RAMCloud is a storage system that provides low-latency access to large-scale datasets. To achieve low latency, RAMCloud stores all data in DRAM at all times. To support large capacities (1PB or more), it aggregates the memories of thousands of servers into a single coherent key-value store. RAMCloud ensures the durability of DRAM-based data by keeping backup copies on secondary storage. It uses a uniform log-structured mechanism to manage both DRAM and secondary storage, which results in high performance and efficient memory usage. RAMCloud uses a polling-based approach to communication, bypassing the kernel to communicate directly with NICs; with this approach, client applications can read small objects from any RAMCloud storage server in less than 5μs, durable writes of small objects take about 13.5μs. RAMCloud does not keep multiple copies of data online; instead, it provides high availability by recovering from crashes very quickly (1 to 2 seconds). RAMCloud’s crash recovery mechanism harnesses the resources of the entire cluster working concurrently so that recovery performance scales with cluster size.

We argue that controlling energy allocation is an increasingly useful and important feature for operating systems, especially on mobile devices. We present two new low-level abstractions in the Cinder operating system, reserves and taps, which store and distribute energy for application use. We identify three key properties of control -- isolation, delegation, and subdivision -- and show how using these abstractions can achieve them. We also show how the architecture of the HiStar information-flow control kernel lends itself well to energy control. We prototype and evaluate Cinder on a popular smartphone, the Android G1.

RAMCloud is a DRAM-based storage system that provides inexpensive durability and availability by recovering quickly after crashes, rather than storing replicas in DRAM. RAMCloud scatters backup data across hundreds or thousands of disks, and it harnesses hundreds of servers in parallel to reconstruct lost data. The system uses a log-structured approach for all its data, in DRAM as well as on disk: this provides high performance both during normal operation and during recovery. RAMCloud employs randomized techniques to manage the system in a scalable and decentralized fashion. In a 60-node cluster, RAMCloud recovers 35 GB of data from a failed server in 1.6 seconds. Our measurements suggest that the approach will scale to recover larger memory sizes (64 GB or more) in less time with larger clusters.

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.

MOTIVATION: High-throughput sequencing (HTS) technologies are transforming the study of genomic variation. The various HTS technologies have different sequencing biases and error rates, and while most HTS technologies sequence the residues of the genome directly, generating base calls for each position, the Applied Biosystem's SOLiD platform generates dibase-coded (color space) sequences. While combining data from the various platforms should increase the accuracy of variation detection, to date there are only a few tools that can identify variants from color space data, and none that can analyze color space and regular (letter space) data together. RESULTS: We present VARiD--a probabilistic method for variation detection from both letter- and color-space reads simultaneously. VARiD is based on a hidden Markov model and uses the forward-backward algorithm to accurately identify heterozygous, homozygous and tri-allelic SNPs, as well as micro-indels. Our analysis shows that VARiD performs better than the AB SOLiD toolset at detecting variants from color-space data alone, and improves the calls dramatically when letter- and color-space reads are combined. AVAILABILITY: The toolset is freely available at http://compbio.cs.utoronto.ca/varid.

Virtual Machine (VM) fork is a new cloud computing abstraction that instantaneously clones a VM into multiple replicas running on different hosts. All replicas share the same initial state, matching the intuitive semantics of stateful worker creation. VM fork thus enables the straightforward creation and efficient deployment of many tasks demanding swift instantiation of stateful workers in a cloud environment, e.g. excess load handling, opportunistic job placement, or parallel computing. Lack of instantaneous stateful cloning forces users of cloud computing into ad hoc practices to manage application state and cycle provisioning. We present SnowFlock, our implementation of the VM fork abstraction. To evaluate SnowFlock, we focus on the demanding scenario of services requiring on-the-fly creation of hundreds of parallel workers in order to solve computationally-intensive queries in seconds. These services are prominent in fields such as bioinformatics, finance, and rendering. SnowFlock provides sub-second VM cloning, scales to hundreds of workers, consumes few cloud I/O resources, and has negligible runtime overhead.

The development of Next Generation Sequencing technologies, capable of sequencing hundreds of millions of short reads (25-70 bp each) in a single run, is opening the door to population genomic studies of non-model species. In this paper we present SHRiMP - the SHort Read Mapping Package: a set of algorithms and methods to map short reads to a genome, even in the presence of a large amount of polymorphism. Our method is based upon a fast read mapping technique, separate thorough alignment methods for regular letter-space as well as AB SOLiD (color-space) reads, and a statistical model for false positive hits. We use SHRiMP to map reads from a newly sequenced Ciona savignyi individual to the reference genome. We demonstrate that SHRiMP can accurately map reads to this highly polymorphic genome, while confirming high heterozygosity of C. savignyi in this second individual. SHRiMP is freely available at http://compbio.cs.toronto.edu/shrimp.