Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Server virtualization and workload consolidation enable multiple workloads to share a single physical server, resulting in significant energy savings and utilization improvements. The shift of physical server architectures to NUMA and the increasing popularity of scale-out cloud applications undermine workload consolidation efficiency and result in overall system degradation. In this work,  characterize the consolidation of cloud workloads on NUMA virtualized systems, estimate four different sources of architecture overhead, and explore optimization opportunities beyond the default NUMA-aware hypervisor memory management   Motivated by the observed architectural impact on cloud workload consolidation performance,  propose three optimization techniques incorporating NUMA access overhead into the hypervisor's virtual machine memory allocation and page fault handling routines. Among these, estimation of the memory zone access overhead serves as a foundation for the other two techniques: a NUMA overhead aware buddy allocator and a P2M swap FIFO. Cache hit rate, cycle loss due to cache miss, and IPC serve as indicators to estimate the access cost of each memory node. Our optimized buddy allocator dynamically selects low-overhead memory zones and "proportionally" distributes memory pages across target nodes. The P2M swap FIFO records recently unused PFN, MFN lists for mapping exchanges to rebalance memory access pressure within one domain. Our real system based evaluations show a 41.1% performance improvement when consolidating 16-VMs on a 4- socket server (the proposed allocator contributes 22.8% of the performance gain and the P2M swap FIFO accounts for the rest). Furthermore, our techniques can cooperate ll with other methods (i.e. vCPU migration) and scale ll when varying VM memory size and the number of sockets in a physical host
['Ming Liu', 'Tao Li']
I am writing a research paper on a idea Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads
[{"from": "human", "value": "I am writing a research paper on a idea Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Server virtualization and workload consolidation enable multiple workloads to share a single physical server, resulting in significant energy savings and utilization improvements. The shift of physical server architectures to NUMA and the increasing popularity of scale-out cloud applications undermine workload consolidation efficiency and result in overall system degradation. In this work,  characterize the consolidation of cloud workloads on NUMA virtualized systems, estimate four different sources of architecture overhead, and explore optimization opportunities beyond the default NUMA-aware hypervisor memory management   Motivated by the observed architectural impact on cloud workload consolidation performance,  propose three optimization techniques incorporating NUMA access overhead into the hypervisor's virtual machine memory allocation and page fault handling routines. Among these, estimation of the memory zone access overhead serves as a foundation for the other two techniques: a NUMA overhead aware buddy allocator and a P2M swap FIFO. Cache hit rate, cycle loss due to cache miss, and IPC serve as indicators to estimate the access cost of each memory node. Our optimized buddy allocator dynamically selects low-overhead memory zones and \"proportionally\" distributes memory pages across target nodes. The P2M swap FIFO records recently unused PFN, MFN lists for mapping exchanges to rebalance memory access pressure within one domain. Our real system based evaluations show a 41.1% performance improvement when consolidating 16-VMs on a 4- socket server (the proposed allocator contributes 22.8% of the performance gain and the P2M swap FIFO accounts for the rest). Furthermore, our techniques can cooperate ll with other methods (i.e. vCPU migration) and scale ll when varying VM memory size and the number of sockets in a physical host"}]