled dependent tasks verification-aware TS and RA in cloud Development Operations (DevOps) are presented in this paper. Primarily, the graph is constructed for the DevOps application by using Exponential Coffman Kahn's Directed Acyclic Graph (ECK-DAG). Then, workflow assignment, attribute extraction, clustering by Elbow Cohen's Density-Based Spatial Clustering of Applications with Noise (ECDBSCAN), workflow fragmentation, and Load balancing by Addax Rastrigrin Optimization Algorithm (AROA) are performed. Further, the Merkle tree is constructed for the fragmented workflows using HEX-Gini-BLOOM Merkle-Tree (HGB-MT). Afterward, the constructed Merkle tree is checked with the constructed graphs. The tasks are scheduled if it is verified; otherwise, the task request is rejected. Then, the dependent tasks' status is checked in the HGB-MT; if any dependent tasks are pending, then the least priority is given to that task. Next, by employing OBL-Fuzzy, the tasks are scheduled. Finally, by using AROA, the resources are allocated. As per the results, the proposed model achieved a high accuracy of 99%.

References:

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Keywords:

Task scheduling, Resource Allocation, Development Operations (DevOps), Continuous Integration/Continuous Deployment (CI/CD), Virtual Machine (VM) load prediction, Elbow Cohen's Density-Based Spatial Clustering of Applications with Noise (ECDBSCAN), and Load balancing.