APEX: Adaptive Ext4 File System for Enhanced Data Recoverability in Edge Devices Recently Edge Computing paradigm has gained significant popularity both in industry and academia. With its increased usage in reallife scenarios, security, privacy and integrity of data in such environments have become critical. Malicious deletion of missioncritical data due to ransomware, trojans and viruses has been a huge menace and recovering such lost data is an active field of research. As most of Edge computing devices have compute and storage limitations, difficult constraints arise in providing an optimal scheme for data protection. These devices mostly use LinuxUnix based operating systems. Hence, this work focuses on extending the Ext4 file system to APEX (Adaptive Ext4): a file system based on novel onthefly learning model that provides an Adaptive Recoverability Aware file allocation platform for efficient postdeletion data recovery and therefore maintaining data integrity. Our recovery model and its lightweight implementation allow significant improvement in recoverability of lost data with lower compute, space, time, and cost overheads compared to other methods. We demonstrate the effectiveness of APEX through a case study of overwriting surveillance videos by CryPy malware on RaspberryPi based Edge deployment and show 678% and 32% higher recovery than Ext4 and current stateoftheart File Systems. We also evaluate the overhead characteristics and experimentally show that they are lower than other related works.

An Efficient and Flexible Metadata Management Layer for Local File Systems The efficiency of metadata processing affects the file system performance significantly. There are two bottlenecks in metadata management in existing local file systems: 1) Path lookup is costly because it causes a lot of disk IOs, which makes metadata operations inefficient. 2) Existing file systems have deep IO stack in metadata management, resulting in additional processing overhead. To solve these two bottlenecks, we decoupled data and metadata management and proposed a metadata management layer for local file systems. First, we separated the metadata based on their locations in the namespace tree and aggregated the metadata into fixedsize metadata buckets (MDBs). This design fully utilizes the metadata locality and improves the efficiency of disk IO in the path lookup. Second, we customized an efficient MDB storage system on the raw storage device. This design simplifies the file system IO stack in the metadata management and allows metadata lookup to be completed with constant time complexity. Finally, this metadata management layer gives users the flexibility to choose metadata storage devices. We implemented a prototype called Otter. Our evaluation demonstrated that Otter outperforms native EXT4, XFS, Btrfs, BetrFS and TableFS in many metadata operations. For instance, Otter has 1.2 times to 9.6 times performance improvement over other tested file systems in file opening.