Playing online games should be fun. One of the primary causes of player frustration in online games is lag, or delay in exchange of game state data [1]- [8]. Current lag mitigation strategies are based on the assumption that a player's Quality of Experience (QoE) is influenced only by her own lag [9]- [12]. We systematically show that this assumption is incorrect, because in an online cooperative game the change in QoE of one player due to their lag can have a cascading effect on the QoE of the other players. Our results are obtained through a novel experimental framework based on previous QoE and online game research.Understanding a player's QoE as a cascade function that includes other players' network conditions provides valuable information for designing cooperative online games. Based on our observations, we recommend changes to the current approach to lag mitigation in cooperative games. We argue that the primary objective of lag mitigation should not be to reduce the lag of all players. Instead the primary objective should be to reduce the lag of the most lagged player within each cooperative group.
Given a set of datacenters and groups of application clients, well-connected datacenters can be rented as traffic proxies to reduce client latency. Rental costs must be minimized while meeting the application specific latency needs. Here, we formally define the Cooperative Group Provisioning problem and show it is NP-hard to approximate within a constant factor. We introduce a novel greedy approach and demonstrate its promise through extensive simulation using real cloud network topology measurements and realistic client churn. We find that multi-cloud deployments dramatically increase the likelihood of meeting group latency thresholds with minimal cost increase compared to single-cloud deployments.
Today's diagnostic systems can generate a large amount data. Data from sources such as onboard reasoners and historical maintenance data are often stored in heterogeneous systems and cannot be collected immediately and aggregated for use. In our previous work we described a software visualization tool that allowed integration of different data sources and displayed the data with elements organized according to maintenance-oriented ontologies. This tool allows users to search quickly through available data to locate interesting relationships in the sequences of maintenance events. Additional previous work described a diagnostic maturation tool, called ModelMat, that updates causal relationships in a Timed Failure Propagation Graph based on historical diagnostic session data. In this paper, we present an update to both of these projects discussing enhancements to each as well as work in progress to create a single, integrated toolset, called Bobcat, to support ontologyguided diagnostic knowledge discovery.
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