Footprint-Based DIMM Hotplug

Power-efficiency has become one of the most critical concerns for HPC as we continue to scale computational capabilities. A significant fraction of system power is spent on large main memories, mainly caused by the substantial amount of DIMM standby power needed. However, while necessary for some workloads, for many workloads large memory configurations are too rich, i.e., these workloads only make use of a fraction of the available memory, causing unnecessary power usage. This observation opens new opportunities for power reduction by powering DIMMs on and off depending on the current workload. In this article, we propose footprint-based DIMM hotplug that enables a compute node to adjust the number of DIMMs that are powered on depending on the memory footprint of a running job. Our technique relies on two main subcomponents-memory footprint monitoring and DIMM management-which we both implement as part of an optimized page management system with small control overhead. Using Linux\u27s memory hotplug capabilities, we implement our approach on a real system, and our results show that our proposed technique can save 50.6-52.1 percent of the DIMM standby energy and the CPU+DRAM energy of up to 1.50 Wh for various small-memory-footprint applications without loss of performance

Fluidmem: Open Source Full Memory Disaggregation

To satisfy the performance demands of memory-intensive applications facing DRAM shortages, the focus of previous work has been on incorporating remote memory to expand capacity. However, the emergence of resource balancing as a priority for cloud computing requires the capability to dynamically size virtual machine memory up and down. Furthermore, hardware-based or kernel space implementations hamper flexibility with respect to making customizations or integrating the continuing open source advancements in software infrastructure for the datacenter. This thesis presents an architecture to meet the performance, bi-directional sizing, and flexibility challenges of memory disaggregation in the cloud. The implementation, called FluidMem, is open source software that integrates with the Linux kernel, KVM hypervisor, and multiple key-values stores. With FluidMem, a virtual machine's local memory can be transparently extended or entirely transferred to a remote key-value store. By fully implementing the dynamic aspect of data center memory disaggregation, FluidMem allows a VM's footprint to be precisely sized, expandable for application demands, but it leaves cloud operators with a non-intrusive recourse if memory becomes scarce. Full memory disaggregation in FluidMem enables the local memory footprint of a Linux VM to be scaled down to 180 pages (720 KB), yet still accept SSH logins. A user space page fault handler in FluidMem uses the userfaultfd mechanism in the Linux kernel to relocate any page of the VM to remote memory and outperforms swap-based paging. Page fault latencies via FluidMem to RAMCloud are 40% faster than the RDMA remote memory swap device in the Linux kernel, and 77% faster than SSD swap. FluidMem's remote memory expansion performance with three key-value backends is evaluated against swap-based alternatives for a MongoDB workload and the Graph500 benchmark

Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup

Energy Efficient Servers

Computer scienc

Energy Efficient Servers

Computer scienc