2024/11/25 by Demian Hespe, Lukáš Hübner, Hespe, Demian +5
Computer Science · #Cloud Computing and Resource Management #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2411.16255
openalex publication_date 2024/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Supercomputers getting ever larger and energy-efficient is at odds with the reliability of the used hardware. Thus, the time intervals between component failures are decreasing. Contrarily, the latencies for individual operations of coarse-grained big-data tools grow with the number of processors. To overcome the resulting scalability limit, we need to go beyond the current practice of interoperation checkpointing. We give first results on how to achieve this for the popular MapReduce framework where huge multisets are processed by user-defined mapping and reducing functions. We observe that the full state of a MapReduce algorithm is described by its network communication. We present a low-overhead technique with no additional work during fault-free execution and the negligible expected relative communication overhead of 1/(p-1) on p PEs. Recovery takes approximately the time of processing 1/p of the data on the surviving PEs. We achieve this by backing up self-messages and locally storing all messages sent through the network on the sending and receiving PEs until the next round of global communication. A prototypical implementation already indicates low overhead <4 % during fault-free execution.