Farewell to Servers: Hardware, Software, and Network Approaches towards Datacenter Resource Disaggregation

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1 Farewell to Servers: Hardware, Software, and Network Approaches towards Datacenter Resource Disaggregation Yiying Zhang

2 Datacenter

3 3

4 Monolithic Computer OS / Hypervisor 4

5 Can monolithic Application Hardware servers continue to Heterogeneity meet datacenter needs? Flexibility Perf / $

6 TPU GPU FPGA HBM ASIC NVM NVMe DNA Storage 6

7 Making new hardware work with existing servers is like fitting puzzles 7

8 Tons of Issues Fitting bus standards Slots for new devices? Power for new devices? Changing bus? Software for new devices? 8

9 6 months of my life went to Firmware v0 SATA Firmware v1 Firmware v2 9

10 Can monolithic Application Hardware servers continue to Heterogeneity meet datacenter needs? Flexibility Perf / $

11 Application Elasticity and Perf / $ Whole VM/container has to run on one physical machine - Move current applications to make room for new ones VM1 App1 App2 Container1 Core Core Core Core Core Core Cor e Core Core Core Core 8GB Main Memory 3GB Memory 4GB Main Memory Node 1 Node 2 11

12 CPU/Memory Usages across Machines and across Jobs Source: Gu et al. Efficient Memory Disaggregation with Infiniswap NSDI 17 Memory usage in datacenter are highly imbalanced 12

13 Resource Utilization in Production Clusters * Google Production Cluster Trace Data. Unused Resource + Waiting/Killed Jobs Because of Physical-Node Constraints 13

14 No fine-grained failure handling 14

15 Can monolithic Application Hardware servers continue to Heterogeneity meet datacenter needs? Flexibility Perf / $

16 How to achieve better heterogeneity, flexibility, and perf/$? Go beyond physical node boundary 16

17 Resource Disaggregation: Breaking monolithic servers into networkattached, independent hardware components 17

18 18

19 Application Flexibility Heterogeneity Hardware Hardware Perf / $ Network Distributed Memory 19

20 Disaggregated Datacenter End-to-End Solution Unmodified Application Performance Dist Sys Heterogeneity OS Flexibility Network Reliability Hardware $ Cost

21 Disaggregated Datacenter flexible, heterogeneous, elastic, perf/$, resilient, scalable, easy-touse Physically Disaggregated Resources Virtually Disaggregated Resources Disaggregated Operating System New Processor and Memory Architecture Dumb Remote Memory + Smart Control One-Sided Remote Memory / NVM Distributed Shared Persistent Memory (SoCC 17) Distributed Non-Volatile Memory Networking for Disaggregated Resources Kernel-Level RDMA Virtualization (SOSP 17) RDMA Network New Network Topology, Routing, Congestion-Ctrl Infiniband

22 Key Challenge of Resource Disaggregation: Cost of Crossing Network Bandwidth Latency Mem Bus GB/s ~50ns PCIe 3.0 (x16) 16 GB/s ~700ns Network hardware is much faster than before InfiniBand (EDR) InfiniBand (HDR) 12.5 GB/s 500ns 25 GB/s <500ns Current network still slower than local memory bus GenZ GB/s <100ns 22

23 Network Requirements for Resource Disaggregation Low latency High bandwidth Scale RDMA Reliable 23

24 RDMA (Remote Direct Memory Access) Directly read/write remote memory Bypass kernel Memory zero copy Benefits: Low latency High throughput Low CPU utilization 24

25 Things have worked well in HPC Special hardware Few applications Cheaper developer 25

26 RDMA-Based Datacenter Applications Pilaf [ATC 13] HERD-RPC [ATC 16] Cell [ATC 16] FaRM [NSDI 14] Wukong [OSDI 16] FaSST [OSDI 16] HERD [SIGCOMM 14] Hotpot [SoCC 17] NAM-DB [VLDB 17] RSI [VLDB 16] DrTM [SOSP 15] APUS [SoCC 17] Octopus [ATC 17] DrTM+R [EuroSys 16] FaRM+Xact Mojim [SOSP 15] [ASPLOS 15] 26

27 Things have worked well in HPC Special hardware Few applications Cheaper developer What about datacenters? Commodity, cheaper hardware Many (changing) applications Resource sharing and isolation 27

