The effect of ligand binding on the conformational transitions of the add A-riboswitch in cellular environments is investigated theoretically within the framework of the generalized Langevin equation combined with steered molecular dynamics simulations. Results for the transition path time distribution provide an estimate of the transit times, which are difficult to determine experimentally. The time for the conformational transitions of the riboswitch aptamer is longer for the ligand bound state as compared to that of the unbound one. The transition path time of the riboswitch follows a counterintuitive trend as it decreases with an increase in the barrier height. The mean transition path time of either transitions of the riboswitch in the ligand bound/unbound state increases with an increase in the complexity of the surrounding environment due to the caging effect. The results of the probability density function, transition path time distribution, and mean transition path time obtained from the theory qualitatively agree with those obtained from the simulations and with earlier experimental and theoretical studies.
Modern cloud and data center platforms suffer failures and performance degradation from large traffic surges caused by both external (e.g., DDoS attacks) or internal (e.g., workload changes, operator errors, routing misconfigurations) factors. If not mitigated, traffic overload could have significant financial and availability implications for cloud providers. In this paper, we propose NetFuse, a mechanism to protect against traffic overload in OpenFlow-based data center networks. NetFuse is (1) scalable because it uses passively-collected OpenFlow control messages to detect active network flows; (2) accurate because it uses multi-dimensional flow aggregation to determine the right criteria to combine network flows that lead to overloading behavior; and (3) effective in limiting the damage of surges while not affecting the normal traffic because it uses a toxin-antitoxinlike mechanism to adaptively shape the rate of the flow based on application feedback. Experimental results on a real OpenFlow testbed show that NetFuse is effective in identifying and isolating misbehaving traffic with a small false positive rate (< 9%).
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