Defining and Improving IT Utilisation Efficiency through Holistic Data Centre Monitoring

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1 Defining and Improving IT Utilisation Efficiency through Holistic Data Centre Monitoring Michael Rudgyard CEO Concurrent Thinking Ltd

2 Who are we?

3 Concurrent Thinking History A spin-out company of a well-established UK SI Technology was developed for High Performance Computing Management of HPC resources needs to be system-wide Scalability (of both the architecture and the GUI) is paramount New company formed in March 2010 Took on the product IP and existing HPC customer base Notable investment from the UK Carbon Trust Currently in semi-stealth mode Have developed new features for the Data Centre market that leverage the infrastructure of a tried and tested product

4 How Efficient is your Data Centre?

5 PUE; design vs. operational efficiency metrics Most new data centres are being designed against PUE targets For a given IT hardware capacity, PUE is a good planning metric However, it is usually a poor operational metric Most importantly: what if the servers are not doing any useful work?? The data centre may still have a good PUE, but it would be very inefficient by any business metric We really need a measure of IT Usage Effectiveness ie. how effective the power is being used to deliver necessary IT services Or: how effective the power is being used for business benefit

6 ITUE A better class of efficiency metrics? Unlike PUE, the concept of ITUE encompasses a family of performance metrics Some metrics may provide useful generic ITUE measurement MIPS/watt or CPU Utilisation/watt (for compute bound tasks) IOPS/watt or Bytes/watt (when I/O is predominant) Some end-users may be interested in application-related metrics: Database transactions/watt Page refresh/watt Search/watt Some may be business related: s of products sold / watt; or s of products sold / integrated IT cost

7 Driving End-User Behaviour With few exceptions, the most successful methodology for improving energy conservation across all sectors is: Step 1: Identify who/what is responsible for significant energy waste Step 2: Drive behaviour to encourage change What is the implication for the Data Centre? Need to monitor and report ITUE metrics by customer, department or end-user Who or what applications/service are the worst offenders? Management can use data to drive better practice (charge-back?)

8 Advantages of Holistic Management

9 Bridging the IT and Facilities divide Efficient DCs will need to monitor & manage both IT and Facilities systems in a coherent manner: Environmental systems (temperature, humidity, air-conditioning..) Power (at the distribution board, rack PDU and server PSU level ) IT equipment (using standard protocols such as IPMI and SNMP ) Operating systems & Virtual Machines (integrating with IT systems)..and perhaps applications themselves Software tools will need to integrate with multiple systems from multiple vendors (both hardware and software) in an agnostic manner

10 Holistic Management Example Benefits Optimised environmental management to improve PUE (& ITUE) Identification of unused, under-used, inefficient or over-spec ed IT equipment Using active power management during low utilisation periods Dynamic orchestration of virtual machines based on environmental, power and IT usage constraints Non-trivial energy savings through simple changes (20-25%) Opportunity for very significant savings in most DCs (25-75%)

11 The Challenge of Scale

12 Bigger is better Consolidation of Data Centres is already happening Driven by economies of scale and the Cloud The trend is only likely to accelerate Conversely, as Data Centres become bigger, energy management will become even more important The winners in the race for the clouds will be those who can operate the most efficiently.. but few know how efficient they are now!!

13 Who knows best? The largest data centres are owned by a handful of IT giants: Google, Amazon, Microsoft, Yahoo etc These giants are very aware of Data Centre Efficiency Some have turned common perceptions on their heads Some even design their own servers All have developed their own systems and software How will existing DC providers compete?

14 The scale-out problem Imagine a data-centre with ,000 servers (cf. Google) ie. 1,500-3,000 racks and a similar number of PDUs and sensors and up to (say) 16 VMs per server You might want to monitor (derive reports from & orchestrate ) 1,500-12,000 environmental sensors data-points per server (IPMI, Power) = 1-3M points data-points per OS/VM (eg. SNMP, WMI) = M points as well as user and application data. That s hell of a lot of information! But even scaling this back by an order of magnitude presents a challenge for software.

15 The software challenge Things that won t work: Using a single-instance software architecture Information will need to be processed in a distributed manner Putting unrefined data in a standard SQL data-base or you ll need another data-centre to store, process & retrieve the data! Expecting simple GUIs (eg. lists and trees) to be effective Visualisation becomes a key aspect to usability Increased need for automation and data consolidation

16 Concurrent Thinking s Products

17 Command & Control Architecture concurrentcommand provides full system management GUI concurrentcontrol devices act as slaves, and are designed to enable scalable and fault tolerant system monitoring and imaging 1U appliance Collates information from concurrentcontrol devices Delivers highly polished, Web 2 GUI Manage anywhere from mobile, Iphone, PDA, PC etc Built to be a scalable interface Zero U, low-power appliance Monitors data from devices associated with local racks Power control and monitoring, Environmental information All Out of Band IPMI; SNMP for O/S utilisation 16 5V ports for wired sensors Connects to Ethernet management network Provides a scalable tree architecture

18 Features Monitoring Power from power clamps, third party PDUs & PMBus PSUs Environmental sensors: wired (5V) and wireless (866Mhz) Server hardware - IPMI, DCMI and Intel Node Manager support SNMP & WMI support for OS and VM monitoring; optional in-band daemon Reporting Power charge-back and ITUE metrics by group/customer/user/application Scalable, real-time data-centre views Extensive reporting of historical data Breach monitoring and reporting; Event data-base and visualisation Management Data Centre Inventory Power management (PDU and IPMI support) Event scheduling Serial-over-LAN & SSH terminals

19 Configurable Dashboard

20 Visualisation of real-time metrics - data centre view

21 Visualisation of real-time metrics - rack view

22 Dynamic graphing of historical metrics

23 Event Database with Visualisation

24 Scheduled Events

25 Energy Cost Schedule

26 Hardware repository: PDU association to servers

27 Planned Features Integration with third party VMs (VMWare, KVM, Hyper-V..) Dynamic orchestration of virtual machines Support for multiple sites