Bae, James

Cao, Wenqi

Liu, Ling

Sahin, Semih

Wu, Yanzhao

Zhang, Qi

English

Clemson University: TigerPrints

Huge Data Analytics with Transparent In-Network Memory ComputingLing Liu, James Bae, Wenqi Cao*, Semih Sahin*, Yanzhao Wu, Qi Zhang*ProfessorDistributed Data Intensive Systems LabSchool of Computer ScienceCollege of Computing*Wenqi Cao (facebook), Semih Sahin (google, USA) and Qi Zhang (IBM TJ Watson) contributed to this umbrella project when they were PhD students at Georgia Tech.  CPU CPUCPUCPU CPUCPU CPUCPU CPU CPU CPU CPU CPU CPUCPU CPUCPUCPU CPUCPU CPUMEM MEMMEMMEM MEMMEM MEMMEM MEMMEMMEM MEMMEM MEMTransparent In-Network Memory Centric ComputingMEM MEMMEMMEM MEMMEM MEMCluster Computing Performance ObservationThe peak memory that can fit the full working set is measured for Memcached, Redis and VoltDB, which are 25GB, 29GB and 30GB respectively. 75% configuration:  median latencies worsen by 61x, 462x and 70x respectively  99th percentile latencies are worsened by 104x, 391x and 208x respectively. 50% configuration: median latencies are worsen by 214x, 802x and 369x respectively, and  99th percentile latencies are degraded by 316x, 724x and 850x respectively. • Google datacenter usage analysis4Memory Allocated vs UsedSource: Towards understanding heterogeneous clouds at scale: Google trace analysis, by Charles Reiss (UC Berkeley), Alexey Tumanov(CMU), Gregory R. Ganger (CMU), Randy H. Katz (UC Berkeley), Michael A. Kozuch (Intel Labs)Intel Science & Technology Center for Cloud Computing, Carnegie Mellon University http://sbac.lip6.fr/2014/session%209/3-JALorenzo.pdf Vanilla Linux v.s. XmemPod LinuxNoSQL bigdata system performance comparison (larger time scale for Linux measurement, 5x ~ 649x improvement) Machine learning workload performance comparison (larger time scale for Linux measurement, 8x ~ 36x improvement) XmemPodXmemPodTRANSPARENT IN-NETWORK MEMORY CENTRIC COMPUTING50% of application working set is in memoryusing FS-SM in FastSwap:  throughputs of Redis, Memcached, and VoltDB increase by up to 571x, 171x, and 240x respectively, compared with Linux. increase by 11.4x, 5.1x, and 2.0x compared to Infinitswap and increase by 10.5x, 4.9x, and 1.8x compared with nbdXTransparent Host-Remote In-Network Memory DisaggregationWenqi Cao and Ling Liu. ``Hierarchical Orchestration of Disaggregated Memory, IEEE Transactions on Computers.XMemPod code on Github.com/git-disl/.• Transparent In-Network Memory Centric Computing– Transparent utilization of available memory of other in-network executors • Instead of distributed controlled data partitioning and resorting to external I/O storage for contingency• Transparent In-Network Federated Edge System Computing– Transparent federation of available edge system computing capability • instead of moving/collecting data to a central location7Huge Data Challenge: Transparent In-Network Memory Centric Computing

Huge Data Analytics with Transparent In-Network Memory Computing (Presentation)

https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=1056&amp;context=hugedata

Huge Data Analytics with Transparent In-Network Memory Computing (Presentation)

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Clemson University: TigerPrints