Derivative works — US Patent 9002795

Defensive Disclosure: Derivative Works of US Patent 9002795

This document outlines several derivative variations of the core inventive concepts disclosed in US Patent 9002795, focusing on independent claim 1. These disclosures aim to establish prior art for future incremental improvements by competitors, rendering such advancements obvious or non-novel to a person having ordinary skill in the art (PHOSITA). The primary objective is to broaden the scope of existing public knowledge surrounding object-based data storage and intelligent attribute allocation.

Derivatives of Claim 1: Method for Allocating Data Attributes from an i-node to Storage Media

The core concept is the intelligent allocation of i-node attributes (specifically read vs. write attributes) to different storage media zones based on their access frequency, controlled by an object-based storage interface within the device.

1. Material & Component Substitution

Derivative 1.1: Multi-Tiered NAND Flash Storage Media

Enabling Description:
Instead of traditional magnetic discs, the storage media comprises a multi-tiered NAND flash memory array. This array integrates different NAND technologies, each acting as a distinct media zone with differing zone attributes. For example, a first zone utilizes Single-Level Cell (SLC) NAND for its high endurance and low latency, designated for frequently accessed i-node read attributes. A second zone employs Triple-Level Cell (TLC) NAND for its higher density and lower cost, suitable for less frequently accessed i-node write attributes. A third zone might use Quad-Level Cell (QLC) or Penta-Level Cell (PLC) NAND for archival or very infrequent write attributes. The object-based storage interface (OSI) within the NAND controller analyzes i-node attribute access frequencies and allocates them to the appropriate SLC, TLC, or QLC zones, leveraging wear-leveling algorithms specific to each NAND type. The data channel connects via a high-speed NVMe interface, and the OSI is implemented as a dedicated Flash Translation Layer (FTL) accelerator in an ASIC.

graph TD
    A[Host System] -->|NVMe Commands/Objects| B(NVMe Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --> |Analyze i-node access frequencies| D1(FTL-managed SLC Zone)
    C --> |Allocate less frequent writes| D2(FTL-managed TLC Zone)
    C --> |Allocate more frequent reads| D3(FTL-managed QLC/PLC Zone)
    D1 --&gt; E[NAND Flash Media Array]
    D2 --&gt; E
    D3 --&gt; E

Derivative 1.2: Phase-Change Memory (PCM) and Resistive RAM (ReRAM) Hybrid Storage

Enabling Description:
The storage device incorporates a hybrid non-volatile memory (NVM) architecture. The "more frequently accessed" i-node read attributes are allocated to a Phase-Change Memory (PCM) zone, characterized by its byte-addressability, high read/write endurance, and low latency. The "less frequently accessed" i-node write attributes, which typically involve larger block updates, are directed to a Resistive RAM (ReRAM) zone, offering higher density and competitive endurance, albeit potentially with slightly higher write latency. The physical separation between these zones is inherent in their distinct manufacturing processes and memory arrays. The object-based storage interface (OSI) is a hardware-accelerated memory controller that manages the distinct access patterns and refresh/wear-leveling requirements of PCM and ReRAM, performing real-time attribute access frequency analysis. The data channel could utilize a CXL (Compute Express Link) interface for low-latency memory-semantic access.

graph TD
    A[Host System] -->|CXL Memory Transactions| B(CXL Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --&gt; |Analyze i-node access frequencies| D1(PCM Memory Array Zone)
    C --&gt; |Allocate more frequent reads| D2(ReRAM Memory Array Zone)
    C --&gt; |Allocate less frequent writes| D3(NVM Controller)
    D1 --&gt; D3
    D2 --&gt; D3

Derivative 1.3: Ferroelectric RAM (FeRAM) with Holographic Storage

Enabling Description:
For specialized applications requiring extremely high write endurance for critical metadata, the "more frequently accessed" i-node read attributes and critical i-node write attributes are stored in a small, ultra-fast Ferroelectric RAM (FeRAM) zone. This FeRAM zone provides non-volatility with DRAM-like speeds and virtually unlimited write endurance, making it ideal for rapidly changing metadata. Concurrently, a "less frequently accessed" zone utilizes holographic storage media, offering massive capacity and high data transfer rates for larger, less frequently accessed i-node write attributes or historical metadata logs. The object-based storage interface (OSI) dynamically manages data migration and access between the FeRAM and holographic media based on determined access frequencies, using advanced optical read/write heads for the holographic component and standard memory interfaces for FeRAM. The data channel could be a specialized photonics-based interconnect for high-bandwidth holographic access.

graph TD
    A[Host System] -->|High-Speed Data Channel| B{Object-Based Storage Interface (OSI)}
    B --&gt; |Analyze i-node access frequencies| C1(FeRAM Zone)
    B --&gt; |Allocate less frequent writes| C2(Holographic Storage Zone)
    C1 --&gt; D[Memory Controller]
    C2 --&gt; D

