The flash storage is a type of nonvolatile semiconductor device that is operated continuously and has been substituting the hard disk or secondary memory in several storage markets, such as PC/laptop computers, mobile devices, and is also used as an enterprise server. Moreover, it offers a number of benefits, including compact size, low power consumption, quick access, easy mobility, heat dissipation, shock tolerance, data preservation during a power outage, and random access. Different embedded system products, including digital cameras, smartphones, personal digital assistants (PDA), along with sensor devices, are currently integrating flash memory. However, as flash memory requires unique capabilities such as “erase before write” as well as “wear-leveling”, a FTL (flash translation layer) is added to the software layer. The FTL software module overcomes the problem of performance that arises from the erase before write operation and wear-leveling, i.e., flash memory does not allow for an in-place update, and therefore a block must be erased prior to overwriting upon the present data. In the meantime, flash storage devices face challenges of failure and thus they must be able to recover metadata (as well as address mapping information), including data after a crash. The FTL layer is responsible for and intended for use in crash recovery. Although the power-off recovery technique is essential for portable devices, most FTL algorithms do not take this into account. Firstly, we review various schemes of crash recovery leveraging FTL for flash storage devices. We illustrate the classification of the FTL algorithms. Moreover, we also discuss the various metrics and parameters evaluated for comparison with other approaches by each scheme, along with the flash type. In addition, we made an analysis of the FTL schemes. We also describe meaningful considerations which play a critical role in the design development for power-off recovery employing FTL. Secondly, we propose an effective scheme for the recovery of flash memory leveraging the shadow paging concept for storage devices using flash memory. To combat the sudden power off problem, the suggested RSLSP approach saves and keeps the map block data as a combination of two tables, i.e., first is the original block and the second block is a replica for the original one. Our proposed strategy not only improves the capacity of a flash memory device as compared to the state- of-the-art schemes suggested in the literature, but is also compatible with the existing FTL-based schemes. Thirdly, When considering which flash memory technology is to be used in conjunction with ternary content addressable memory (TCAM), we need to balance several factors to ensure optimal performance, speed, endurance, reliability, integration complexity, and cost-effectiveness. Hence, it leads to a multi-criteria decision-making problem regarding the selection of other memory technologies such as 3D XPoint, Magnetoresistive RAM, Resistive RAM and Ferroelectric RAM. In this paper, we use the analytical network process (ANP) method to select the suitable technology in conjunction with TCAM considering the features of the memory technologies for SD-IoT. We provide a comprehensive numerical model leveraging ANP to rank the memory technologies regarding their weights. The highest weights identify the most suitable technology for TCAM. We perform simulations to show the effectiveness of the mathematical model utilizing ANP. The results show that suggested methodology reduces the recovery delay, improve the packets received ratio, decrease the jitter and increase the throughput. Keywords: Storage Management, Software-defined Module, power failure, FTL, Recovery, NAND, Memory management, ANP, SDN, TCAM
