Power Comparison Power Comparison of Cloud Data of Cloud Data Center Architectures
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Power consumption is a primary concern for cloud computing data centers. Being the network one of the non- negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of im- proving network performance and being energy-efficient. In this paper we provide a comparison study of data center architectures, covering both classical two- and three-tier design and state-of- art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies.
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Power Comparison Power Comparison of Cloud Data of Cloud Data Center Architectures
- 1. Power Comparison of Cloud Data Center Architectures Pietro Ruiu Andrea Bianco, Paolo Giaccone Claudio Fiandrino, Dzmitry Kliazovich 13th Italian Networking Workshop: San Candido, Italy January 13 - 15, 2016
- 2. The quest for green data centers • Data centers are the new polluters of 21st century – in 2012, accounted for 15% of the global ICT energy consumption – expected to increase in the next years • Data center consumption – 75% ICT equipment • powering and cooling • mostly due to servers – 25% power distribution and facility operations • Strong interest in designing and operating data centers with higher energy efficiency – not only to reduce OPEX costs 2
- 3. Key question • Given • a data center topology • a power consumption profile for each ICT device • Define the min-power job allocation policy • Evaluate power consumption as function of the data center load Classical questions • Given • a power consumption profile for each ICT device • a generic job allocation policy • Compare the power consumption behavior in function of the data center topology Our question 3
- 4. Data center model 4 Data center network (DCN) Power Load Power Load Power Load Servers ICT device Power profile
- 5. Local vs global energy proportionality • consume proportional to the load • consume (and pay) only if really needed Ideal energy proportionality • Constant power (CONST) • Full Energy Proportional (FEP) • Linear (LIN) Local power consumption for single device • maybe very different from local power consumption Global power consumption for overall system 5 CONST Power Load FEP Power Load LIN Power Load Power Load
- 6. Global power consumption 6 • N resources/devices to be allocated for a set of requests/VMs • power consumption profile for each resource/device • allocation policy • consolidate: activate the minimum number of resources • load-balance: distribute load across the resources Resource allocation • depends on granularity of the resources (i.e. the value of N) • depends on allocation policy Overall power consumption
- 7. Global Power Consumption 7 Local power consumption Consolidate policy Load-balance policy Normalized power = Power / Load FEP Power Load Power Load Load Norm.Power Power Load Load Norm.Power CONST Power Load N Load Power N Load Power N Load Norm.Power N Load Norm.Power • Local energy proportionality implies global energy proportionality • If N is enough large, consolidate policy reaches global energy proportionality for CONST local power Local and Global Energy Proportionality
- 8. Our contributions • Network-aware min-power VM online allocation policy • Flow-level C++ simulator of data centers • Power comparison of different network topologies 8 • Energy profiles for each ICT device (switch, link, server) • DCN topology • VM arrival process Flow-level simulator • Global power consumption • Load on each ICT device • VM blocking probabilityVM allocation policy
- 9. DCN topologies • traditional Clos-based switch topologies – classical 2, 3 tiers – Jupiter • Google’s disclosed DCN architecture • “Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network”, ACM SIGCOMM CCR, Oct. 2015 9
- 10. • Switches: 10 core sw – 18 TOR sw • Servers 180 • Total ICT devices: 208 nodes CORE TOR 10Gbps40Gbps 2-tiers DCN 10
- 11. • 3 core sw – 6 aggregation sw – 18 TOR sw • 180 servers – 27 switches – 207 nodes CORE AGGREGATION TOR 10Gbps40Gbps40Gbps 3-tiers DCN 11
- 12. • 24 spine sw – 16 aggregation sw – 16 TOR sw • 192 servers – 44 switches – 236 nodes = 4p@40Gbps (or 16p@10Gbps) SPINEAGGREGATIONTOR 10Gbps10Gbps40Gbps40Gbps40Gbps MB MB MB MB Jupiter-like DCN 12
- 13. Online VM allocation policy 13 • for each VM, select a server at random • connect through the minimum incremental DCN power • load-balance on the servers RSS (Random Server Selection) • for each VM, select the server with minimum incremental power (server + DCN) • consolidate VMs in the same server, in the same rack, in closeby racks, etc • variant of min-cost Dijkstra algorithm MNP (Minimum Network Power)
- 14. VM generation • time is slotted • at each timelot, a new VM arrives and must communicate B bps to a previously randomly allocated VM – B is randomly chosen – destination VM is chosen with Bernoulli trials • simulation can run until saturating the data center 14 VM1 VM2 VM3 VM4 VM5
- 15. RSS (Random Server Selection) • small datacenter (180-192 servers) • Jupiter appears to be the most energy proportional – due to the larger number of switches (44 vs 27-28) 15 0 5 10 15 20 25 20 30 40 50 60 70 80 90 100 NormalizedpowerperVM[W] Data Center load [%] 3-Tier LIN FEP CONST 0 5 10 15 20 25 20 30 40 50 60 70 80 90 100 Data Center load [%] 2-Tier LIN FEP CONST 0 5 10 15 20 25 20 30 40 50 60 70 80 90 100 Data Center load [%] Jupiter LIN FEP CONST
- 16. MNP (Minimum Network Power) 16 • small datacenter (180-192 servers) • MNP allows to achieve global energy-proportionality • under FEP, power jumps due to abrupt activation of new layers 0 0.5 1 1.5 2 2.5 3 20 30 40 50 60 70 80 90100 NormalizedpowerperVM[W] Data Center load [%] 3-Tier LIN FEP CONST 0 0.5 1 1.5 2 2.5 3 20 30 40 50 60 70 80 90 100 Data Center load [%] 2-Tier LIN FEP CONST 0 0.5 1 1.5 2 2.5 3 20 30 40 50 60 70 80 90 100 Data Center load [%] Jupiter LIN FEP CONST
- 17. Conclusions • Global energy proportionality of an overall data center depends on • local power profile of each device • topology (number of devices) • VM allocation policy Take-home message • consider large topologies with 10,000 servers • compare data center networks given the same bisection bandwidth • consider the allocation of clusters of VMs Future works 17
Hinweis der Redaktion
- Perche’ LIN si comporta cosi’ male???