CitationBilal ABSTRACTOptimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.

Vicarious traumatization is now a well-known entity and may have negative influences on those that are involved in rescue efforts in any disaster or traumatic events. Healthcare workers work with trauma survivors and witness an immense array of gruesome and ghastly images. This work has the potential to cause those engaged in rescue efforts to become affected subconsciously.Job-related stress may cause psychological symptoms in care providers who provide support and listen to the survivors' account of trauma. A therapist working in disaster situations may become a victim of psychological anguish—undermining their physical and mental well-being as well as their profession, adversely affecting their traumatized patients, and leading to a counter-productive therapist-survivor relationship.This significant theme of secondary trauma must be recognized in relief workers at early stages and must be addressed at an individual as well as organizational level. The key may lie in turning to social supports, adapting positive coping mechanisms, and subsequently seeking mental health consultation. Further research is required in this area to determine the best resolution.

Finding good cloud configurations for deploying a single distributed system is already a challenging task, and it becomes substantially harder when a data analytics cluster is formed by multiple distributed systems since the search space becomes exponentially larger. In particular, recent proposals for single system deployments rely on benchmarking runs that become prohibitively expensive as we shift to joint optimization of multiple systems, as users have to wait until the end of a long optimization run to start the production run of their job. We propose Vanir, an optimization framework designed to operate in an ecosystem of multiple distributed systems forming an analytics cluster. To deal with this large search space, Vanir takes the approach of quickly finding a good enough configuration and then attempts to further optimize the configuration during production runs. This is achieved by combining a series of techniques in a novel way, namely a metrics-based optimizer for the benchmarking runs, and a Mondrian forest-based performance model and transfer learning during production runs. Our results show that Vanir can find deployments that perform comparably to the ones found by state-of-the-art single-system cloud configuration optimizers while spending 2× fewer benchmarking runs. This leads to an overall search cost that is 1.3-24× lower compared to the state-of-the-art. Additionally, when transfer learning can be used, Vanir can minimize the benchmarking runs even further, and use online optimization to achieve a performance comparable to the deployments found by today's single-system frameworks. * Work done in part while author was interning at KAUST.

Sound innovation capabilities help the nations not only to capture bigger market shares but also to sustain long-term economic growth. Innovation is of vital importance at all stages of a country’s development as it promotes productivity, value creation, employment, economic growth, and sustainability. Several factors can affect the innovation activities of a country. For example, peaceful and stable environment, effective macroeconomic designs, sound institutional quality, and efficient utilization of resources are of great significance for a country to nourish economic, business, and market activities. Applying the Auto Regressive Distributive Lag approach to cointegration, this study investigates the short- and long-run impacts of aid, political instability, and terrorism upon the innovation of a laggard economy, namely, Pakistan. Our findings reveal that aid, political instability, and terrorism all have adverse impacts on innovation. Results across robustness checks remain the same. This study is of strong policy implications for policymakers, governments and opposition parties, and security and intelligence agencies to develop sound macroeconomic designs and policies, bring harmony for political stability, and curb terrorism, respectively.

No abstract