Straggler Node Detection and Prevention using supervised Job Scheduling Approach
Load balancing the distributed resource management and matchmaking techniques to meet the tangible job to an actual resource using scheduling techniques. Distributed data processing systems expedite jobs by turning them back into distributed processing tasks. Nevertheless, the system performance can be enhanced by relatively slow or straggler functions than with the actual charge on a data cluster, resulting in delayed work completion and unsustainable resource consumption. Established straggler avoidance methods wait to identify stragglers and then restart them, slowing the identification of stragglers and wasting money. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. System developed different frameworks for every network and workflow to capture node and configuration heterogeneity, involving the time-consuming compilation of important training data. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead to eliminate of nodes. The major benefit of this research, the system can handle massive data nodes with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naïve inter learning frameworks.