The advancements in the field of Information Communication Technology have engendered revolutionary changes in the journalism industry, not only on the part of the journalists and the media personnel, but also on the people consuming these news stories, who today, are only a click away from all the updates they need. However, these advances have also exposed the prevailing venality, wearying off the trust of the public in news media. How then, does an individual discern that which, out of the countless news stories for an incident, should be trusted? This work introduces a system that presents the user a multidimensional analysis for trust in news from various media sources based on the textual content of the articles, assessment of the journalists' perspectives and the temporal diversity of the issues being covered by the media houses publishing the news articles. Our experiments on a self-collected dataset confirm that the system aids in a comprehensive analysis of trust.

Configuration fault localization is the process of identifying fault in the configuration of component(s) that is the source of failure given a set of observed failure conditions. Configuration faults are harder to detect than on/off failures as it involves analysis of the parameters that constitute the configuration. While distributed systems become more complex and interconnected, the requirements on configuration fault localization have changed. In this paper we present a new, simple but effective approach to configuration fault localization, which utilizes the difference in configuration parameters of components that share a resource. We establish a Reference Configuration State (RCS) by determining a set of non-faulty probing components for each faulty component with respect to shared resources. Performing difference in configuration of reference state with that of the faulty components localizes faulty configuration parameter. Experiments through simulations demonstrate that our approach is effective in identifying configuration faults with reduced time and increased accuracy. Our algorithm gracefully handles the complexity of the problem as the system size grows.

Most enterprises today run their applications on virtual machines (VMs). VM mobility - both live and offline, can provide enormous flexibility and also bring down OPEX (Operational Expenditure) costs. However, both live and offline migration of VMs is still limited to within a local network because of the complexities associated with cross subnet live and offline migration. These complexities mainly arise from the hierarchical addressing used by various layer 3 routing protocols. For cross data center VM mobility, virtualization vendors require that the network configuration of the new data center where a VM migrates must be similar to that of the old data center. This severely restricts wide spread use of VM migration across data center networks. For offline migration, the above limitations can be overcome by reconfiguring IP addresses for the migrated VMs. However, even this effort is non-trivial and time consuming as these IP addresses are embedded in various configuration files inside these VMs. As enterprises grow and new data centers emerge in different geographic locations, there is a need to interconnect these data centers in a way that allows seamless VM mobility. In this context, we present CrossRoads - a network fabric that provides layer agnostic and seamless live and offline VM mobility across multiple data centers. We leverage software defined networking and implement an OpenFlow based prototype of CrossRoads. CrossRoads extends the idea of location independence based on pseudo addresses proposed in recent research to work with a control plane overlay of OpenFlow network controllers in various data centers. We evaluate CrossRoads on an innovative testbed that leverages nested virtualization to emulate two data centers. Our results confirm that CrossRoads has negligible performance overhead as compared to a Default layer 2 network - its average performance was no worse than 2.3% as compared to Default fabric across all experiments. In some experiments, it even outperformed the Default by up to 30%.

Fault Localization is the process to identify the component(s) that is the exact source of failure given a set of observed failure indications. Despite being a focus of research for a long time, fault localization is still deemed to be a challenge due to the complexity of current distributed environment. Growing adoption of cloud computing wherein multiple applications share multiple resources increases the complexity of the problem. Existing probing techniques are found to be inefficient due to large number of applications and resources. Availability and utilization of such shared resource environment trigger the need for finding other novel techniques to fault localization. In this paper, we present an elimination-based fault localization method that leverages shared resources among applications. Shared resources are used as `Readily Available Probes' to find the real-time state of applications. These probes are used to eliminate non-faulty resources leaving minimal subset of resources that are likely to the faulty components. We show this method significantly reduces the effort required to design and implement probes. Various experiments demonstrate that our method reduces time taken and increases efficiency and accuracy of problem determination.