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Exploiting opportunistic memory by oversubscription is an appealing approach to improving cluster utilization and throughput. In this paper, we find the efficacy of memory oversubscription depends on whether or not the oversubscribed tasks can be killed by an OutOf Memory (OOM) killer in a timely manner to avoid significant memory thrashing upon memory pressure. However, current approaches in modern cluster schedulers are actually unable to unleash the power of opportunistic memory because their user space OOM killers are unable to timely deliver a task killing signal to terminate the oversubscribed tasks. Our experiments observe that a user space OOM killer fails to do that because of lacking the memory pressure knowledge from OS while the kernel space Linux OOM killer is too conservative to relieve memory pressure. In this paper, we design a user-assisted OOM killer (namely UA killer) in kernel space, an OS augmentation for accurate thrashing detection and agile task killing. To identify a thrashing task, UA killer features a novel mechanism, constraint thrashing. Upon UA killer, we develop Charon, a cluster scheduler for oversubscription of opportunistic memory in an on-demand manner. We implement Charon upon Mercury, a state-of-the-art opportunistic cluster scheduler. Extensive experiments with a Google trace in a 26-node cluster show that Charon can: (1) achieve agile task killing, (2) improve the best-effort job throughput by 3.5X over Mercury while prioritizing the production jobs, and (3) improve the 90th job completion time of production jobs over Kubernetes opportunistic scheduler by 62%. 

Exploiting opportunistic memory by oversubscription is an appealing approach to improving cluster utilization and throughput. In this paper, we find the efficacy of memory oversubscription depends on whether or not the oversubscribed tasks can be killed by an OutOfMemory (OOM) killer in a timely manner to avoid significant memory thrashing upon memory pressure. However, current approaches in modern cluster schedulers are actually unable to unleash the power of opportunistic memory because their user space OOM killers are unable to timely deliver a task killing signal to terminate the oversubscribed tasks. Our experiments observe that a user space OOM killer fails to do that because of lacking the memory pressure knowledge from OS while the kernel space Linux OOM killer is too conservative to relieve memory pressure. In this paper, we design a user-assisted OOM killer (namely UA killer) in kernel space, an OS augmentation for accurate thrashing detection and agile task killing. To identify a thrashing task, UA killer features a novel mechanism, constraint thrashing. Upon UA killer, we develop Charon, a cluster scheduler for oversubscription of opportunistic memory in an on-demand manner. We implement Charon upon Mercury, a state-of-the-art opportunistic cluster scheduler. Extensive experiments with a Google trace in a 26-node cluster show that Charon can: (1) achieve agile task killing, (2) improve the best-effort job throughput by 3.5X over Mercury while prioritizing the production jobs, and (3) improve the 90th job completion time of production jobs over Kubernetes opportunistic scheduler by 62%. 

Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution. 

Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution. 

Logging is a universal approach to recording important events in system workflows of distributed systems. Current log analysis tools ignore the semantic knowledge that is key to workflow construction and analysis. In addition, they focus on infrastructure-level distributed systems. Because of fundamental differences in log features, they are ineffective in distributed data analytics systems. This paper proposes IntelLog, a semantic-aware non-intrusive workflow reconstruction tool for distributed data analytics systems. It is capable of building hierarchical relationships between components and events from logs generated by the targeted systems with little or even no domain knowledge. Leveraging natural language processing, IntelLog automatically extracts and formats semantic information in each log message, including system events, identifiers, locality information, and metrics values. It builds a graph to represent the hierarchical relationship of components in the targeted system via nomenclature conventions. We implement IntelLog for Hadoop MapReduce, Spark and Tez. Evaluation results show that IntelLog provides a fine-grained view of the system workflows with semantics. It outperforms existing tools in automatically detecting anomalies caused by real-world problems, misconfigurations and system bugs. Users can query the formatted semantic knowledge to understand and further troubleshoot the systems.