Message from the editor

Diwakar Krishnamurthy, University of Calgary

I'd like to once again thank our hardworking team of information officers for bringing out this issue.

Dr. Cristina Rottondi continues her series of articles on issues pertaining to the smart grid.
Dr. Burak Kantarci and Dr. Massimo Tornatore discuss greening of data centers and cloud computing systems in separate articles. Finally, in our conference showcase Dr. Patricia Lago summarizes the first two editions of the ICT for sustainability conference that she helped co-organize. As always, I encourage you to get back to us with comments, feedback, and contributions.

Demand Management of Residential and Commercial Users: Techniques and Approaches

by Cristina Rottondi, Politecnico di Milano

•  Local generators: are local energy plants (e.g., photovoltaic (PV) plants), which generate electric energy
  that can be either used locally or injected into the grid.
•  Smart devices: are electric appliances which are able to monitor themselves and that can be remotely controlled.
•  Sensors: are used to monitor several data of interest, such as the user's position within the house, temperature and light.
  Moreover, in the case of traditional devices, power meter sensors can be used tomonitor and control these appliances.
  
  
•  Energy storage systems (ESSs): are storage devices which allow the system to be flexible in managingelectric resources.
  
  
•  Energy management unit (EMU): exchanges information with the other elements of the system and manages the electric resources of users based on an intelligent mechanism. Specifically, this mechanismdefines the schedule of appliances, the operating plan of ESSs and the demand and supply profiles (i.e.,when to buy and inject energy into the grid).
  
  
• Smart grid domains: are the distribution, operation, market, service provider and customers domains of
  the smart grid [2]. A utility company, which is part of the market domain, supplies electric energy to users
  from whom it receives payments according to energy tariffs. Eventually, also a profit-neutral entity, called
  independent system operator (ISO), can be introduced which stands between suppliers and customers.
  The ISO procures electricity from suppliers and sells it to users with the goal of matching supply and
  demand.
  

• Demand-response (DR) vs. demand-side management (DSM): DR methods are reactive solutions designed to encourage consumers to dynamically change their electricity demand in the short-term, according to signals provided by the grid/utilities such as prices or emergency condition requests. Typically, these techniques are used to reduce the peak demand or to avoid system emergencies such as blackouts. On the other hand, DSM represents a proactive approach aimed at making consumers energy-efficient in the long-term.
• Individual users vs. community of users: demand management mechanisms can be designed to control electric resource of individual users [4]. However, this approach may have some undesirable effects since it may disturb the natural diversity of consumers, which is fully exploited by the power system to optimize its efficiency in generating and distributing energy. To contain these unwanted side-effects, management mechanisms can control community of users, thus managing their resources based on a system-wide perspective [5]. The natural extension of demand management mechanisms for communities of users is represented by solutions designed for microgrids, which are small-scale versions of electricity systems.
• Centralized vs. distributed: in case of demand management mechanisms for community of users, two different approaches can be used to control the electric resources of consumers. In centralized solutions, based on optimization methods, all users cooperate in managing their resources and the EMU optimizes a shared utility function [6]. On the other hand, in the case of distributed solutions, users take decisions locally and individually and conflicts among consumers are modeled based on game theoretic frameworks [7].
• Deterministic vs. stochastic: several parameters of demand management systems, such as renewable energy generation, devices usage and energy prices for future periods, are estimated by prediction methods. In the case of deterministic techniques, these parameters are defined as deterministic data [8], while in the case of stochastic methods, they are represented as random variables in order to consider uncertainty in the decision-making process [9].
• Day-ahead vs. real-time: demand management mechanisms can use different time scales in controlling the resources of customers. In the day-ahead case, the operating plan of electric resources of users is defined over the next 24-hour time period (or an alternative future time horizon) [6]. To this end, demand management mechanisms require predictions/estimates of some parameters of the system, such as the energy generation of local sources, electricity prices and devices usage preferences for the next day. These data can be defined by learning algorithms executed by the EMU based on information provided by sensors, smart appliances or external sources (e.g., weather forecast). On the other hand, in the real-time case, the users' plan is defined in real-time based on real-time events and data [10].

References:

Can data centers sustain through dynamic electricity pricing markets?

Burak Kantarci, Clarkson University, USA and Hussein T. Mouftah, University of Ottawa, Canada

Since the Environmental Protection Agency’s (EPA) data center report to US congress in 2006 [1], data center sustainability has been a phenomenal topic as data centers have been pointed as the main polluters of the ICTs. Especially, with the advent of cloud computing, corporate data centers are evolving to giant cloud data centers with enhanced computing, storage and communications capability. Indeed, these enhancements are at the expense of increased power consumption which does not only denote the computing power but other non-IT power components such as cooling, uninterrupted power supply, lighting and so on. It can be intuitively said that the lower the computing power gets the lower the non-IT power is. Is it really the gigantic power consumption that makes data centers the major polluters of the cloud dominated era?

As everyone pursuing research, development or service in this subject knows, Greenhouse Gas (GHG) emissions are the main sustainability indicators of any system. Therefore, greening the data centers via energy savings holds if data centers are always powered by the same energy source. Local solutions can be energy-aware workload and virtual machine placement [2], monitoring hot and cool isles in the data center for workload (re)placement [3][4], and sleep scheduling of physical servers. In an inter-data-center network environment, virtual data center (VDC) mapping with the objective of reduced carbon footprint and increased revenue of the provider can be a promising solution for the inter-data center network [5].

