Networks and/in data centers! Dr. Paola Grosso! System and Network Engineering (SNE) research group! UvA!

Transcription

1 Networks and/in data centers Dr. Paola Grosso System and Network Engineering (SNE) research group UvA

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3 ICT for sustainability Green by ICT or Green ICT. We ll cover in my presentation: - Review - What are people focusing on? - My two research area : 1. Virtualization: What is the effect of virtualization on energy efficiency (in data centers)? 2. Networks: What is the contribution of networking components to the energy efficiency (of data centers) and the improvement possibilities?

4 Greening the Cloud A RAAK-MKB project with the collaboration of: What determines the energy consumption of Cloud services? This project dives into the hardware and investigates, using sensor technology, how software determines the energy use by hardware components.

5 Bachelor graduation thesis (Yu Ri Tan -2015) Accents in literature - up to 2014

6 Bachelor graduation thesis (Yu Ri Tan -2015) Accents in literature -recent

7 Bachelor graduation thesis (Yu Ri Tan -2015) Focus on the management Goals: ~7% Metrics: ~38% Implementation: 55% Handling workload: ~26% Data center hardware: ~24% Prediction self learning: ~5%

8 Bachelor graduation thesis (Yu Ri Tan -2015) Summary Goals: Business Level Objective (BLO) Service Level Agreement (SLA) Metrics: Power Usage Effectiveness (PUE) Data Center Infrastructure Efficiency (DCiE) Rack Cooling Index (RCI) Relative Idle Consumption (RIC) Handling workloads: Dynamic Voltage Frequency Scaling (DVFS) Adaptive Link Rate (ALR) Geographical Load Balancing (GLB) Virtual Machine manager (migration, allocation) Learning Cooling Virtual Machine manager (migration, allocation) Server components (CPU etc.) Constant response time Hardware Cooling:CRAH, CRAC Power Distribution Units Energy meters Redundant hardware

9 Virtualization

10 Virtualization What is the effect of virtualization on energy efficiency? Three focus areas: 1. Cloud management systems 2. Hypervisors 3. Configuration and operation of virtual machines.

11 Why virtualization? Virtualization provides consolidation of resources, while guaranteeing resource isolation.

12 Virtualization overhead N. Huber, M. von Quast, F. Brosig, and S. Kounev. Analysis of the Performance-Influencing Factors of Virtualization Platforms. In DOA 10, J. Li, Q. Wang, D. Jayasinghe, J. Park, T. Zhu, and C. Pu, Performance overhead among three hypervisors: An experimental study using hadoop benchmarks, in Proc. of the 2nd IEEE International Congress on Big Data, 2013, pp

13 Energy saving in clouds Quantifying the energy performance of VMs is the first step toward energy-aware job scheduling. Q. Chen, P. Grosso, K. van der Veldt, C. de Laat, R. Hofman and H.Bal. (2011) Profiling energy consumption of VMs for green cloud computing In: International Conference on Cloud and Green Computing (CGC2011), Sydney

14 Modeling the VM energy behaviour Open challenge: Coupling with the knowledge of what the software will need to do.

15 Semantic descriptions and energy monitoring Energy Description Language Energy Knowledge Base indl: Processing isa indl:node Component indl:storage edl:outlet isa isa edl:hardware Sensor Component edl:software Sensor Component isa isa isa indl:memory indl: hascomponent edl: attachto edl:hasoutlet edl: Sensor edl: PowerCapability nml:node edl:monitored By edl:power Meter edl:monitor Component edl: EnergySource edl:useenergysource edl:hascapability edl:hasdriver edl: Driver isa isa edl:haslog isa edl:atpowerstate edl: MonitorLog edl:hasload edl:load edl:hasmeasurement edl: Measurement edl: PowerState edl:unit edl:hasunit edl:metric edl:usemetric isa edl:green Metric isa edl:observed GMetric Semantic descriptions for energyaware infrastructures enhance the share of energy knowledge isa edl:performance Metric isa INDL Characteristic Monitor Metric edl:calculated GMetric Globus MDS Query Manager Agent Repository Resource Collector and Descriptor Agent Native data Transfer Bright Cluster Manager Sensor Distributed Information Sources Agent Domain Ontology InfoSource Ontology H. Zhu, K. van der Veldt, P. Grosso, X. Liao & C. de Laat (2013). EKB: semantic information system for energy-aware monitoring in distributed infrastructures. In Proceedings 2013 IEEE Third International Conference on Cloud and Green Computing (pp ). Los Alamitos, CA: IEEE.

