
JS7 JobScheduler, an Open Source Workload Automation System, is designed for performance and resilience. JS7 implements state-of-the-art security standards. It offers unlimited performance for parallel executions of jobs and workflows.
JS7 provides cross-platform job execution and managed file transfer. It supports complex dependencies without the need for coding. The JS7 REST-API allows automation of inventory management and job control.
JS7 can operate thousands of Agents across any platform in parallel.
Platforms
- Cloud scheduling for Docker®, OpenShift®, Kubernetes® etc.
- True multi-platform scheduling on premises, for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid cloud and on-premises use
User Interface
- Modern GUI with no-code approach for inventory management, monitoring, and control using web browsers
- Near-real-time information provides immediate visibility to status changes, log outputs of jobs and workflows.
- Multi-client functionality, role-based access management
- OIDC authentication and LDAP integration
High Availability
- Redundancy & Resilience based on asynchronous design and autonomous Agents
- Clustering of all JS7 Products, automatic fail-over and manual switch-over
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Dragonfly serves as a seamless substitute for Redis, offering enhanced performance while reducing costs. It is specifically engineered to harness the capabilities of contemporary cloud infrastructure, catering to the data requirements of today’s applications, thereby liberating developers from the constraints posed by conventional in-memory data solutions. Legacy software cannot fully exploit the advantages of modern cloud technology. With its optimization for cloud environments, Dragonfly achieves an impressive 25 times more throughput and reduces snapshotting latency by 12 times compared to older in-memory data solutions like Redis, making it easier to provide the immediate responses that users demand. The traditional single-threaded architecture of Redis leads to high expenses when scaling workloads. In contrast, Dragonfly is significantly more efficient in both computation and memory usage, potentially reducing infrastructure expenses by up to 80%. Initially, Dragonfly scales vertically, only transitioning to clustering when absolutely necessary at a very high scale, which simplifies the operational framework and enhances system reliability. Consequently, developers can focus more on innovation rather than infrastructure management.
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CoinLion
Founded in August 2017, CoinLion, LLC emerged from the demand for a more effective method of trading and overseeing new cryptocurrencies. CoinLion serves as a platform for trading and managing portfolios, enabling users to effortlessly handle their cryptocurrency transactions. With the cryptocurrency landscape featuring over 1,500 options and new ones being introduced regularly, CoinLion stands out as the inaugural trading platform that incorporates portfolio management, social features, and token rewards all in one place. Its robust tools simplify the processes of buying, selling, and managing various crypto assets. Users can also learn and collaborate with fellow members through the CoinLion Research Portal, which emphasizes support for a diverse array of both crypto and fiat currencies. The platform is dedicated to the continuous addition of new cryptocurrencies to its offerings. Furthermore, CoinLion utilizes an ERC20 token known as the CoinLion Token, which is built on the Ethereum Blockchain, serving as a utility token specifically designed for use within the CoinLion trading platform and research portal, enhancing user engagement and functionality. This commitment to innovation and user experience sets CoinLion apart in the rapidly evolving cryptocurrency market.
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NetApp MetroCluster
NetApp MetroCluster setups consist of two geographically distinct, mirrored ONTAP clusters that function together to ensure ongoing data availability and SVM safeguarding. Each cluster continuously replicates its data aggregates to its counterpart, ensuring that both locations maintain identical copies of the data. In case one of the sites experiences a failure, administrators can quickly activate the mirrored SVM on the operational cluster, allowing for uninterrupted data service. The MetroCluster system accommodates both fabric-attached (FC) and IP-based cluster configurations: the fabric-attached MetroCluster utilizes FC transport for SyncMirror synchronization between sites, while MetroCluster IP operates over layer-2 stretched IP networks. Deployments of Stretch MetroCluster facilitate coverage across an entire campus, and with ONTAP versions 9.12.1 and 9.15.1, MetroCluster IP configurations can support up to four nodes using NVMe/FC or NVMe/TCP. Furthermore, it is important to note that front-end SAN protocols such as FC, FCoE, and iSCSI are fully supported within this architecture, enhancing the overall versatility of MetroCluster solutions. This flexible design accommodates various enterprise needs, making it an attractive option for organizations looking to optimize their data management strategies.
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