Apart from the discussion about consumer behavior, as a part of demand side management, dynamic electricity pricing has potential in integrating renewable resources into the electricity grid. Thus, electricity prices can be lowered when the renewable energy supply is higher and vice versa [6]. It can be hypothesized that data centers can take advantage of dynamic electricity pricing if they can migrate workloads among themselves. Such a system will require demand profile prediction by a centralized information service which communicates with network resource managers, smart grid communication network, resource coordinators and computing resource managers.

We have recently proposed a framework which enables workload migration among data centers based on time varying electricity prices at different locations [7]. A minimalist visualization of the proposed system mainly consists of the inter-data center network and the Smart Grid Communication Network (SGCN). The inter-data center network consist of an IP over WDM transport network and the data centers associated with the backbone nodes, and the SGCN consist of the power generators, substations, transformers, smart meters and the control center [8]. SGCN components are interconnected via aggregation switches which are connected to the inter-data center network via a metro ring network. Based on the changes in the demand profile, the inter-data center network is virtualized periodically in order to achieve the design goals, namely energy-efficiency and minimum electricity costs for the network and the data center operators.

Our research has reported that such a system is promising in terms of electricity bill costs for the data center operators, as well as the network operators when ToU tariffs are adopted. Moreover, energy consumption is also reduced if each data center is obliged to keep certain amount of its original workload prior to migration, which is further promising in terms of data center sustainability. Further, having shown that path delays for workload migration as well as service provisioning can be limited if ToU-awareness is integrated into the information service which is expected to virtualize the network and determine the workload migration map.

Despite the advantages of dynamic electricity price-aware design of inter-data center networks, the following points remain as challenging open issues for the researchers in this field:

• Determining the workload type(s) to be migrated: As research reports that each data center has to host a certain amount of the workload prior to migration, some workload types can be identified in advance and enforced to remain in the original data center. Service Level Agreements play critical role in the decision of workload types to be migrated.

• Determining the workload migration time dynamically: In our recent research, we have let the information service periodically calculate a workload migration map and reconfigure the virtualized topology of the inter-data center network. Based on the dynamics of varying electricity prices at different locations, workload migration, as well as topology reconfiguration can be triggered adaptively to ensure further sustainability of the data centers.
  
  
• Minimum QoS degradation: Workload migration and topology reconfiguration should ensure minimum QoS degradation. Thus, service disruption for particular workload types should strictly be avoided. The easiest method to avoid service disruption is to avoid migration of the workload of higher priority service to another data center. On the other hand, corresponding workload’s remaining in the original data center increases the electric bills as the workload is not taking advantage of time varying electric prices. Besides, this situation may degrade sustainability since the workload is not migrated towards the locations where renewable resources are supposed to be available. Trade-off mechanisms should be adopted to address this conflict.
  
  
• The impact of the several time varying electricity pricing policies: Although the entire discussion focuses on TOU pricing, real time pricing (RTP) and critical peak pricing (CPP) are also possible dynamic pricing policies however, the consumers are more reluctant to switch to the tariffs where electricity prices vary more frequently. On the other hand, assuming that dynamic electricity prices have the potential to reflect the availability of renewable resources, data centers can further benefit from other highly dynamic pricing policies such as RTP and CPP. Therefore, researchers are encouraged to focus on consumer behavior models to help RTP and/or CPP-based solutions become comfortably adopted.
  
  
• Location policies for new data centers: The crucial questions for data center operator are the following: Where to build the data center? Here are some viable strategies: “Build the data center where the demand is more intense”, “Build the data center where more renewables are available,” or “Build the data center where electricity prices are lowest (assuming flat rates are applied)”. Data center location problem has been widely studied [9], and all of these strategies have pros and cons in several aspects. If the dynamic electricity pricing policies can be considered as indicators of renewable energy; the data center operators can minimize their operational expenses via long term analysis of varying electricity prices, which in turn leads to less GHG emissions and more sustainable environment.

Greening the cloud using renewable-energy-aware service migration

U Mandal, MF Habib, S Zhang, M Tornatore, B Mukherjee IEEE Network Vol. 27, no. 6, pp. 36-43, 2013

Conference Showcase

Lorenz Hilty, Mattias Höjer, Patricia Lago and Josefin Wangel  

    
ICT is a transformational technology bearing both opportunities and risks for sustainable development. The "ICT for Sustainability" (ICT4S) conferences areabout utilizing the transformational power of ICT for making our world more sustainable: saving energy and material resources by creating more value from less physical input, increasing quality of life for ever more people without compromising future generations´ ability to meet their needs. While the potential of ICTs´ contribution to sustainable development has been increasingly recognized during the last decades, implementing this potential has proven to be a challenge. One of the main obstacles is the cooperation between the academic disciplines, such as engineering sciences and social sciences, but also between academia, industry, and politics. From its beginning, the conference therefore had a strong focus on exchanging ideas and mediating between different intellectual cultures. 

References

[1] ICT for Sustainability: Proceedings of the First International Conference on Information and Communication Technologies for Sustainability. E-Collection ETH Institutional Repository (2013) http://dx.doi.org/10.3929/ethz-a-007337628

[2] Modelling and Evaluating the Sustainability of Smart Solutions (Thematic Issue), Environmental Modelling and Software 56 (2014) http://www.sciencedirect.com/science/journal/13648152/56

[3] Hilty, L.M.; Aebischer, B. (eds.): ICT Innovations for Sustainability. Advances in Intelligent Systems and Computing 310. Springer International Publishing (2015) http://link.springer.com/book/10.1007%2F978-3-319-09228-7

[4] Höjer, M., Lago, P., and Wangel J. (eds): Proceedings of the 2014 conference ICT for Sustainability, Atlantis Press (2014)

http://www.atlantis-press.com/php/pub.php?publication=ict4s-14

[5] EnviroInfo and ICT4S Conference 2015: 2015.ict4s.org