16 Linear Power estimation models M- Linear ANN GMD H GMM CDF Model-training time (sec) Estimation time for single example (sec) <10e-8 <10e-7 10e-7-10e e-4-10e-3 10e-4-10e Linear M Linear 0.1 ANN GMDH GMM RMSE (Watt) CPU load during training Large training data demand <7% <7% <10% <10% <10% No No Yes Yes Yes (a) Hadoop Nodes Linear M Linear ANN GMDH GMM (b) GPU Nodes To find out precise and usable models for power estimation is necessary to evaluate their performance. RMSE(Watt) 30 RMSE(Watt) Writer Sort 10 0 Linpack Linpack GPU H. Zhu, P. Grosso, X. Liao & C.T.A.M. de Laat (2014). Evaluation of approaches for power estimation in a computing cluster. In International Green Computing Conference. Dalls, TX: IEEE.

17 What next for virtualization? 3 thoughts: Very hard to chose an optimal hypervisor. Most research done on XEN and KVM. Is there a general rule-of-thumb in choosing which hypervisor to adopt? Performance costs (lower performances) needs to be weighted against lower energy costs. What are the boundaries per application? Micro-services and containers will see wider adoption. What are their energy effects?

18 Networks

19 NRENs National Education Networks

20 Does the network matter? The energy footprint of data center networks is 10-20% of total energy The proportion could be up to 50% if power management techniques are used on the server-side Global cloud IP traffic will 3.9-fold from 2013 to Networks suffer from inefficient power usage because of overprovisioning.

21 Unifying Top-down and Bottom-up Approaches to Evaluate Network Energy Consumption Ishii et al. In: Lightwave Technology, Journal of, vol.pp, no.99, pp.1-1 Looking ahead

22 What s new? Programmability 87% growth in SDN deployment in North Americanbased enterprises by 2016 [2] White-box networking Diverting the control to the software

23 Emerging interest in SDN for energy efficiency Emerging studies improve on the energy consumption of servers by the VM migration. Some change the OpenFlow protocol to be energy-aware. All of them are implemented in intra-data center scale. All have a fixed initial traffic matrix. Moghaddam, F. A., Lago, P., & Grosso, P. Energy-Efficient Networking Solutions in Cloud-Based Environments: A Systematic Literature Review. ACM Computing Surveys (CSUR), 47(4), 64. (2015) Yearly distribution of the OpenFlow technique adoption by decision frameworks from December 2008 to November 2013 [3

24 Green routing with SDN Make a routing decision to aggregate traffic over a subset of links and devices in over-provision networks and switch off unused network components H. Zhu. J. Aznar Baranda, C. de Laat and P. Grosso. Green routing for software defined data center networks based on OpenNaaS (ICGreen 2015)

25 Green routing in data centra Topology: BCube(2,3), 8 8-port switches in total. Capacity of each link: 1Gbps; Delay of each link: 1 millisecond. Mean power of switch: 120 watts. Dynamic power/overall power = 10%. Network power saving against the network utilization in the BCube network Mean flow delay time against the network utilization in the BCube network H. Zhu. X. Liao, C. de Laat and P. Grosso. (2015) Joint flow routing-scheduling for energy efficient software defined data center networks (under revision)

26 What next for networks? 3 thoughts: Moving from simulation to experimentation in real data center networks. Will we see main stream use in data centra? Couple the programmability of the network with the application desired service. How to teach the developers how to program the network? The datacenter of the future is embedded in the fabric of the Cloud and driven by Big Data operations/analytics/computation. How do we coordinate inter-data-center operations sustainably?

27 Thanks and. consider collaboration with us. We encourage research projects for our Master students in industry.