28 Native RDMA Kernel Bypassing 28

29 Fat applications No resource sharing Low-level Difficult to use Difficult to share RDMA Socket Developers want High-level Easy to use Resource share Isolation Abstraction Mismatch 29

30 Things have worked well in HPC Special hardware Few applications Cheaper developer What about datacenters? Commodity, cheaper hardware Many (changing) applications Resource sharing and isolation 30

31 Native RDMA Kernel Bypassing 31

32 Requests /us On-NIC SRAM stores and caches metadata Expensive, Write-64B Write-1K Total Size (MB) unscalable hardware 32

33 Things have worked well in HPC Special hardware Few applications Cheaper developer What about datacenters? Commodity, cheaper hardware Many (changing) applications Resource sharing and isolation 33

34 Fat applications No resource sharing Expensive, unscalable hardware Are we removing too much from kernel? 34

35 LITE -Without Local Indirection Kernel TiEr High-level High-level abstraction abstraction Resource Resource sharing sharing Protection Performance isolation Performance isolation Protection 35

36 All problems in computer science can be solved by another level of indirection Butler Lampson 36

37 User Space Hardware RNIC 37

38 Simpler applications User Space LITE APIs Memory APIs RPC/Msg APIs Sync APIs Kernel Space LITE Hardware RNIC 38

39 Simpler applications User Space LITE APIs Memory APIs RPC/Msg APIs Sync APIs Kernel Space LITE Global lkey Global rkey Hardware RNIC Global lkey Global rkey Cheaper hardware Scalable performance 39

40 Implementing Remote memset Native RDMA LITE 40

41 All problems in computer science can be solved by another level of indirection Butler Lampson David Wheeler except for the problem of too many layers of indirection David Wheeler 41

42 Main Challenge: How to preserve the performance benefit of RDMA? 42

43 LITE Design Principles 1.Indirection only at local node 2.Avoid hardware-level indirection 3.Hide kernel-space crossing cost except for the problem of too many layers of indirection David Wheeler Great Performance and Scalability 43

44 Requests /us LITE RDMA:Size of MR Scalability Write-64B LITE_write-64B Write-1K LITE_write-1K LITE scales much better than native Total Size (MB) RDMA wrt MR size and numbers 44

45 LITE Application Effort Application LOC LOC using LITE Student Days LITE-Log LITE-MapReduce 600* 49 4 LITE-Graph LITE-Kernel-DSM Simple to use Needs no expert knowledge Flexible, powerful abstraction Easy to achieve optimized performance * LITE-MapReduce ports from the 3000-LOC Phoenix with 600 lines of change or addition 45

46 Runtime (sec) MapReduce Results LITE-MapReduce adapted from Phoenix [1] Hadoop Phoenix LITE LITE-MapReduce 2. outperforms Hadoop 0. by 4.3x to 5.3x Phoenix 2-node 4-node 8-node [1]: Ranger etal., Evaluating MapReduce for Multi-core and Multiprocessor Systems. (HPCA 07) 46

47 LITE Summary Virtualizes RDMA into flexible, easy-to-use abstraction Preserves RDMA s performance benefits Indirection not always degrade performance! Division across user space, kernel, and hardware 47

48 Disaggregated Datacenter flexible, heterogeneous, elastic, perf/$, resilient, scalable, easy-touse Physically Disaggregated Resources Virtually Disaggregated Resources Disaggregated Operating System New Processor and Memory Architecture Dumb Remote Memory + Smart Control One-Sided Remote Memory / NVM Distributed Shared Persistent Memory (SoCC 17) Distributed Non-Volatile Memory Networking for Disaggregated Resources Kernel-Level RDMA Virtualization (SOSP 17) RDMA Network New Network Topology, Routing, Congestion-Ctrl Infiniband

49 Traditional OSes Manages single node and all hardware resources in it Bad for hardware heterogeneity and hotplug Does not handle component failure 49

50 50

51 Lego: the First Disaggregated OS 51

52 When hardware is disaggregated, the OS should be also! 52

53 OS 53

54 54

55 Micro-OS Service Manages hardware resource of a component Virtualizes the hardware Communicates with other micro-os services Runs in hardware controller (kernel space) Only processors have user space 55

56 Lego Heterogeneity Architecture Flexibility Elasticity Resource util Failure isolation 56

57 Many Challenges Handling component failure Manage distributed, heterogeneous resources Fitting micro-os services in hardware controller Implementing Lego on current servers 57

