Energy consumption prediction based on time-series models for CPU-intensive activities in the cloud

Due to the increasing energy consumption in cloud data centers, energy saving has become a vital objective in designing the underlying cloud infrastructures. A precise energy consumption model is the foundation of many energy-saving strategies. This paper focuses on exploring the energy consumption of virtual machines running various CPU-intensive activities in the cloud server using two types of models: traditional time-series models, such as ARMA and ES, and time-series segmentation models, such as sliding windows model and bottom-up model. We have built a cloud environment using OpenStack, and conducted extensive experiments to analyze and compare the prediction accuracy of these strategies. The results indicate that the performance of ES model is better than the ARMA model in predicting the energy consumption of known activities. When predicting the energy consumption of unknown activities, sliding windows segmentation model and bottom-up segmentation model can all have satisfactory performance but the former is slightly better than the later.

An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Obesity prevention : the role of policies, laws and regulations

The commercial drivers of the obesity epidemic are so influential that obesity can be considered a robust sign of commercial success – consumers are buying more food, more cars and more energy-saving machines. It is unlikely that these powerful economic forces will change sufficiently in response to consumer desires to eat less and move more or corporate desires to be more socially responsible. When the free market creates substantial population detriments and health inequalities, government policies are needed to change the ground rules in favour of population benefits.
Concerted action is needed from governments in four broad areas: provide leadership to set the agenda and show the way; advocate for a multi-sector response and establish the mechanisms for all sectors to engage and enhance action; develop and implement policies (including laws and regulations) to create healthier food and activity environments, and; secure increased and continued funding to reduce obesogenic environments and promote healthy eating and physical activity.
Policies, laws and regulations are often needed to drive the environmental and social changes that, eventually, will have a sustainable impact on reducing obesity. An 'obesity impact assessment' on legislation such as public liability, urban planning, transport, food safety, agriculture, and trade may identify 'rules' which contribute to obesogenic environments. In other areas, such as marketing to children, school food, and taxes/levies, there may be opportunities for regulations to actively support obesity prevention. Legislation in other areas such as to reduce climate change may also contribute to obesity prevention ('stealth interventions'). A political willingness to use policy instruments to drive change will probably be an early hallmark of successful obesity prevention.

Balancing comfort expectations and greenhouse gas emissions, thermal comfort in office buildings in a changing climate

According to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the construction sector has the greatest potential for climate change mitigation. This work investigates the potential for climate change mitigation in naturally ventilated and mixed mode office buildings, by evaluating the range of influence of building design and occupants on greenhouse gas emissions as well as thermal and visual comfort.

Thermal comfort is evaluated according to the EN 15251 adaptive thermal comfort model, visual comfort is based on daylight autonomy and view. Parametric studies have been conducted based on building simulation for the climate of Athens, Greece. Input data are based on a literature review, and on results from a field study conducted among office occupants and architects in Athens.

The results show that the influence of occupants on greenhouse gas emissions is larger than the influence of building design. Energy saving office equipment, as well as active use of building controls for shading and lighting by occupants are crucial parameters regarding the reduction of CO2 emissions. In mixed mode buildings, the coefficient of performance of the cooling system is an important parameter as well. Regarding thermal and visual comfort, the influence of building design is predominant. A green building, well protected against heat from the sun and able to balance solar and internal heat gains, provides higher comfort levels and is less affected by the influence of occupants. In mixed mode buildings, building design is the predominant influence on the magnitude of cooling loads. A hot summer including heat waves can significantly reduce thermal comfort and increase the resulting greenhouse gas emissions. Green buildings are least affected by these influences.

The EN 15251 adaptive thermal comfort model provides a thermal comfort evaluation method valid throughout Europe. However, for the Mediterranean climate of Athens, Greece, most of the configurations investigated within this study do not meet the requirements according to this model. EN 15251 refers to an adaptive thermal comfort model for naturally ventilated and to a static model for mechanically ventilated buildings. For mixed mode buildings, the static model is recommended, but literature indicates that occupants in those buildings might be more tolerant towards higher temperatures. The hypothetical application of the EN 15251 adaptive thermal comfort model in mixed mode offices, as investigated in this study, shows potential for greenhouse gas emission savings. However, this influence is small compared to that of building design and occupants. Conclusions are drawn regarding the categorisation and exceeding criteria according to EN 15251 adaptive thermal comfort model for offices in a Mediterranean climate.

