The annual Hajj presents diversified negative experiences to millions of pilgrims worldwide. The negative experiences and recommendations to overcome them as per pilgrims' feedback are yet to be analyzed from an aggregated perspective in the literature, which we do in this paper. To do so, first, we perform a large-scale survey (n=988) using our comprehensive questionnaire. Then, we perform both quantitative (e.g., clustering) and qualitative (e.g., thematic) analyses on the survey data. Our quantitative analysis reveals up to seven clusters of negative experiences. Further, going beyond the quantitative analysis, our qualitative analysis reveals 21 types of negative experiences, 20 types of recommendations, and nine themes connecting the negative experiences and recommendations. Accordingly, we reveal associations among the negative experiences and recommendations based on the themes in thematic analysis and present the associations through a tripartite graph. However, we have some limitations in this study, such as fewer female and young participants. In future, we plan to collect more responses from female and young participants and extend our work by analyzing linkages in the tripartite graph by augmenting the edges within the graph with appropriate weights. Overall, the findings of this study are expected to facilitate the prioritization of tasks for the management personnel in charge of the Hajj pilgrimage.

Mass gatherings (such as Hajj/Umrah), owing to their immensity, often present a variety of difficulties to the attendees. To have a comprehensive understanding of the difficulties as well as their potential remedies, surveying a good number of attendees is unavoidable and this can be facilitated through engaging data collectors. A crucial part here is identifying the integrity of the data collectors, which is yet to be explored in the literature to the best of our knowledge. To address this gap, in this study, we first perform a mass-scale data collection over Hajj/Umrah pilgrims through online (n = 236) and in-person (n = 752) surveys, where we cover a substantial part (n = 712) through paid data collectors (n = 53). We critically investigate data collection activities of the data collectors through focused group discussions involving expert reviewers. We explore two computing techniques to unveil the integrity of the data collectors from two different perspectives. Our study finds out influential (religious and socio-geographical) aspects that impact the data collection process. Besides, we find that the collaborative participation of expert reviewers is obligatory to scrutinize the data collectors' integrity. Additionally, our explored computing techniques, namely conflict analysis and learning-based analysis, can identify up to 74% and 99% of the unreliable data collectors respectively. We observe that, although these computing-based filtering can indicate the integrity up to a certain level, human-in-the-loop is unavoidable for concluding on the integrity. To the best of our knowledge, this is the first study of its kind (i.e., integrity analysis of the data collectors) in the literature.

Cooling energy consumption is one of the most significant parts of total energy consumed by distributed systems. However, little effort has been spent so far to integrate the cooling energy in simulators that are used for simulating distributed systems. Therefore, in this paper, we propose an integration of cooling energy consumption in a widely-known simulator of distributed systems namely SimGrid. Here, we present necessary energy models that are needed to measure cooling energy required for a distributed system. Subsequently, we perform necessary modifications in SimGrid to integrate the models. We perform rigorous simulation over different settings using our integrated modules of SimGrid. Alongside, we perform real experimentation using settings similar to that used in our simulation. We compare our simulation results against that we find from real experimentation. The comparison reveals applicability of our cooling energy integration in SimGrid for simulating diversified distributed systems.

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 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.