A System dynamics approach to data center capacity planning - A case study

This thesis is an empirical study where the System Dynamics methodology is applied to help the Chief Technical Officer of a Norwegian IT company, operating in the cloud computing industry, in planning for future data center capacity. Put simply, cloud computing is the provisioning of centralized IT services and infrastructure to businesses in an on-demand, reliable, and inexpensive fashion, which is why it is sometimes loosely referred to as computing as a utility'. The client's main interest in this project is to gain an analysis tool that can help in estimating the point in time at which the capacity limit of the company's data center in Oslo will be reached. This is a critical question for the business since setting up a new data center has a lead time of around one year, and it is essential to start planning for such an effort well beforehand. In this thesis, a System Dynamics model is built for this purpose, with its structure based on empirical knowledge elicited from the client of the project. Rigorous testing is applied to build confidence in the reliability and usefulness of the model. The model structure successfully replicates historical behavior of important variables in the system. The established robustness of the model qualifies it as suitable to use for policy and scenario testing. A few examples of such tests are carried out and documented in this report, including various tests regarding the central question of when the data center's capacity limit will be reached. This model can eventually become the basis of a management flight simulator that the client could use to try out different policies to see their consequences before implementing them in the real world. This project has been carried out with two overarching purposes, one professional and one academic. The professional goal, as already mentioned, is to help the client in medium-term capacity planning. The academic aspiration of the thesis, however, is to establish the usefulness of the System Dynamics methodology in data center planning and cloud computing business fields. To the best of the author's knowledge, no previous System Dynamics works have been carried out in this area. Yet, being dominated by aging chains, co-flows, accumulations, delays, and feedbacks, data center management is in this thesis demonstrated to be a promising area for applying System Dynamics.GEO-SD360JMASV-SYS

London's Housing Crisis; A System Dynamics Analysis of Long-term Developments: 40 Years into the Past and 40 Years into the Future

In London, housing affordability has been rapidly declining over the past few decades. Furthermore, London, and the UK in general, has experienced persistent volatility in house prices, new housing supply, and housing finance. These features characterise the main aspects of London’s housing crisis which is the topic of this PhD. Our existing understanding of this crisis remains largely fragmented and mostly qualitative. In this thesis, I build a novel quantitative system dynamics model based on existing literature and statistical data to explain developments in London’s housing system since 1980, with a particular focus on the feedback loops between house prices and housing credit. The model is shown to be capable of endogenously reproducing the salient features of the system’s past behaviour, such as the excessive growth in prices and housing credit as well as the characteristic boom-bust cycles. Extending the simulation into the future under business-as-usual continues to generate exponential growth and increasingly larger amplitude oscillations. Furthermore, I simulate a number of policies aimed at mitigating the unchecked growth and volatility. Supply side policies considered include a steep increase in affordable housing construction, a relaxation of planning restrictions, and a combination of the two. These policies show promise in slowing the growth in house prices (and housing debt) but do little to curb market volatility. Demand side policies considered include introducing a capital gains tax on all residential property, lowering average loan-tovalue ratios, enforcing historically anchored property valuations for mortgage lending, and a combination of all three. These policies, particularly when combined, appear to be highly effective in eliminating periodic oscillations. They also serve to slow down the worsening of affordability to some extent, but demand-side policies alone do not appear capable of stopping the trend in deteriorating affordability. In order to eliminate largescale market volatility and simultaneously stop the continual worsening of affordability, it is shown to be necessary to intervene on both sides of the problem with a portfolio of targeted policies. In conclusion, I argue that the unit of analysis in housing policy and discourse must become feedback loops rather than individual factors. Integrated, feedback-centred, dynamic simulation tools are needed in long-term planning for the affordability and stability of the housing market in London and in the UK. The system dynamics model introduced in this thesis serves as a proof of concept for a promising approach to policymaking in the area of the UK’s housing policy

