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Emergency Demand Response (EDR) has attracted research attention in recent years with its critical role in smart grids . Even though there are numerous potential participants for EDR, we especially focus on EDR, especially within datacenters and buildings, due to their huge power consumption yet flexible control knobs for power shedding. To reduce the deployment cost, many edge datacenters now are co-located inside buildings, which are responsible for power and IT infrastructure (called mixed-use buildings). In this paper, we consider a scenario that has not been addressed in the literature, in which multiple loads in geographically Distributed Mixed-use Buildings (geo-MUBs) can team up to participate EDR. We then design a mechanism that can coordinate tenants and geo-distributed buildings to minimize the system cost for EDR based on a robustly distributed framework, Alternating Direction Method of Multipliers (ADMM). In this mechanism, we also design a privacy-preserving scheme to conceal all tenants' transactions by using a lightweight algorithm. Simulation results show that our proposed method can reduce the total cost by 48.8% compared to existing approaches while satisfying all tenants constraints.

Today, Model predictive control (MPC) has largely been used to optimize energy consumption and maintain thermal comfort in buildings. However, to build an online MPC model in building, the dynamics of the physical system must be accurately modeled, which is a time-consuming and costly task. Neural network models help to overcome the modeling problems especially with the availability of historical data. This research presents a novel online data-driven control framework named Model Predictive Control via Genetic algorithm (MPC-GA) allowing the optimal operation of the heating, ventilation, and air conditioning system and has been experimentally validated in a multi-zone retail building. The MPC-GA combines an attention-based neural network time series multivariate prediction model with a MPC framework. The prediction model used a dual-stream neural networks based on multivariate time series of controlled and uncontrolled inputs. The attention mechanism is applied on controlled parameters to give them more weight to better predict the zone temperature. The prediction model is used as input for the optimization framework which minimizes: energy consumption, peak demand and discomfort during occupied hours under self-tuned setpoint , temperature ramp and equipment cycling constraints. A heuristic search algorithm using a genetic algorithm is used to solve the online data-driven MPC-GA models and obtain the future optimal combination settings of all controls for all the zones over a prediction horizon. The benchmark results showed that the MPC-GA outperforms RBC control systems with more than 50% and 80% reduction in energy consumption and discomfort respectively.

Among the innovative approaches to reduce the greenhouse gas (GHG) emissions of data centres during their use phase, cloud computing systems relying on data centres located in different regions appear promising. Cloud computing technology enables real-time load migration to a data centre in the region where the GHG emissions per kWh are the lowest. In this paper, we propose a novel approach to minimize GHG emissions cloud computing relying on distributed data centres. Unlike previous optimization approaches, our method considers the marginal GHG emissions caused by load migrations inside the electric grid instead of only considering the average emissions of the electric grid's prior load migrations. Results show that load migrations make it possible to minimize marginal GHG emissions of the cloud computing service. Comparison with the usual approach using average emission factors reveals its inability to truly minimize GHG emissions of distributed data centres. There is also a potential conflict between current GHG emissions accounting methods and marginal GHG emissions minimization. This conflict may prevent the minimization of GHG emissions in multi-regional systems such as cloud computing systems and other smart systems such as smart buildings and smart-grids. While techniques to model marginal electricity mixes need to be improved, it has become critical to reconcile the use of marginal and average emissions factors in minimization of and accounting for GHG emissions.

In order to satisfy the high bandwidth and performance demands of applications, host servers are built with multiple network interfaces, and a data center network consists of multiple redundant links. It is important to make efficient use of all the available network capacity, using multiple physical paths whenever possible, but traditional forwarding mechanisms using a single path are not able to take advantages of available multiple physical paths. The state-of-the-art MPTCP (Multipath Transmission Control Protocol) solution uses multiple randomly selected paths, but cannot give total aggregated capacity. Moreover, it works as a TCP process, and so does not support other protocols like UDP. This paper presents an alternative solution using adaptive multipath routing in a Layer-2 network with static (capacity and latency) metrics, which adapts link and path failures. This solution provides in-network aggregated path capacity to individual flows, as well as scalability and multitenancy, by separating end-station services from the provider's network. The results of deploying a proof-of-concept prototype on a data center testbed, which show the aggregated path capacity per flow, demonstrate an improvement of 14% in the worst bisection bandwidth utilization, compared to the MPTCP with 5 subflows.

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.