The growth in the individual size and number of data centres has resulted in a relative shift in the energy footprint of the IT sector from devices to data centre and networks. The aggregate electricity demand of the cloud (including data centres and networks, but not devices) in 2011 was 684 billion kWh (if compared with the electricity demand of individual countries in the same year, the cloud would rank 6th in the world)5 and is predicted to increase by 81% by 20206. The energy costs of powering data centres is thus becoming a significant operational cost for data centre operators and has focused attention on improving the energy efficiencies of data centres and reducing their carbon footprint.
Whilst reductions in the individual energy consumption of servers due to new low power CPU architectures and multi-core designs have been readily implemented, there has been little improvement in the efficiency of server utilisation. As ‘on-demand’ services impose varying loads on a data centre, most servers will either be operating at a fraction of their capability or may even be unused yet still powered up. A 2008 survey by McKinsey & Company found that server utilization rarely exceeded 6% and
overall data centre utilization was as low as 50%7 whilst an NDRC study found that the average US server operates at only 5% -
15% utilization level while consuming 60% - 90% of its maximum system power8. A more recent IBM study indicated that despite hardware improvements, the mean server CPU utilization is still only 18% In order to reduce their hardware (i.e. server) costs and reduce their overall energy consumption data centres have begun to adopt virtualization, running multiple virtual machines (VMs) on each server. In a virtualized environment, live VM migration capabilities to relocate VMs both within single (and for large enterprises, across multiple data centres) can be exploited to achieve various resource management objectives, in particular reducing the number of active servers needed at any given time to meet a load (‘resource-load matching’) thus allowing inactive servers to be powered down (typically 15% - 30% of the servers
in a data centre are running but without meeting any of the load10). In addition virtualization eases server maintenance provisioning and fault tolerance management11.
In a modern data centre, the VM workload is dynamic, varying in response to user demand. However, data centre operators do not currently have a comprehensive automated system for optimally placing VMs and allocating the optimal number of servers to meet the workload requirements. This results in sub-optimal hardware utilization as mentioned above, high hardware (servers and storage) and facility CapEx, a high OpEx (energy consumption) and a poorer end user experience (lower response
time and/or intermittent availability). The few existing tools for managing data centre workloads do not scale, forcing data centres to be sub-divided into management entities of circa 100 servers, leading to greater operational complexity.
The Specific Objectives of FishDirector Innovation Project – Our FishDirector software has been developed to the stage
where it is ready for testing under real world conditions. We therefore propose a Feasibility Study to verify the technical and
economic viability of its implemtation including a technology state-of-the-art review to assess potential competitors, market
analysis, identification and planning for testing/demonstration, and formulation of an elaborated business plan, with a view to
continue to an application to phase 2 of the SME Instrument. We therefore propose to conduct a phase 1 Feasibility Study
with the following Objectives:
F1 - Market and competitor analysis:
• Assess user needs, demand and market segments and sizes.
• Identify purchase processes, channels and gatekeepers.
F2 - Cost assessment:
• Assess the product development, production and demonstration/testing costs.
F3 - Business plan development:
• Determine the optimal go-to-market strategy.
• Decide upon likely revenue stream(s) arising from implementation of the strategy.
• Develop the price model and structure.
• Conduct risk assessment.
• Formulate an exploitation and dissemination plan.
• Explore the possibilities and implications of product adaptation or customisation for third-party customers.
F4 - IPR analysis and novelty verification/network patent analysis (see section 2.2).
F5 - Demonstration design:
• Establish the requirements (hardware, personnel) for undertaking demonstrations of FishDirector at a data centre
under real load operating conditions.
F6 - Plan for phase 2 development
• Formulate a detailed work plan for the phase 2 project.
