The HARNESS Project. Cloud application performance modelling. Guillaume Pierre Université de Rennes 1

Transcription

1 The HARNESS Project Cloud application performance modelling Hardware- and Network-Enhanced Software Systems for Cloud Computing Paris Open Source Summit November 2015 Guillaume Pierre Université de Rennes 1 1

2 GPU-based parallel-thread engines FPGA-based shared dataflow engines Project Hypothesis heterogeneous cloud resources provide significant benefits Objective: Seamlessly Middlebox-based incorporate innovative, in-network processing heterogeneous technologies for and storage computation, communication, and storage ASIC-based into cloud Technical platform challenge: infrastructures OpenFlow How do we make these switching resources fabric programmable and manageable in the cloud context? SSD-based storage media 2

3 and the World is Starting to Agree Intel s Cloud strategy Xeon CPU and FPGA on same socket (Intel purchased Altera) predicts 30% of cloud workloads by 2020 Microsoft Bing and Catapult 1632 servers with Xeon and PCIe FPGA card FPGA offers 40x speedup over CPU overall 2x system improvement, with 10% power increase Microsoft Azure with SmartNIC FPGA-enhanced NIC inline traffic management, flow control, encryption, QoS, Software programmable and updateable in the field Google/NVIDIA MapD massively parallel database GPU-based big data DB, with 1000x better query performance 3

4 Focus: Five Key Research Problems Design principles making heterogeneous resources first class Cloud platform predicting performance and selecting resources Resource management scheduling computational tasks Resource virtualisation sharing resources in time and space Programming models encoding domain-specific knowledge and function 4

5 The performance modeling problem aspectdef DspBalancing var op_granularity = [{DspBalance: full,multiplyop: 5,AddOp: 5 }, {DspBalance: balanced,multiplyop:3}];? select function.statement end apply for (var i in op_granularity) { var gprofile = op_granularity[i]; for (var k in gprofile) { if (k!= DspBalance ) { match &= ($statement.num_construct(k) >= gprofile[k]);}} Choosing the right resources in a heterogeneous cloud is not an easy task! 5

6 The HARNESS approach Let developers specify which resources applications is capable of exploiting Automatically profile new applications What is the application s execution time and cost if using resource configuration X? Provide simple choices to the user Fastest, cheapest, in-between Bounded execution time Bounded execution cost 6

7 Exhaustive profiling J Accurate results LBut profiling may take forever Can we get similar results faster? 7

8 Blackbox profiling Four exploration strategies: 1. Uniform sampling 2. Greedy utilization-based 3. Random simulated annealing 4. Directed simulated annealing using resource utilization information 8

9 Blackbox profiling with resource utilization information After 1 iteration After 10 iterations After 20 iterations Mixing the (random) simulated annealing with resource utilization information improves the speed and accuracy of blackbox profiling Heterogeneous Resource Selection for Arbitrary HPC applications in the Cloud. Anca Iordache, Eliya Buyukkaya and Guillaume Pierre. In Proceedings of the 10th International Federated Conference on Distributed Computing Techniques (DAIS 2015),June

10 Blackbox+whitebox profiling Main idea: Collect resource utilization information Keep blackbox modeling as the basis Develop specialized whitebox models Improve the search using the whitebox models 10

11 Blackbox+whitebox profiling Example: detect memory bottlenecks The main search is performed by the blackbox model Any number of whitebox models watch executions - If (and only if) they produce interesting information, whitebox models may shrink the search space - E.g., don t explore configurations using GPUs, memory must be at least X, no more than 2 cores per VM, etc. 11

12 Extrapolated profiling Main idea: 1. Perform the profiling using small inputs Sufficient to understand application behavior 2. Extrapolate the profile to larger input sizes E.g., if the application s complexity is strictly linear then ExecTimelarge = large/small * ExecTimesmall Problem 1: discover the extrapolation function Ø Run a handful of executions using production sizes, derive the correlation function Problem 2: a good configuration for small inputs may not be sufficient for large inputs Ø Whitebox models! 12

13 Extrapolated profiling Extrapolated profiling requires: 10-20% less profiling time 30-35% less profiling budget than regular blackbox+whitebox 13

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15 Conclusion Automatic application profiling is both realistic and feasible 1. Blackbox modeling as the basis 2. Whitebox models to integrate domain-specific knowledge 3. Extrapolated profiling provides further speedup Fully integrated: In the HARNESS platform: In the ConPaaS platform: