Big data analytics have become widespread as a means to extract knowledge from large datasets. Yet, the heterogeneity and irregularity usually associated with big data applications often overwhelm the existing software and hardware infrastructures. In such context, the flexibility and elasticity provided by the cloud computing paradigm offer a natural approach to cost-effectively adapting the allocated resources to the application's current needs. However, these same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a key step to proper management and planning. This paper explores three modeling approaches for performance prediction of cloud-based big data applications. We evaluate two queuing-based analytical models and a novel fast ad hoc simulator in various scenarios based on different applications and infrastructure setups. The three approaches are compared in terms of prediction accuracy, finding that our best approaches can predict average application execution times with 26% relative error in the very worst case and about 7% on average.
Nifurtimox (Nfx) and benznidazole (Bz) are the current drugs used for the treatment of Chagas disease. The mechanisms of action and resistance to these drugs in this parasite are poorly known. Prostaglandin F2α synthase or old yellow enzyme (OYE), an NAD(P)H flavin oxidoreductase, has been involved in the activation pathway of other trypanocidal drugs such as Nfx; however, its role in the mechanism of action of Bz is uncertain. In this paper, we performed some experiments of functional genomics in the parasite Trypanosoma cruzi with the aim to test the role of this gene in the resistance to Bz. For this, we overexpressed this gene in sensitive parasites and evaluated the resistance level to the drug and other chemical compounds such as hydrogen peroxide, methyl methanesulfonate and gamma radiation. Interestingly, parasites overexpressing OYE showed alteration of enzymes associated with oxidative stress protection such as superoxide dismutase A and trypanothione reductase. Furthermore, transfected parasites were more sensitive to drugs, genetic damage and oxidative stress. Additionally, transfected parasites were less infective than wild-type parasites and they showed higher alteration in mitochondrial membrane potential and cell cycle after treatment with Bz. These results supply essential information to help further the understanding of the mechanism of action of Bz in T. cruzi.
The lack of cooperation in Peer-to-Peer (P2P) applications poses serious challenges to the quality of service provided to their clients, specifically in P2P live streaming applications given their strict real-time constraints. We here investigate the potential of exploiting topological properties of the P2P overlay network to predict the level of cooperation of a peer, measured by the ratio of the upload to the download traffic during a pre-defined time window. Using data collected from SopCast, we first show that centrality metrics provide good evidence of a peer's cooperation level in the system. We then develop a regression-based model that is able to estimate, with reasonable accuracy, the level of cooperation of a peer in the near future given its centrality measures in the recent past. Our proposed strategy complements existing incentive mechanisms for cooperation in P2P live streaming, and can be applied to detect non-cooperative peers.
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