2. Operational Parameter Expansion

Derivative 2.1: Nanoscale Molecular Storage with Quantum Tunneling Zones

Enabling Description:
The storage media consists of molecular structures arranged in nanoscale zones, where data is encoded by manipulating molecular states. "Physically separate zones" are defined by distinct molecular configurations or densities. "Differing zone attributes" relate to energy barriers for molecular switching, affecting write/read speeds and endurance. The object-based storage interface (OSI) operates at the quantum level, utilizing quantum tunneling or resonant frequency excitation to write and read i-node attributes. "More frequently accessed" i-node read attributes are placed in zones requiring minimal energy for state transitions, offering ultra-low latency. "Less frequently accessed" i-node write attributes are allocated to denser zones with higher energy barriers, ensuring long-term stability. The data channel would be a quantum interconnect, transmitting entangled photons or electrons, and the analysis of i-node attribute access frequencies is performed by a dedicated neuromorphic processor within the storage device, operating at picosecond frequencies.

graph TD
    A[Quantum Host Processor] -->|Quantum Interconnect (Entangled Qubits)| B(Quantum Data Channel)
    B --> C{Nanoscale OSI (Neuromorphic Processor)}
    C --> |Analyze i-node access frequencies (THz)| D1(Low-Energy Barrier Molecular Zone)
    C --> |Allocate more frequent reads| D2(High-Density Molecular Zone)
    C --> |Allocate less frequent writes| E[Nanoscale Molecular Storage Media]
    D1 --&gt; E
    D2 --&gt; E

Derivative 2.2: Cryogenic Distributed Exascale Archival System

Enabling Description:
An exascale storage system designed for scientific archives operates in a cryogenic environment (e.g., liquid helium temperatures, 4K). The storage media comprises multiple geographically and thermally isolated zones, with "differing zone attributes" including thermal stability, quantum coherence times (for quantum memories), and data retention periods. "More frequently accessed" i-node read attributes for active research datasets are maintained in warm, high-performance NVMe SSD zones (still within the cold chain, but warmer than deep archive). "Less frequently accessed" i-node write attributes and long-term archival metadata are placed in superconducting memory or atomic-scale storage zones maintained at ultra-low temperatures, offering extreme data density and stability but with higher latency for initial access and potentially requiring complex quantum error correction. The object-based storage interface (OSI) is a distributed control plane, managing global data placement policies, energy consumption, and thermal budgets across the exascale system, making dynamic reallocations based on access frequency fluctuations, with data transfers occurring at multi-terabit-per-second frequencies over superconducting data channels.

graph TD
    A[Global Data Center] -->|Superconducting Data Channel (Tb/s)| B(Distributed OSD Interface)
    B --> C{Cryogenic Data Manager (OSI)}
    C --> |Allocate warm zone (40-77K) for active reads| D1(NVMe SSD Zone)
    C --> |Allocate ultra-cold zone (4K) for archive writes| D2(Superconducting Memory Zone)
    D1 --&gt; E[Cryogenic Storage Rack]
    D2 --&gt; E
    E --&gt; F(Exascale Archival Media)

Derivative 2.3: High-Pressure, High-Temperature Geological Data Store

Enabling Description:
A specialized data storage device is deployed in extreme geological environments, such as deep boreholes or volcanic monitoring stations, requiring operation under high pressure (e.g., 1000 atmospheres) and high temperature (e.g., 300°C). The storage media is designed with distinct "zones" composed of radiation-hardened, high-temperature silicon carbide (SiC) memory components (e.g., SiC MRAM or FeRAM) and extreme-environment magnetic media (e.g., using specialized alloys). "Differing zone attributes" include thermal resilience, pressure tolerance, and radiation hardness. The object-based storage interface (OSI) is a hardened, embedded controller that analyzes the access frequency of i-node attributes. "More frequently accessed" real-time sensor data i-node read attributes are allocated to the high-temperature FeRAM/MRAM zones for rapid access and frequent updates. "Less frequently accessed" historical geological survey data i-node write attributes are stored in the robust, high-capacity magnetic media zones. The data channel uses high-temperature, pressure-resistant optical fiber interconnects, and the entire system is designed for active cooling under extreme conditions.

graph TD
    A[Geological Sensor Array] -->|Hardened Optical Interconnect| B(High-Temp/Pressure Data Channel)
    B --> C{Ruggedized OSD Interface (SiC Processor)}
    C --> |Analyze i-node access frequencies (high temp/pressure)| D1(High-Temp FeRAM/MRAM Zone)
    C --> |Allocate more frequent reads| D2(Extreme-Environment Magnetic Zone)
    C --> |Allocate less frequent writes| E[High-Temp/Pressure Storage Media]
    D1 --&gt; E
    D2 --&gt; E

