SAN FRANCISCO, CA, USA Adaptive Energy- Adaptive Energy- efficient Resource efficient Resource Sharing for Sharing for Multi-threaded Workloads Multi-threaded Workloads in Virtualized Systems in Virtualized Systems Can Hankendi Ayse K. Coskun Boston University Electrical and Computer Engineering Department This project has been partially funded by:
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SAN FRANCISCO, CA, USA Adaptive Energy-efficient Resource Sharing for Multi-threaded Workloads in Virtualized Systems Can HankendiAyse K. Coskun Boston.
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SAN FRANCISCO, CA, USA
Adaptive Energy-efficient Adaptive Energy-efficient Resource Sharing for Resource Sharing for Multi-threaded Workloads Multi-threaded Workloads in Virtualized Systemsin Virtualized Systems
Can Hankendi Ayse K. Coskun
Boston UniversityElectrical and Computer Engineering
Department
This project has been partially funded by:
Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
Energy Efficiency in CEnergy Efficiency in Coomputing Clustersmputing Clusters
• Energy-related costs are among the biggest contributors to the total cost of ownership.
• Consolidating multiple workloads on the same physical node improves energy efficiency.
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(Source: International Data Corporation (IDC), 2009)
Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
Multi-threaded Applications in the Multi-threaded Applications in the CloudCloud
• HPC applications are expected to shift towards cloud resources.
• Resource allocation decisions significantly affect the energy efficiency of server nodes.
• Energy efficiency is a function of application characteristics.
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Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
OutlineOutline
• Background
• Methodology
• Adaptive Resource Sharing
• Results
• Conclusions
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Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
BackgroundBackground
Cluster-level VM Management
- Consolidation policies across server nodes
- VM migration techniques
[Srikantaiah, HotPower’08][Bonvin, CCGrid’11]
Node-level Management
Recent Co-scheduling policies-Co-scheduling contrasting workloads-Balancing performance events across nodes
- Cache misses- IPC- Bus accesses
[Dhiman, ISLPED’09][Bhadauria, ICS’10]
- Co-scheduling based on thread communication
- Identifying best thread mixes to co-schedule
[Frachtenberg, TPDS’05][McGregor, IPDPS’05]
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Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
Virtualized System SetupVirtualized System Setup
• 12-core AMD Magny Cours Server 2x 6-core dies attached side by side in
the same package Private L1 and L2-caches for each core 6 MB shared L3-cache for each 6-core die
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• Virtualized through VMware vSphere 5 ESXi hypervisor 2 Virtual Machines (VM) with Ubuntu Server Guest OS
Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
ResultsResults
• We generate 50 workload sets, each consists of randomly selected 10 PARSEC applications.
• Proposed resource sharing technique improves the throughput-per-watt by 12% on average in comparison to application selection based co-scheduling techniques.
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Computing in Heterogeneous, Autonomous 'N' Goal-oriented Environments
Conclusions & Future WorkConclusions & Future Work
• Consolidation is a powerful technique to improve the energy efficiency on data centers.
• Energy efficiency of parallel workloads varies significantly depending on application characteristics.
• Adaptive VM configuration for parallel workloads improves the energy efficiency by 12% on average over existing co-scheduling algorithms.
• Future research directions include:Investigating the effect of memory allocation decisions on energy efficiency;Utilizing application-level instrumentation to explore power/energy optimization opportunities;Expanding the application space.