Hardware- and Network-Enhanced Software Systems for Cloud Computing

Transcription

1 The HARNESS Project: Hardware- and Network-Enhanced Software Systems for Cloud Computing Prof. Alexander Wolf Imperial College London (Project Coordinator)

2 Cloud Market Strata SaaS Software as a Service PaaS IaaS Platform as a Service Infrastructure as a Service HARNESS

3 PaaS Design Drivers Application perspective minimise development costs minimise operating costs maximise performance standardised APIs optimal deployment scale out / scale up Provider prospective minimise ownership costs maximise usage maximise market growth commoditised resources virtualised resources data-centre expansion

4 Provider prospective minimise ownership costs maximise usage PaaS Design Drivers Application perspective State-of-the-art: minimise Optimised development for horizontal costs scale-out minimise over operating homogeneous costs resources maximise But insufficient performance for many applications Application requirements Fast job completion time with interdependent big data maximise market growth Fresh results within seconds Examples standardised APIs optimal deployment scale out / scale up commoditised resources scientific computing time-series analysis virtualised resources on-line information retrieval data-centre expansion on-line data analytics

5 An Innovative Approach to PaaS the HARNESS project premise Application perspective minimise development costs minimise operating costs maximise performance standardised APIs optimal deployment scale out / scale up specialised resources Provider prospective minimise ownership costs maximise usage maximise market growth GPUs ASICs FPGAs Software switches commoditised resources Network middleboxes virtualised resources data-centre expansion specialised resources SSDs...

6 Goal: Programmable and Manageable GPU-based parallel-thread engines Middleboxes for in-network aggregation and storage Solid-state disk drives ASIC-based OpenFlow switching fabric FPGA-based shared dataflow engines

7 Approach: Enrich IaaS and PaaS Provide an IaaS layer that can manage heterogeneous resources computation, communication and storage resource allocation and scheduling Provide a PaaS layer that can exploit heterogeneous resources multi-tenancy application development cross-resource allocation and scheduling

8 Driving Use Cases basis for demonstration and validation shared memory cache cache cache CPUs CPUs CPUs I/O Delta Merge for SAP HANA in-memory OLTP and OLAP queries for big data analytics Reverse Time Migration (RTM) scientific computation for the geosciences f n f 1 predict update y E { 1,1} Preprocess Share state Parallelize Aggregate Iterate AdPredictor Machine Learning open-source map/reduce data-flow distributed computation

9 Driving Use Cases basis for demonstration and validation shared memory cache cache cache CPUs CPUs CPUs I/O Delta Merge for SAP HANA in-memory OLTP and OLAP queries for big data analytics 20% of cycles and 10s of seconds locking Reverse Time Migration (RTM) scientific computation for the geosciences two weeks on 300 multi-core nodes Parallelize y E { 1,1} O(10 9 f ) entries in daily Web visit log 1 predict Aggregate f n update Preprocess Share state Iterate AdPredictor Machine Learning open-source map/reduce data-flow distributed computation

10 AdPredictor Training Process

11 HetMR: Heterogeneous MapReduce MapReduce deployment and execution system for hybrid CPU/accelerator environments Map on GPGPU Reduce on CPU Map on CPU Reduce on FPGA

12 Application design Research Focus Areas Performance prediction and cross-resource mgmt. Individual-resource mgmt. Resource virtualisation

13 Mike s Story