Solano Labs, Inc.

Jay Moorthi

William Josephson

Christopher A Thorpe

Steven R Willis

Thomas E Westberg

A distributed data storage system is provided for offering shared data to one or more clients. In various embodiments, client systems operate on shared data while having a unique writeable copy of the shared data. According to one embodiment, the data storage system can be optimized for various use cases (e.g., read-mostly where writes to shared data are rare or infrequent (although writes to private data may be frequent. Some implementations of the storage system are configured to provide fault tolerance and scalability for the shared storage. For example, read-only data can be stored in (relatively) high latency, low cost, reliable storage (e.g. cloud), with multiple layers of cache supporting faster retrieval. In addition, some implementations of the data storage system offer a low-latency approach to data caching. Other embodiments improve efficiency with access modeling and conditional execution cache hints that can be distributed across the data storage system.

Systems and methods for efficient distribution of stored data objects

Provided is a system for building and validating an application (including e.g., various software versions and revisions, programming languages, code segments, among other examples) without any scripting required by a system user. In one embodiment, an SDLC system is configured to construct a build and test environment, by automatically analyzing a submitted project. The build environment is configured to assemble existing user code, for example, to generate an application to test. Code building can include any one or more of code compilation, assembly, and code interpretation. The system can include a user interface provided to clients, users, and/or customer environments to facilitate user interaction and control of build and test validation. The system can accept user specification of configurations that controls the way the system runs the user's tests. The system can also provide flexible billing models for different customers.

System for distributed software quality improvement

Systems and methods for branched development environments enable construction of development and deployment environments for use at various stages of software development and delivery. Various embodiments are configured to deliver these constructed environments to reproduce faithfully the state of an application at a given revision of the application's source or configuration. The branched development system can be configured to use the constructed environments to offer a variety of novel results and benefits that simplify the development, test, deployment, and debugging tasks commonly associated with software throughout its lifecycle.

Systems and methods for managing software development environments

Systems and methods are provided for managing and/or executing cloud compute instances that may be pre-empted by their providers, known in at least one provider as 'spot instances'. Various aspects manage early termination (i.e., before customer deallocation or compute task completion) of spot instances, enabling improved utilization, and increasing reliability of executing tasks on spot instances. In some embodiments, systems and/or methods are provided for prediction of when spot instance kills are likely to occur. These systems and/or methods can gracefully handle spot kills issued by Providers (e.g., trigger preservation operations), trigger transitions to spot instances at another Provider, trigger increased bidding to preserve current spot instances, and/or trigger a premium payment to allow for execution of preservation operations, among other options. Other embodiments enable customers to directly manage instance kill operations (e.g., a customer or a customer's program selects instances to terminate via a UI or API, etc.).

Cost optimization of cloud computing resources

Provided are systems and methods for simplifying cloud compute markets. A compute marketplace can be configured to determine, automatically, attributes and/or constraints associated with a job without requiring the consumer to provide them. The compute marketplace provides a clearing house for excess compute resources which can be offered privately or publically. The compute environment can be further configured to optimize job completion across multiple providers with different execution formats, and can also factor operating expense of the compute environment into the optimization. The compute marketplace can also be configured to monitor jobs and/or individual job partitions while their execution is in progress. The compute marketplace can be configured to dynamically redistribute jobs/job partitions across providers when, for example, cycle pricing changes during execution, providers fail to meet defined constraints, excess capacity becomes available, compute capacity becomes unavailable, among other options.

Growing community of inventors

San Francisco, CA, United States of America

Jay Moorthi