ABSTRACT The energy efficiency of MapReduce systems has become a crucial aspect of managing big data analytics infrastructure. Hadoop is the most popular open‐source implementation of the MapReduce paradigm. Its scheduler plays a key role in ensuring this efficiency. To minimize energy consumption in slot‐based Hadoop systems, various MapReduce scheduling techniques have been developed. However, YARN‐based Hadoop schedulers have not been given much attention in terms of improving energy efficiency. Thus, addressing the MapReduce scheduling problem for YARN‐based Hadoop is essential for promoting sustainable cloud computing. In this paper, we propose a framework for the online MapReduce scheduling problem, where jobs arrive randomly and their arrival times are unknown. To tackle this issue, we developed two online scheduling algorithms that greedily select the job with the lowest energy usage at each iteration. We evaluate the proposed methods for large‐scale, three separate workloads of standard benchmark jobs, namely, WordCount (CPU‐bound), TeraSort (I/O‐bound), and NutchIndexing (mix‐bound). We also evaluate the performance for a mixed workload comprising all three benchmark jobs. The experimental results show that the proposed method considerably minimizes energy consumption for all workloads compared to the existing FAIR and capacity schedulers. Particularly, we find it to be 35% more energy‐efficient than the FAIR scheduler, contributing to sustainable cloud computing.

Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources and retrieve complex biological events, such as cardiovascular volume overload, using measured lifestyle events. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on the relevant biological systems; they are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using the event operator language. The interface events can then be utilized to provide a continuous estimate of the biological variables relevant to the user's health state. The event-based framework also makes it easier to estimate which event is causally responsible for a particular change in the individual's health state.

Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related enjoyment using a Preferential Personal Food Model and their biological response to food using their Biological Personal Food Model. Inspired by the power of 3-dimensional color models for visual processing, we introduce a 6-dimensional taste-space for capturing culinary characteristics as well as personal preferences. We use event mining approaches to relate food with other life and biological events to build a predictive model that could also be used effectively in emerging food recommendation systems.

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geo-spatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

Managing health lays the core foundation to enabling quality life experiences. Modern multimedia research has enhanced the quality of experiences in fields such as entertainment, social media, and advertising; yet lags in the health domain. We are developing an approach to leverage multimedia systems for human health. Health is primarily a product of our everyday lifestyle actions, yet we have minimal health guidance on making everyday choices. Recommendations are the key to modern content consumption and decisions. Cybernetic navigation principles that integrate health media sources can power dynamic recommendations to dramatically improve our health decisions. Cybernetic components give real-time feedback on health status, while the navigational approach plots health trajectory. These two principles coalesce data to enable personalized, predictive, and precise health knowledge that can contextually disseminate the right actions to keep individuals on a path to wellness.