Ray Carroll, TSSG - Sustainable and Energy Efficient Data Centre
Bio-inspired Service Use Cases: Sustainable and Efficient Data-Centres Ray Carroll, Sasitharan Balasubramaniam, Dmitri Botvich, Willie Donnelly
Overview <ul><li>Background and Motivation </li></ul><ul><li>Biological Behaviours </li></ul><ul><li>Bio-Inspired Use Cases </li></ul><ul><ul><li>Service Lifecycle Management </li></ul></ul><ul><ul><li>Load Balancing </li></ul></ul><ul><ul><li>Service Discovery </li></ul></ul><ul><ul><li>Service Composition </li></ul></ul><ul><ul><li>Sustainability and Energy Efficiency </li></ul></ul><ul><li>Conclusions </li></ul>
Background & Motivation Future Internet <ul><li>increased numbers of users </li></ul><ul><li>increased number of services </li></ul><ul><li>increased types of service (including composed) </li></ul><ul><li>increased modes of access </li></ul><ul><li>increased variation in service usage patterns </li></ul>Increased Data-Centre Burden (Management and Energy Usage) Bio-Inspired Biology has evolved solutions to similar problems over millions of years so why not copy nature. = Image from CONRO Image from Wyss Institute
Biological Service Behaviour Bio-Services Migration : - Food Supplies, Safety etc Replication : - Sustainability of species Chemotaxis : - Navigation and Search Evolution : - Biological Optimisation Community : - Support, Coordination and Communication Energy Budget : - Adaptation to environmental conditions Services augmented with biology mimicking behaviour
Service Lifecycle Management Energy Budgets & Trading Services require energy to function. Services paid energy dividend for processing user requests Services pay for server resources (e.g. CPU) Survival of the fittest - Energy required to replicate and expand gradient so useful services flourish, redundant services die.
Load Balancing Migration Replication Energy Budget Service Migration allows system to react to changing server load Service Replication allows system react to changing request load Energy Budgets ensures system returns to equilibrium when load drops
Service Discovery Service emits gradient creating a gradient field Service queries follow gradient to source # of messages & blocking rate vs. varying gradient size Chemotaxis Energy Budget Use chemical gradient to aid distributed service discovery Gradient field grows and shrinks depending on service energy Image from blog.memsic.com
Service Composition Community v Non-Community Composition Completion at varying gradients Community v Non-Community Message Volume at varying gradients Community Service records interactions with other services Community provides recommendations based on these interactions Use Community to minimize overhead in distributed service composition
Sustainability and Energy Efficiency Green Migration Migrate services to DCs with more amenable characteristics Countries produce varying levels of renewable energy, have different temperature and C0 2 profiles Use service migration to maximize renewable energy usage and reduce cooling energy usage ICT Industry consumes approx. 2% of the total worldwide CO2 emissions. As much as the aviation industry.
Sustainability and Energy Efficiency Evolution Evolutionary Genetic Algorithm to orchestrate migrations GA models evolution, by generating a population of solutions to a problem (called chromosomes), and combining and/or mutating these chromosomes over time to produce the ‘fittest’ solution.
Sustainability and Energy Efficiency Overall Quantity of Renewable Energy Quantity of Renewable Energy per DC Link Utilisation in underlying networks Renewable Energy from Dynamic Traffic
Conclusion <ul><li>Data-centres and services will be core elements of the Future Internet. </li></ul><ul><li>Any Future Internet solution will need consider the efficient management and the sustainability of data-centres from the outset. </li></ul><ul><li>Our work has shown that biologically inspired mechanisms hold potential for providing benefit in both these areas. </li></ul>
References Carroll, R, Balasubramaniam, S, Botvich, D and Donnelly, W, “ Dynamic Optimization Solution For Green Service Migration in Data Centres ”, to appear in proceedings of International Conference on Communication (ICC), Kyoto, Japan, June 2011. Carroll, R, Balasubramaniam, S, Botvich, D and Donnelly, W, “ Bio-inspired Service Management Framework: Green Data-Centres Case Study ”, in proceedings of 7th International Symposium on Frontiers of Information Systems and Network Applications (FINA), Singapore, 2011. Carroll, R, Balasubramaniam, S, Botvich, D and Donnelly, W, “ Application of Genetic Algorithm to Maximise Clean Energy usage for Data Centres” , to appear in proceedings of Bionetics 2010, Boston, December 2010. Balasubramaniam, S., Botvich, D., Carroll, R., Mineraud, J., Nakano, T., Suda, T., Donnelly, W., “ Adaptive Dynamic Routing Supporting Service Management for Future Internet ”, Globecom 2009, Hawaii, December 2009. Carroll, R., Balasubramaniam, S., Botvich, D., Donnelly, W., “ The Effect of Community on Distributed Bio-Inspired Service Composition ”, proceedings of 4th International Workshop on Natural Computing (IWNC09), Japan, September 2009.