A System dynamics approach to data center capacity planning - A case study

This thesis is an empirical study where the System Dynamics methodology is applied to help the Chief Technical Officer of a Norwegian IT company, operating in the cloud computing industry, in planning for future data center capacity. Put simply, cloud computing is the provisioning of centralized IT services and infrastructure to businesses in an on-demand, reliable, and inexpensive fashion, which is why it is sometimes loosely referred to as computing as a utility'. The client's main interest in this project is to gain an analysis tool that can help in estimating the point in time at which the capacity limit of the company's data center in Oslo will be reached. This is a critical question for the business since setting up a new data center has a lead time of around one year, and it is essential to start planning for such an effort well beforehand. In this thesis, a System Dynamics model is built for this purpose, with its structure based on empirical knowledge elicited from the client of the project. Rigorous testing is applied to build confidence in the reliability and usefulness of the model. The model structure successfully replicates historical behavior of important variables in the system. The established robustness of the model qualifies it as suitable to use for policy and scenario testing. A few examples of such tests are carried out and documented in this report, including various tests regarding the central question of when the data center's capacity limit will be reached. This model can eventually become the basis of a management flight simulator that the client could use to try out different policies to see their consequences before implementing them in the real world. This project has been carried out with two overarching purposes, one professional and one academic. The professional goal, as already mentioned, is to help the client in medium-term capacity planning. The academic aspiration of the thesis, however, is to establish the usefulness of the System Dynamics methodology in data center planning and cloud computing business fields. To the best of the author's knowledge, no previous System Dynamics works have been carried out in this area. Yet, being dominated by aging chains, co-flows, accumulations, delays, and feedbacks, data center management is in this thesis demonstrated to be a promising area for applying System Dynamics

Urban systems complexity in sustainability and health: an interdisciplinary modelling study

Background: Improving urban health and sustainability raises complex questions that are best addressed through interdisciplinary and even transdisciplinary approaches, in which scientific research and analysis and stakeholder engagement have important roles. In this study we report pilot work in Nairobi (Kenya) and London (UK) that uses innovative methods to integrate qualitative and quantitative modelling to provide evidence to support policy development for health and sustainability in these cities. Methods: We used two primary modelling methods, system dynamics and microsimulation, and sought to understand the value of these tools in combination to support policy decisions. System dynamics was used to establish an aggregated and non-linear causal map of the interconnections between diverse variables, and thus to gain insight into the policies and specific processes that need to be examined in further depth. System dynamics was a key tool for city-level stakeholder engagement. In part informed by the outcome of the system dynamics process, microsimulation was then used to quantify local effects on health of selected policy options. The results were mapped using geographic information systems methods. Findings: The combination of system dynamics and microsimulation models provided a framework that enhanced collective knowledge about the interrelationships of policy decisions, funding, public awareness, and environmental and health effects. Our initial participatory system dynamics work on air pollution in Nairobi found that a combination of policies that focus on households and outdoor air could reduce household air pollution by about 50%, leaving it still above WHO-recommended levels. Yet, the investments in monitoring and health impact assessment have the potential to trigger reinforcing mechanisms that create synergies among existing policies and increase the return on investment. Preliminary 106-year microsimulation runs of the effects of PM2·5 in London revealed that anthropogenic emissions are associated with about 2300 incident cases of ischaemic heart disease annually. The two methods appeared to have valuable complementarity in their focus on aggregated dynamics at the policy level versus local policy effects. Interpretation: The use of system dynamics can produce a quantitative model of the policy implementation process, including the organisational barriers and opportunities for change. This can be extended to include aggregate outputs from other models to quantify a more holistic and high-level quantitative model of the dynamics of selected policy questions. Together, these methods can estimate regional environmental and local health effects of selected policies, but also inform about overcoming the barriers to these policies. Funding: The Housing in Nairobi's Informal Settlements—A Complex Urban System project funded by the Engineering and Physical Sciences Research Council, and the Complex Urban Systems for Sustainability and Health project funded by the Wellcome Trust