NDLI: Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads

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Unifying on-chip and inter-node switching within the Anton 2 network

Avoiding core's DUE & SDC via acoustic wave detectors and tailored error containment and recovery

The Direct-to-Data (D2D) cache

Fine-grain task aggregation and coordination on GPUs

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Memory persistency

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Increasing off-chip bandwidth in multi-core processors with switchable pins

The dirty-block index

Single-graph multiple flows

Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads

Flipping bits in memory without accessing them

A low power and reliable charge pump design for phase change memories

Eliminating redundant fragment shader executions on a mobile GPU via hardware memoization

Real-world design and evaluation of compiler-managed GPU redundant multithreading

Harnessing ISA diversity

Going vertical in memory management

Replay debugging

Improving the energy efficiency of big cores

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Volume 42, Issue 1, March 2014

Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads

Content Provider	ACM Digital Library
Author	Liu, Ming Li, Tao
Abstract	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, we 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, we 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 well with other methods (i.e. vCPU migration) and scale well when varying VM memory size and the number of sockets in a physical host
Starting Page	325
Ending Page	336
Page Count	12
File Format	PDF
ISSN	01635964
DOI	10.1145/2678373.2665720
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	42
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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