Techfite Firm’s Problem and Emerging Technology Solution

Before determining the potential solutions for TechFite’s current situation, it is essential to outline the organizational need and its scope. Considering that the most prominent concerns are related to the lack of available log storage space and efficient protection techniques, the organizational need to be resolved is the improvement of data management strategies. TechFite reports an extremely low success rate regarding the identification of intrusion signatures and experiences a consistent shortage of storage space, meaning that more productive methods of digital information handling are required. Therefore, the scope of the described need includes the identification of relevant strategies that could enhance the security performance of TechFite’s firewall and provide the corporation with extra storage space for the necessary information.

In the area of storage management, a prominent emerging technology solution is the scale-out architecture. This method utilizes a system of network-connected storage devices, the resources of which are used to collect digital information and protect it from potential attacks (Oh et al., 2016). A distinct characteristic of the proposed strategy is its accessibility, as additional storage devices may be included in the network to increase the current storage capacity.

Scale-out or composable architectures are a significant part of the data fabric, identified by Gartner’s hype cycle as a prominent emerging technology that could reach its plateau in the period from 2026 to 2031 (Gartner, 2021). Currently leaving the peak of inflated expectations, data fabric and the capability to create flexible scale-out architectures are considered highly prominent aspects of organizational growth in the future.

To successfully implement the scale-out storage technology, it would be necessary to follow a step-by-step approach to infrastructure integration. First of all, scale-out storage is based on server clustering, where multiple servers work simultaneously using one system (Liang et al., 2020). Therefore, the first step involves establishing the needed servers, clustering them, and linking the resulting nodes into the working system via the local area network (LAN) (Hanifa, 2017). This stage is essential for the future development of the architecture, which relies on the connection between the individual clusters and the main system.

After that, software-defined networking will be required to control the established nodes (Hanifa, 2017). Currently, a large number of applications are available for installment, allowing the TechFite executives to manage the scale-out integration process and expand or reduce the existing network.

The most valuable impact of the scale-out technology is its scalability. As this data storage method allows for the connection of multiple nodes via LAN, the necessary clusters can be easily added or deleted from the network using the main system software. This leads to the layering of hardware and software infrastructure, which improves the resilience of the network and protects it from potential attacks (Liang et al., 2020). Given that the information is scattered throughout several modules, extra resources for cybersecurity enhancement become available. For TechFite, this possibility allows to improve the available storage space, including or excluding extra storing capacity, simultaneously achieving higher protection standards.

Nevertheless, there are several limitations to the use of the scale-out technology. For instance, the integration of additional clusters must be thoroughly planned, as extra upgrades and system proficiency will be required to proceed (Oh et al., 2016). Based on the number of nodes in use, software enhancement might demand a significant amount of time. However, this disadvantage can be overcome by utilizing a selective upgrade method, where each cluster is upgraded independently of the others (Oh et al., 2016). In this regard, it would be imperative for TechFite to plan any intended software or cluster updates, during which some of the systems would be unavailable.

An alternative technology solution could be the scale-up strategy, which is commonly implemented to increase storage capacity. A considerable advantage of this system is its cost-effectiveness and practicability, as only physical hardware is utilized (Liu et al., 2020). Moreover, this method is considered more efficient due to the availability of hardware and the simplicity of use (Liu et al., 2020). Nonetheless, a crucial disadvantage is a need for supplementary physical space, which might be costly for TechFite. Finally, scale-up networks are more vulnerable to hacking attempts and are more challenging to protect without expensive hardware improvements.

The benefits of the scale-out technology include the implementation of cloud services and long-term cost reduction. Cloud storage contributes to the availability of information, which allows TechFite to eliminate the complications related to geographical differences. As the data is being stored via cloud services, it is easily accessible for analysis or retrieval. Furthermore, scale-out systems are more cost-effective in the long term, as less physical space and maintenance are needed (Liu et al., 2020). However, a valuable limitation is the necessity to link numerous devices, which necessitates consistent examination (Oh et al., 2016). After that, this network becomes more strenuous to update as the number of clusters increases, and TechFite would be forced to schedule maintenance appropriately.

To analyze the efficiency of the implemented scale-out storage system, it is possible to use performance tests and system evaluation methods. For instance, to determine whether the necessary storage space has been achieved, TechFite can initiate storage space evaluations that reveal if the log file storing capacity has elevated since the adoption of the emerging technology (Liu et al., 2020). The productivity of the network can be assessed using processing speed examinations, uncovering how quickly the data can be reviewed in comparison with the previous storage method.


Gartner. (2021). 5 trends drive the Gartner Hype Cycle for emerging technologies, 2020. Web.

Hanifa, S. M. (2017). Understanding hindsight, insight and forsight data to large-scale distributed data intelligence (algorithms) machine: A scale-out review. I-Manager’s Journal on Pattern Recognition, 4(3), 32–43. Web.

Liang, Z., Lombardi, J., Chaarawi, M., & Hennecke, M. (2020). DAOS: A scale-out high performance storage stack for storage class memory. In D. K. Panda (Ed.), Supercomputing Frontiers (pp. 40–54). Springer International Publishing.

Liu, J., Curry, M. L., Maltzahn, C., & Kufeldt, P. (2020). Scale-out edge storage systems with embedded storage nodes to get better availability and cost-efficiency at the same time. 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20). Web.

Oh, M., Eom, J., Yoon, J., Yun, J. Y., Kim, S., & Yeom, H. Y. (2016). Performance optimization for all flash scale-out storage. 2016 IEEE International Conference on Cluster Computing (CLUSTER), 316–325. Web.

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