Phylogenies based on four loci confirmed the relatedness of all nine validly published species type strains within the Pseudomonas syringae species complex. To further establish the phylogenetic structure within the complex, all 67 pathovar type strains (with defined host ranges) were sequenced using a 578-nucleotide rpoD locus. Since this locus encompassed that used in a previous seven-locus study, it was possible to relate these strains to the existing phylogroup, genomospecies and binomial classifications. All species type strains were distinguished by relatively long branch lengths with all four loci, except for P. savastanoi, P. ficuserectae, P. meliae, P. amygdali and P. tremae, which were attributed to phylogroup 3. The grouping of P. tremae with these genomospecies-2 species was surprising since this species was previously designated as the sole representative of genomospecies 5. The oat pathogen P. syringae pv. coronafaciens was also distinguished by relatively long branch lengths with all four loci. The rpoD phylogeny grouped all the pathovar type strains into major clades that corresponded to previously defined phylogroups, except for two genomospecies-7 strains and P. caricapapayae, which were identified as a new phylogroup (6). There was good correlation between phylogroup and genomospecies classifications, except that two genomospecies-8 strains (P. avellanae and P. syringae pv. theae) were found as a distinct clade within phylogroup 1 along with P. syringae pvs morsprunorum and actinidiae. The rpoD locus will provide a common reference framework to improve monitoring and surveillance of these important pathogens.
We introduce cloud micro-elasticity, a new model for cloud Virtual Machine (VM) allocation and management. Current cloud users over-provision long-lived VMs with large memory footprints to better absorb load spikes, and to conserve performance-sensitive caches. Instead, we achieve elasticity by swiftly cloning VMs into many transient, short-lived, fractional workers to multiplex physical resources at a much finer granularity. The memory of a micro-elastic clone is a logical replica of the parent VM state, including caches, yet its footprint is proportional to the workload, and often a fraction of the nominal maximum. We enable micro-elasticity through a novel technique dubbed VM state coloring, which classifies VM memory into sets of semantically-related regions, and optimizes the propagation, allocation and deduplication of these regions. Using coloring, we build Kaleidoscope and empirically demonstrate its ability to create micro-elastic cloned servers. We model the impact of microelasticity on a demand dataset from AT&T's cloud, and show that fine-grained multiplexing yields infrastructure reductions of 30% relative to state-of-the art techniques for managing elastic clouds.
A basic building block of cloud computing is virtualization. Virtual machines (VMs) encapsulate a user's computing environment and efficiently isolate it from that of other users. VMs, however, are large entities, and no clear APIs exist yet to provide users with programatic, fine-grained control on short time scales.We present SnowFlock, a paradigm and system for cloud computing that introduces VM cloning as a first-class cloud abstraction. VM cloning exploits the well-understood and effective semantics of UNIX fork. We demonstrate multiple usage models of VM cloning: users can incorporate the primitive in their code, can wrap around existing toolchains via scripting, can encapsulate the API within a parallel programming framework, or can use it to load-balance and self-scale clustered servers.VM cloning needs to be efficient to be usable. It must efficiently transmit VM state in order to avoid cloud I/O bottlenecks. We demonstrate how the semantics of cloning aid us in realizing its efficiency: state is propagated in parallel to multiple VM clones, and is transmitted during runtime, allowing for optimizations that substantially reduce the I/O load. We show detailed microbenchmark results highlighting the efficiency of our optimizations, and macrobenchmark numbers demonstrating the effectiveness of the different usage models of SnowFlock.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.