The results of this work show, that not only green buildings, but also green occupants can significantly contribute to the mitigation of the climate change. Mechanisms of the real estate market as well as the lifestyle of occupants are important influences in this context. Sustainability therefore refers to finding the right balance between occupant’s comfort expectations and resulting greenhouse gas emissions for a specific building, rather than optimisation of single parameters

Commentary : Obesity - why governments must act

The commercial drivers of the obesity epidemic are so influential that obesity can be considered a robust sign of commercial success – consumers are buying more food, more cars, and more energy-saving machines (1). It is unlikely that these powerful economic forces will change sufficiently in response to consumer desires to eat less and move more, or to corporate desires to be more socially responsible. When the 'free market' creates substantial population detriments and health inequalities, government policies are needed to change the ground-rules in favour of population benefits.

Temperature-agile and structure-tunable optical properties of VO 2/Ag thin films

By integrating together VO2’s unique near-room-temperature (RT) semiconductor–metal (S–M) phase transition with a thin silver (Ag) layer’s plasmonic properties, VO2/Ag multilayers could present a much enhanced optical transmission change when increasing the temperature from RT to over VO2’s S–M phase-transition temperature. Changing VO2 and Ag layer thicknesses can also significantly tune their transmission and absorption properties, which could lead to a few useful designs in optoelectronic and energy-saving industries.

Influence of growth rate retardation on time budgets and energetics of Arctic Tern Sterna paradisaea and Common Tern S. hirundo chicks

Time budgets of free-living chicks of Arctic Terns Sterna paradisaea and Common Terns S. hirundo throughout development are presented with special reference to changes in time allocation when growth rate varies. Chicks of both species were inactive most of the time observed (87%). Time allocated to the different behaviours changed during development and was generally better correlated with body mass than age. Slower growing nestlings were brooded more and allocated more time to quiescence and less time to locomotion, preening, begging and attacking (the latter two significant only for the Arctic Tern). The energetic implications of variation in time budgets with age and growth rate were considered. Parental brooding resulted in an average energy saving of nearly 40% of an individual nestling's thermoregulatory costs. Whereas thermoregulatory costs remained nearly unchanged in Arctic Tern chicks, these were negatively correlated with growth rate in Common Terns. Tentatively, we estimated a 30% reduction in a nestling's total energy requirement for a 50% reduction in average growth rate for both species.

Selective, spontaneous one-way oil-transport fabrics and their novel use for gauging liquid surface tension

Thin porous materials that can spontaneously transport oil fluids just in a single direction have great potential for making energy-saving functional membranes. However, there is little data for the preparation and functionalities of this smart material. Here, we report a novel method to prepare one-way oil-transport fabrics and their application in detecting liquid surface tension. This functional fabric was prepared by a two-step coating process to apply flowerlike ZnO nanorods, fluorinated decyl polyhedral oligomeric silsesquioxanes, and hydrolyzed fluorinated alkylsilane on a fabric substrate. Upon one-sided UV irradiation, the coated fabric shows a one-way transport feature that allows oil fluid transport automatically from the unirradiated side to the UV-irradiated surface, but it stops fluid transport in the opposite direction. The fabric still maintains high superhydrophobicity after UV treatment. The one-way fluid transport takes place only for the oil fluids with a specific surface tension value, and the fluid selectivity is dependent on the UV treatment time. Changing the UV irradiation time from 6 to 30 h broadened the one-way transport for fluids with surface tension from around 22.3 mN/m to a range of 22.3-56.7 mN/m. We further proved that this selective one-way oil transport can be used to estimate the surface tension of a liquid simply by observing its transport feature on a series of fabrics with different one-way oil-transport selectivities. To our knowledge, this is the first example to use one-way fluid-transport materials for testing the liquid surface tension. It may open up further theoretical studies and the development of novel fluid sensors.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Sampling bloom filter-based detection of unknown RFID tags

Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30 000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.

Network coding based converge-cast scheme in wireless sensor networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.

Energy efficient envelope design for high-rise apartments

The energy required to create a comfortable living environment in  high-density cities in hot and humid climates usually demands a substantial electricity usage with an associated environmental burden. This paper describes an integrated passive design approach to reduce the cooling requirement for high-rise apartments through an improved building envelope design. The results show that a saving of 31.4% in annual required cooling energy and 36.8% in the peak cooling load for the BASECASE apartment can be achieved with this approach. However, all the passive strategies have marginal effect on latent cooling load, often less than 1%.

Power saving in sensor networks via mobile base station repositioning

This research investigated the effect of dynamically repositioning the geographic location of a mobile base station within a sensor network in order to reduce energy consumption and increase network lifetime. Through simulation and experimental methodology, the proposed approach outperformed existing methods by extending network lifetime whilst reducing energy consumption.

Energy-efficient management of data center resources for cloud computing : a vision, architectural elements, and open challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.