Our Account Service provides secure Identity and Access Management for the YellowDog Platform.
We use best in class secure Keyrings technology for the storage of cloud credentials.
Using military-grade AES-256 encryption within a secure architecture, the user retains full control over sensitive access credentials.
During automated operation, our system creates temporary, privilege and time constrained access to only execute the task at hand.
Full auditing of accounts, identities and actions ensure traceability and accountability.
Our Object Store Service combines multiple distinct storage providers (e.g. Azure Blob, Amazon S3, Google Cloud Storage) across multiple providers and regions into one coherent data surface.
This overcomes many of the data management constraints inherent in hybrid- and multi-cloud deployments.
Our Object Store Service segmentation and distribution of data improves the storage performance beyond that offered by a single Object Store. Object Store buckets may be bandwidth limited; as our Object Store Service distributes the data across multiple buckets, the impact of any bandwidth limits are dispersed. This means that a cloud Object Store can be fast enough to boot an Operating System, or transfer assets at a speed that otherwise would require a high-end data transfer appliance.
Our Object Store Service also verifies effective data transfer, allowing faster and connectionless data transfer protocols to be used.
Our Image Registry Service is a virtual machine image catalogue for all images across all regions and cloud providers.
This ensures that the right version of the image is matched to the right type of computing instance and Operating System in every region, in every cloud. This is imperative when the YellowDog Platform is automatically choosing the Best Source of Compute and when incompatibilities in the underlying instance type and operating system (OS) would mean that applications and workloads underperform or fail.
As a result, the tasks are completed faster and at a lower cost than using other third-party workload managers.
Our Compute Service is a common API to provision, manage and de-provision computing resources across multiple clouds and on-premise infrastructure.
When combined with our Client Software Development Kits (SDKs), our Compute Service makes it quick and easy to orchestrate cloud and on-premise computing resources. Out of the box connectors are available for:
Multiple deployment strategies are supported including managing “fleets” of AWS Spot Instances and GCP preemptive instances.
With our Compute Service, strategies can be deployed to determine and provision the Best Source of Compute for workloads across multiple clouds. This could be the fastest deployment time, the lowest cost, the lowest environmental impact, or delivery to a deadline. It also means that business constraints can be adhered to; constraints such as data sovereignty, security certifications or environmental impact.
For intelligent computing resource management, our Compute Service can combine different strategies within a single Workload Requirement to achieve the ultimate efficiency and performance.
Intelligent placement of workload is made by the service understanding computing:
Our Scheduler Service increases utilisation levels by sharing computing resources using fine grained control and prioritisation of tasks.
Scheduling workloads in a hybrid- and multi-cloud environment is significantly more complex than in a data centre based, single-system scheduler location. To provide robust and consistent performance, the scheduler must handle issues such as varying environment characteristics (e.g. external factors impacting network or vCPU performance), greater asynchronicity of interactions, distributed resource management and ownership, dynamic resource availability, complex network topographies and failures of both computing resources and network connections.
Our Scheduler Service is built from the ground up to handle these problems and results in efficient, reliable and well utilised computing resources.
Our Scheduler Service is also designed to complement any existing schedulers that may be implemented to enhance and improve their performance for hybrid- and multi-cloud deployments.
It is fully integrated with the our Object Store Service so that data is automatically supplied for tasks exactly when it is needed; and data output is captured, stored or provided as a form of data pipeline between tasks. This ensures that any dependencies between tasks and groups of tasks are mapped, tracked and synchronised so that workloads are delivered effectively, regardless of where the processing is taking place.
Our Prediction Service uses advanced machine learning to accurately predict workload run times. These predictions can then be used to intelligently schedule when and where workloads are run, ensuring that any job queuing and resource underutilisation is minimised, and any cloud expenditure fully optimised.
Our Prediction Service provides REST APIs for both model training and query, and supports the inclusion of seasonal data, decay parameters and validation of model fit. The service also includes proactive alerting when workloads exceed their predicted execution times or SLAs.
Our Prediction Service integrates with leading workload schedulers and monitoring tools.
Contact our team today to learn more or request a demo.
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