Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this article, we propose a machine learning-based approach that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. Our approach predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of our approach through both real test-bed experimentation and simulation. Our evaluation reveals that our approach achieves substantial reduction in total energy consumption compared to other state-of-the-art alternatives while experiencing marginal performance degradation in a few cases.

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches.

Public sexual harassment has emerged as a large and growing concern in urban Bangladesh, with deep and damaging implications for gender security, justice, and rights of public participation. In this paper we describe an integrated program of ethnographic and design work meant to understand and address such problems. For one year we conducted surveys, interviews, and focus groups around sexual harassment with women at three different universities in Dhaka. Based on this input, we developed "Protibadi", a web and mobile phone based application designed to report, map, and share women's stories around sexual harassment in public places. In August 2013 the system launched, user studies were conducted, and public responses were monitored to gauge reactions, strengths, and limits of the system. This paper describes the findings of our ethnographic and design-based work, and suggests lessons relevant to other HCI efforts to understand and design around difficult and culturally sensitive problems.