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We present results from an ethnographic inquiry of technology-based learning at an after-school learning center in Mumbai (India) that caters to students from neighboring slum communities.We conducted participant observation for 120 hours and 58 semi-structured interviews with different stakeholders (including teachers, staff, parents, and students) at the center over nine weeks from December 2015 to July 2016. Taking an assets-based approach in an underserved context, we uncover the role of care as a resource and present the rich and varied caring behaviors enacted in this sociotechnical system.We then discuss how care effects a greater sense of ownership, interdependency, and community. Examining the role of aligning values in motivating caring behavior, we conclude with recommendations for supporting, leveraging, and extending care via technology design in an underserved, technology-enhanced learning environment.

Data centers are becoming increasingly popular for their flexibility and processing capabilities in the modern computing environment. They are managed by a single entity (administrator) and allow dynamic resource provisioning, performance optimization as well as efficient utilization of available resources. Each data center consists of massive compute, network and storage resources connected with physical wires. The large scale nature of data centers requires careful planning of compute, storage, network nodes, interconnection as well as inter-communication for their effective and efficient operations. In this paper, we present a comprehensive survey and taxonomy of network topologies either used in commercial data centers, or proposed by researchers working in this space. We also compare and evaluate some of those topologies using mininet as well as gem5 simulator for different traffic patterns, based on various metrics including throughput, latency and bisection bandwidth.

Smart grids are becoming ubiquitous today with proliferation of easy to install power generation schemes for Solar and Wind energy. The goal of consuming energy generated locally instead of transmitting it over large distances calls for systems that can process millions of events being generated from smart plugs and power generation sources in near real time. The heart of these systems often is a module that can process dense power consumption event streams and predict the consumption patterns at specific occupational units such as a house or a building. It is also often useful to identify outliers that are consuming power significantly higher than other similar devices in the occupational unit (such as a block or a neighbourhood). In this paper, we present a system that can process over a million events per second from smart plugs and correlate the information to output both accurate predictions as well as identify outliers. Our system is built from the ground up in C++ achieving very high throughput with very low CPU capacity for processing events. Our results show that the throughput of our system is over a million events per second while using under 20% of one core.

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