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Optimizing Hardware for Distributed Database System Prototype : Performance, Networking, and Scalability

Julissa Paramo

Hardware Optimizations
                The implementation of a fully functional global distributed database management system requires optimizations across several areas, with hardware being an important factor. To support such a system, hardware requirements must be considered and optimized to ensure high performance. Key areas of focus include efficient storage solutions, processing capacities, and network infrastructure- all important for a highly available and scalable DDBMS.
                 Hardware optimizations for industry level distributed database systems require careful planning. It is important to consider varying workloads along with potential changes in demands for resources when determining hardware requirements. Outlined below are industry best practices for hardware optimizations, along with the proposed solutions for Gloud Geek’s prototype of the physical distributed database.

Processing Capabilities

  Industry level distributed database systems regularly process large amounts of data, which require sophisticated processing capabilities. Optimizing the processing power of these systems is important for efficient I/O operations, reducing latency, and enhancing read/write speeds (Drljaca, 2021), all of which are important in managing database requests and queries. This optimization is achieved by allocating appropriate CPU resources to each of the database servers, which typically involves the use of multi core processors.

Networks and performance in a DDBMS
            A distributed database management system requires careful network configurations for consistent data transfers across all database instances. The performance of the network can directly influence the amount of data transferred within the network, as well as query transmission times (Hababeh, 2021). A case study demonstrates that the use of clustering within a DDBMS reduces network delays in seconds compared to a network configured with a single centralized server. This finding is important to consider for our final solutions which are outlined below.

Proposed Solutions
            For Gloud Geeks’ proposed solution, a prototype of a physical distributed database system has been implemented as a homogenous system using enterprise-class Linux server virtual machines, demonstrating a functional distributed database. With homogenous distributed database systems, identical software and hardware are utilized across all database instances (SNOMED International, 2021). Gloud Geeks has applied this approach in the configurations of three database servers to support business operations across our three major locations: Los Angeles, Tokyo, and Buenos Aires.
            Although through a virtualized approach, the hardware solution ensures sufficient processing capacity by utilizing dual-core processors--a type of multi-core processor--across all instances. This incorporation is important for optimizing the database server performance, as previously discussed. Additionally for Cloud Geeks, this optimization is significant for employee and client data management, as staff will need daily access to update time entry information. These actions are essential for billing and managing contracts with our marketing clients.
            To ensure consistency of data across all nodes, we’ve interconnected the servers through a network, allowing database requests to be replicated with the use of clustering. The clustering of network sites is essential in our final solutions as it ensures effective data communication, reduces communication time, and enhances the overall performance of the database system (Hababeh, 2021). These optimizations improve the availability and reliability of the database management system by decreasing the risk of single site failures (Kumar et al.,2013). Each database server operates with their prospective processing units while maintaining interconnectivity within the network.

References

Drljaca, B. (Dec 27, 2021). Hardware Optimization: Best Practices for Database Performance    Boost. DZone : Data Engineering and Databases. https://dzone.com/articles/hardware-optimization-best-practices-for-database

SNOMED International. (2021). Data Analytics with SNOMED CT: Data Architectures. International Health Terminology Standards Development Organisation, 46-51.

Hababeh, I. (2012). Improving network systems performance by clustering distributed database sites. The Journal of Supercomputing, 59(1), 249-267.

Kumar, N., Bilgaiyan, S., & Sagnika, S. (2013). An overview of transparency in homogeneous distributed database system. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2(10).