Fault tolerance is becoming increasingly important for upcoming exascale systems,
supporting distributed data processing, due to the expected decrease in the Mean Time …

State-of-the-art distributed stream processing systems such as Apache Flink and Storm have
recently included checkpointing to provide fault-tolerance for stateful applications. This is a …

Nowadays various distributed stream processing systems (DSPSs) are employed to process
the ever-expanding real-time data. The DSPSs are highly susceptible to system failure, and …

Distributed Stream Processing systems are becoming an increasingly crucial aspect of Big
Data processing platforms as customers grow ever more reliant on their capacity to deliver …

For a stream processing system that uses checkpoints as a fault-tolerant method, selecting
the appropriate checkpoint period is the key to ensuring the efficient operation of streaming …

When running deep learning training jobs, in order to prevent training loss due to
softwarelhardware failures, a checkpointing mechanism is usually used to periodically store …

With the increase in computation and data intensive needs along with the real-time
requirements in the big data era, a distributed framework that can handle parallel processing …

Due to the increase and complexity of computer systems, reducing the overhead of fault
tolerance techniques has become important in recent years. One technique in fault tolerance …

Real-time embedded systems with safety-critical functions must often share a limited number
of computational resources. Scheduling models within the Hierarchical Real-Time …

Summary Distributed Stream Processing Systems (DSPS) are very popular to process
unbounded data streams in real‐time. Low processing latency is a fundamental requirement …