Distributed Collaborative Monitoring in Software Defined Networks

We propose a Distributed and Collaborative Monitoring system, DCM, with the following properties. First, DCM allow switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses a novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches

[Al(H2O)6][Cr(OH)6Mo6O18]·10H2O

The title compound, [Al(H2O)6][Cr(OH)6Mo6O18]·10H2O, hexa­aqua­aluminium hexa­hydroxidoocta­deca­oxido­molybdo­chromate(III) deca­hydrate, crystallizes isotypically with its gallium analogue [Ga(H2O)6][Cr(OH)6Mo6O18].10H2O. In the structure of the title compound, both the [Al(H2O)6]3+ cation and the Anderson-type [Cr(OH)6Mo6O18]3− anion lie on centres of inversion. The anion is composed of seven edge-sharing octa­hedra, six of which are MoO6 octa­hedra that are arranged hexa­gonally around the central Cr(OH)6 octa­hedron. The anions are linked to each other by O—H⋯O hydrogen bonds into infinite chains along [100]. These chains are further connected with the [Al(H2O)6]3+ cations through O—H⋯O hydrogen bonds into sheets parallel to (01). O—H⋯O hydrogen bonds involving all the lattice water mol­ecules finally link the sheets into a three-dimensional network

Off-line Deduplication Method for Solid-State Disk Based on Hot and Cold Data

Solid-state disk (SSD) deduplication refers to the identification and deletion of duplicate data stored in an SSD. The reliability of SSDs is improved by deduplication. At present, the common data deduplication of SSDs is based on online data deduplication with Field Programmable Gate Array (FPGA) acceleration. The disadvantage is that FPGA, which has a complex structure. An off-line deduplication method for the SSD based on hot and cold data was proposed in this study to simplify the structure of an SSD deduplication, reduce the cost, and improve the efficiency of deduplication and access performance of SSDs. First, the wear-leveling algorithm was employed in the SSD to divide the data into cold and hot. Then, the corresponding fingerprint was generated for the cold data. Second, the fingerprint was compared, and the cold data with the same fingerprint were deleted. Finally, the cold and hot data were exchanged after deduplication. Results demonstrate that the duplicate recognition rate of the proposed method is 5% - 38%, which is close to that of the online deduplication method. In terms of access performance, the performance of SSDs using the proposed method is improved by 20% compared with that of traditional SSDs and is near the access performance of SSDs using online deduplication. This study provides certain reference for improving the reliability of existing SSDs