Evaluating Task-Level CPU Efficiency for Distributed Stream Processing Systems

Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity scale. As a result, effective performance management to ensure that these meet the required service level objectives (SLOs) is becoming increasingly difficult. A key factor consider when evaluating of DSPS CPU efficiency, which ratio workload processed by system resources invested. In this paper, we argue developing new tools for creating DSPSs can fulfill SLOs while using minimal crucial. This especially significant edge computing situations where limited large cloud deployments conserving power reducing expenses essential. To address challenge, present novel task-level approach measuring efficiency DSPSs. Our supports various streaming frameworks, adaptable, comes with overheads. enables developers understand different at granular provides insights were not previously possible.