Latency-bandwidth tradeoffs in Internet applications

Wide-area Internet links are slow, expensive, and unreliable. This affects applications in two distinct ways. Back-end data processing applications, which need to transfer large amounts of data between data centers across the world, are primarily constrained by the limited capacity of Internet links. Front-end user facing applications, on the other hand, are primarily latency-sensitive, and are bottlenecked by the high, unpredictably variable delays in the wide-area network. Our work exploits this asymmetry in applications' requirements by developing techniques that trade off one of bandwidth and latency to improve the other. We first consider the problem of supporting analytics over the large volumes of geographically dispersed data produced by global-scale organizations. Current solutions for analyzing this data as a whole operate by copying it to a single central data center, an approach that incurs substantial data transfer costs. We instead propose an alternative geo-distributed approach, orchestrating distributed execution across data centers. Our system, Geode, incorporates two key optimizations --- a low-level syntactic network redundancy elimination mechanism, and a high-level semantically aware workload optimization process --- both of which operate by trading off increased processing overhead (and computation latency) within data centers for a reduction in cross-data center bandwidth usage. In experiments we find that Geode achieves an up to 360x cost reduction compared to the current centralized baseline on a range of workloads, both real and synthetic. Next, we evaluate a simple, general purpose technique for trading off bandwidth for reduced latency: initiate redundant copies of latency sensitive operations and take the first copy to complete. While redundancy has been explored in some past systems, its use is typically avoided because of a fear of the overhead that it adds. We study the latency-bandwidth tradeoff due to redundancy and (i) show via empirical evaluation that its use is indeed a net positive in a number of important applications, and (ii) provide a theoretical characterization of its effect, identifying when it should and should not be used and how systems can tune their use of redundancy to maximum effect. Our results suggest that redundancy should be used much more widely than it currently is

Proximity to Food Outlets and Diabetes-Related Health Outcomes: A Cross-Sectional Study in Robeson County, NC

Type 2 diabetes is a growing epidemic in the United States, and already affects 25.8 million Americans (8.3%) in 2011. The prevalence of type 2 diabetes has nearly tripled from 1990 to 2010 and is projected to increase. If this pattern continues, the Centers for Disease Control and Prevention (CDC) estimates that one-third of Americans will have type 2 diabetes by 2050. In North Carolina, this problem is particularly acute; the state has the 13th highest prevalence of diabetes at 9.8% of the general population. Robeson County—a rural area with a large American Indian population of Lumbee descent—has shown dramatically higher diabetes prevalence than the rest of the state, at 13.7%. The high prevalence of diabetes in Robeson County raises significant concerns about the long-term health status its residents. Research has shown that lifestyle modifications, including dietary changes, can reduce the development of diabetes, as well as the need for treatment of existing diabetes. Unfortunately, rural areas tend to have a dearth of healthy food retailers, such as supermarkets, while boasting a plethora of fast food options. Due to various barriers—such as distance to, and price of, healthy food options—low-income and minority groups living in rural areas are even less likely to have consistent access to healthy, affordable food. Without a healthy diet, it can be challenging for individuals to achieve optimal control over diabetes risk factors, such as A1c level, blood pressure level, and body mass index (BMI). Over time, poor eating behaviors can heighten one’s risk for developing diabetes, or experiencing diabetes-related complications such as kidney failure, amputation, or blindness. It is important to evaluate access to healthy food options in Robeson County to inform future intervention and policy action to reverse diabetes trends in this area. While many individuals living in rural areas lack access to healthy food options and are subsequently at risk for developing diabetes, low-income and minority groups face even higher risk for diabetes morbidity and mortality. Minority groups are disproportionately represented among the poor, and low socioeconomic status is often associated with limited access to affordable, healthy food. Robeson County, with nearly 30% of individuals in poverty, and nearly 40% of individuals of American Indian descent, has many residents that are particularly vulnerable to the risk factors that cause diabetes. At present, little is known about the specific interaction between geographic access to healthy foods and diabetes in a predominantly rural, low-income, and minority area, such as Robeson County. Researchers have begun using Geographic Information Systems (GIS) mapping technology to explore the food environment, as it offers the benefit of visually determining ‘food activity spaces,’ the geographic locations and variety of food outlets at which individuals shop. The impetus for using GIS in Robeson County came from the CEO of Robeson Health Care Corporation (RHCC), a federally qualified health center with four clinics serving patients in Robeson County. This research aims to use GIS to better understand the relationship between food access and various diabetes-related risk factors in Robeson County, North Carolina in order to ultimately inform community policy changes.Bachelor of Science in Public Healt

Low Latency Geo-distributed Data Analytics

Low latency analytics on geographically distributed dat-asets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single data-center significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distri-buted analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement op-timization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries ’ arrivals, and places the tasks to reduce network bottlenecks during the query’s ex-ecution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 re-gions using production queries show that Iridium speeds up queries by 3 × − 19 × and lowers WAN usage by 15% − 64 % compared to existing baselines