58 Lego Implementation Built from scratch, >200K LOC and growing Runs all Linux ABIs and unmodified binaries Three micro-os services: processor, memory, storage Global resource manager Emulates disaggregated hardware with regular servers 58

59 Lego Summary Resource disaggregation calls for new system Lego: new OS designed and built from scratch for datacenter resource disaggregation Split OS into distributed micro-os services, running at device Many challenges and many potentials 59

60 Disaggregated Datacenter flexible, heterogeneous, elastic, perf/$, resilient, scalable, easy-touse Physically Disaggregated Resources Virtually Disaggregated Resources Disaggregated Operating System New Processor and Memory Architecture Dumb Remote Memory + Smart Control One-Sided Remote Memory / NVM Distributed Shared Persistent Memory (SoCC 17) Distributed Non-Volatile Memory Networking for Disaggregated Resources Kernel-Level RDMA Virtualization (SOSP 17) RDMA Network New Network Topology, Routing, Congestion-Ctrl Infiniband

61 Physical Resource Disaggregation Great support of heterogeneity Very flexible in resource management But needs hardware, network, and OS changes 61

62 Is there any less disruptive way to achieve better resource utilization and elasticity?

63 Virtually Disaggregated Datacenter Use resources on remote (distributed) machines Monolithic Server Monolithic Server Monolithic Server CPU CPU CPU CPU CPU CPU CPU CPU CPU Main Memory Main Memory Main Memory Storage Storage Storage 63

64 Using Remote/Distributed Resources Was a popular idea in 90s Remote memory/paging/swap Network block device Distributed shared memory (DSM) No production-scale adoption Cost of network communication Coherence traffic 64

65 65

66 Remote/Distributed Memory in Modern Times New and heterogeneous applications Large parallelism New computation and memory requirements New programming models Network is 10x-100x faster InfiniBand: 200Gbps, <500ns GenZ: GB/s, <100ns New types of memory NVM, HBM 66

67 Recent New Attempts Distributed Shared Memory Grappa Network swapping InfiniSwap Non-coherent distributed memory VMware 67

68 Our View Design new remote/distributed memory systems that leverage new hardware and network properties Design for modern datacenter applications Tradeoff of performance and programability First project: distributed Non-Volatile Memory (Persistent Memory) 68

69 DSM Distributed Shared Persistent Memory (DSPM) a significant step towards using PM in datacenters 69

70 DSPM Native memory load/store interface Local or remote (transparent) Pointers and in-memory data structures Supports memory read/write sharing 70

71 DSM Distributed Shared Persistent Memory (DSPM) a significant step towards using PM in datacenters 71

72 DSPM Memory load/store interface Local or remote (transparent) DSPM: (Distributed) One Layer MemoryApproach Pointers and in-memory data structures Supports memory read/write sharing Benefits of both memory and storage No redundant layers Persistent naming No data marshaling/unmarshalling Data durability and reliability (Distributed) Storage 72

73 Hotpot: A Kernel-Level RDMA-Based DSPM System Easy to use Native memory interface Fast, scalable Flexible consistency levels Data durability & reliability 73

74 Hotpot Architecture 74

75 Hotpot Architecture 75

76 Hotpot Architecture 76

77 Hotpot Architecture 77

78 Hotpot Architecture 78

79 Hotpot Architecture 79

80 MongoDB Results Modify MongoDB with ~120 LOC, use MRMW mode Compare with tmpfs, PMFS, Mojim, Octopus using YCSB 10/9/17 80

81 Disaggregated Datacenter flexible, heterogeneous, elastic, perf/$, resilient, scalable, easy-touse Physically Disaggregated Resources Virtually Disaggregated Resources Disaggregated Operating System New Processor and Memory Architecture Dumb Remote Memory + Smart Control One-Sided Remote Memory / NVM Distributed Shared Persistent Memory (SoCC 17) Distributed Non-Volatile Memory Networking for Disaggregated Resources Kernel-Level RDMA Virtualization (SOSP 17) RDMA Network New Network Topology, Routing, Congestion-Ctrl Infiniband

82 Conclusion New hardware and software trends point to resource disaggregation WukLab is building an end-to-end solution for disaggregated datacenter Opens up new research opportunities in hardware, software, networking, security, and programming language 82

83 Thank you Questions? wuklab.io

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