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1. Pufferfish: Container-driven Elastic Memory Management for Data-intensive Applications

   https://doi.org/https://doi.org/10.1145/3357223.3362730  

   Chen, Wei; Pi, Aidi; Wang, Shaoqi; Zhou, Xiaobo (November 2019, ACM SoCC '19: Proceedings of the ACM Symposium on Cloud Computing)

   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. 

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2. OS-Augmented Oversubscription of Opportunistic Memory with a User-Assisted OOM Killer

   https://doi.org/https://doi.org/10.1145/3361525.3361534  

   Chen, Wei; Pi, Aidi; Wang, Shaoqi; Zhou, Xiaobo (December 2019, ACM Middleware '19: Proceedings of the 20th International Middleware Conference)

   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%. 

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3. OS-Augmented Oversubscription of Opportunistic Memory with a User-Assisted OOM Killer

   https://doi.org/doi.org/10.1145/3361525.3361534  

   Chen, Wei; Pi, Aidi; Wang, Shaoqi; Zhou, Xiaobo (December 2019, Middleware '19: ACM Proceedings of the 20th International Middleware Conference)

   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%. 

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4. Multiverse: Dynamic VM Provisioning for Virtualized High Performance Computing Clusters

   https://doi.org/10.1109/CCGrid49817.2020.00-80  

   Gunasekaran, Jashwant Raj; Cui, Michael; Thinakaran, Prashanth; Simons, Josh; Kandemir, Mahmut T.; Das, Chita R. (May 2020, 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID),)

   Traditionally, HPC workloads have been deployed in bare-metal clusters; but the advances in virtualization have led the pathway for these workloads to be deployed in virtualized clusters. However, HPC cluster administrators/providers still face challenges in terms of resource elasticity and virtual machine (VM) provisioning at large-scale, due to the lack of coordination between a traditional HPC scheduler and the VM hypervisor (resource management layer). This lack of interaction leads to low cluster utilization and job completion throughput. Furthermore, the VM provisioning delays directly impact the overall performance of jobs in the cluster. Hence, there is a need for effectively provisioning virtualized HPC clusters, which can best-utilize the physical hardware with minimal provisioning overheads.Towards this, we propose Multiverse, a VM provisioning framework, which can dynamically spawn VMs for incoming jobs in a virtualized HPC cluster, by integrating the HPC scheduler along with VM resource manager. We have implemented this framework on the Slurm scheduler along with the vSphere VM resource manager. In order to reduce the VM provisioning overheads, we use instant cloning which shares both the disk and memory with the parent VM, when compared to full VM cloning which has to boot-up a new VM from scratch. Measurements with real-world HPC workloads demonstrate that, instant cloning is 2.5× faster than full cloning in terms of VM provisioning time. Further, it improves resource utilization by up to 40%, and cluster throughput by up to 1.5×, when compared to full clone for bursty job arrival scenarios. 

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5. Making Serverless Pay-For-Use a Reality with Leopard

   Cao, Tingjia; Arpaci-Dusseau, Andrea C; Arpaci-Dusseau, Remzi H; Caraza-Harter, Tyler (April 2025, USENIX NSDI)

   USENIX (Ed.)

   Serverless computing has gained traction due to its event-driven architecture and “pay for use” (PFU) billing model. However, our analysis reveals that current billing practices do not align with true resource consumption. This paper challenges the prevailing SLIM (static, linear, interactive-only model) assumptions that underpin existing billing models, demonstrating that current billing does not realize PFU for realistic workloads. We introduce the Nearly Pay-for-Use (NPFU) billing model, which accommodates varying CPU and memory demands, spot cores, and preemptible memory. We also introduce Leopard, an NPFU-based serverless platform that integrates billing awareness into several major subsystems: CPU scheduler, OOM killer, admission controller, and cluster scheduler. Experimental results indicate that Leopard benefits both providers and users, increasing throughput by more than 2x and enabling cost reductions. 

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