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Hardware- and Network-Enhanced Software Systems for Cloud Computing, OW2 Open Cloud Forum at Cloud Expo Europe, February 2014

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Information about Hardware- and Network-Enhanced Software Systems for Cloud Computing, OW2...
Technology

Published on March 11, 2014

Author: ocean-project

Source: slideshare.net

Description

Cloud platforms are missing out on the revolution in new hardware and network technologies for realising vastly richer computational, communication, and storage resources. Technologies such as
field-programmable gate arrays (FPGA), general-purpose graphics processing units (GPGPU), network middleboxes, and solid-state disks promise increased performance, reduced energy consumption, and lower
cost profiles. However, their heterogeneity and complexity makes integrating them into the standard cloud Platform as a Service (PaaS) framework a fundamental challenge. This talk introduces the HARNESS project, whose aim is to explore how to bring these innovative and heterogeneous resources into cloud platforms.
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http://www.harness-project.eu/ The HARNESS Project: Hardware- and Network-Enhanced Software Systems for Cloud Computing Prof. Alexander Wolf Imperial College London (Project Coordinator)

http://www.harness-project.eu/ HARNESSHARNESS Software as a ServiceSoftware as a Service Cloud Market Strata PaaS SaaS Infrastructure as a ServiceInfrastructure as a Service IaaS Platform as a ServicePlatform as a Service

http://www.harness-project.eu/  Provider prospective – minimise ownership costs – maximise usage – maximise market growth PaaS Design Drivers  Application perspective – minimise development costs – minimise operating costs – maximise performance standardised APIs optimal deployment commoditised resources virtualised resources scale out / scale up data-centre expansion

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

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

http://www.harness-project.eu/ Goal: Programmable and Manageable GPU-based parallel-thread engines FPGA-based shared dataflow engines Solid-state disk drives ASIC-based OpenFlow switching fabric Middleboxes for in-network aggregation and storage

http://www.harness-project.eu/ 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

http://www.harness-project.eu/ 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 …… f1f1 fnfn y E {−1,1}y E {−1,1} predictpredict updateupdate … f1 fn y E {−1,1} predict update … Share state Aggregate Iterate … Parallelize … Preprocess AdPredictor Machine Learning open-source “map/reduce” data-flow distributed computation

http://www.harness-project.eu/ 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 …… f1f1 fnfn y E {−1,1}y E {−1,1} predictpredict updateupdate … f1 fn y E {−1,1} predict update … Share state Aggregate Iterate … Parallelize … Preprocess AdPredictor Machine Learning open-source “map/reduce” data-flow distributed computationO(109) entries in daily Web visit logO(109) entries in daily Web visit log two weeks on 300 multi-core nodestwo weeks on 300 multi-core nodes 20% of cycles and 10s of seconds locking20% of cycles and 10s of seconds locking

http://www.harness-project.eu/ AdPredictor Training Process

http://www.harness-project.eu/ HetMR: Heterogeneous MapReduce  MapReduce deployment and execution system for hybrid CPU/accelerator environments Map on GPGPU Reduce on CPU Map on GPGPU Reduce on CPU Map on CPU Reduce on FPGA Map on CPU Reduce on FPGA

http://www.harness-project.eu/ Research Focus Areas Application design Performance prediction and cross-resource mgmt. Resource virtualisation Individual-resource mgmt.

http://www.harness-project.eu/ Mike’s Story http://www.harness-project.eu/

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