3. Cross-Domain Application

Derivative 3.1: Biomedical Imaging and Genomics Data Archiver

Enabling Description:
In a biomedical imaging and genomics data archiving system, the data storage device manages i-node attributes for massive datasets like 3D MRI scans, whole-genome sequences, and pathology slides. The "host system" is a medical diagnostic workstation or a bioinformatics pipeline. "Objects" are patient records, genomic samples, or imaging studies. The storage media has zones optimized for different data retention and access needs: a "high-performance" zone (e.g., SSDs) for diagnostic image metadata requiring immediate access, a "compliance" zone (e.g., WORM optical discs or tape with specific encryption) for long-term patient record integrity, and an "exploratory" zone (e.g., high-capacity HDD arrays) for research-related metadata. The object-based storage interface (OSI) within the archiving appliance analyzes i-node attribute access frequencies (e.g., how often a specific patient's diagnostic metadata is accessed vs. a research cohort's archived genomic data). "More frequently accessed" diagnostic metadata i-node read attributes are placed in the SSD zone for rapid retrieval, while "less frequently accessed" historical genomic data i-node write attributes are allocated to the optical/tape compliance zone, ensuring regulatory adherence and long-term preservation.

graph TD
    A[Medical Workstation/Bioinformatics Pipeline] -->|DICOM/HL7 Objects w/ Metadata| B(Data Channel)
    B --> C{Medical OSD Interface (Archiving Appliance)}
    C --> |Analyze i-node access frequencies (e.g., patient access vs. research)| D1(High-Performance SSD Zone)
    C --> |Allocate more frequent diagnostic reads| D2(WORM Optical/Tape Compliance Zone)
    C --> |Allocate less frequent archival writes| D3(High-Capacity HDD Research Zone)
    D1 --&gt; E[Biomedical Storage Media]
    D2 --&gt; E
    D3 --&gt; E

Derivative 3.2: Autonomous Vehicle Sensor Fusion and Black Box Recorder

Enabling Description:
Within an autonomous vehicle, the storage device acts as a "black box" recorder and sensor data fusion buffer. The "host system" is the vehicle's central compute unit. "Objects" include LiDAR point clouds, camera streams, radar returns, and vehicle state telemetry, each with associated i-node attributes (e.g., timestamp, sensor ID, criticality, processed status). The storage media comprises multiple robust, vibration-resistant zones: a "real-time" zone (e.g., industrial-grade NVMe SSD) for mission-critical, high-frequency sensor fusion metadata; a "diagnostic" zone (e.g., high-endurance eMMC) for event-triggered diagnostic logs and "less frequently accessed" operational parameters; and an "after-market" zone (e.g., removable, secure SSD cartridge) for post-incident analysis metadata. The object-based storage interface (OSI), integrated into the vehicle's domain controller, continuously analyzes i-node attribute access frequencies. "More frequently accessed" real-time perception i-node read attributes (e.g., current frame's object detection metadata) are allocated to the NVMe SSD zone. "Less frequently accessed" pre-collision event logs or firmware update i-node write attributes are directed to the eMMC or removable cartridge zones.

graph TD
    A[Vehicle Central Compute Unit] -->|Sensor Fusion Objects w/ Metadata| B(Vehicle Data Channel)
    B --> C{Automotive OSD Interface (Domain Controller)}
    C --> |Analyze i-node access frequencies (e.g., real-time vs. diagnostic)| D1(Industrial-Grade NVMe SSD Zone)
    C --> |Allocate more frequent perception reads| D2(High-Endurance eMMC Diagnostic Zone)
    C --> |Allocate less frequent log writes| D3(Removable Secure SSD After-market Zone)
    D1 --&gt; E[Autonomous Vehicle Storage Media]
    D2 --&gt; E
    D3 --&gt; E

Derivative 3.3: Smart Grid Predictive Maintenance and Operational Log Store

Enabling Description:
In a smart grid infrastructure, specialized data storage devices are deployed at substations and power generation facilities to manage telemetry, control commands, and predictive maintenance analytics metadata. The "host system" is a local grid controller or SCADA gateway. "Objects" are time-series sensor readings (voltage, current, temperature), fault events, control actions, and predictive model states. The storage media features zones with differing attributes crucial for grid operation: a "critical operations" zone (e.g., hardened, low-latency MRAM) for real-time control metadata and fault logs; a "historical analytics" zone (e.g., high-capacity industrial HDD) for long-term trend analysis and training data metadata; and a "secure audit" zone (e.g., immutable WORM media or blockchain-validated storage) for regulatory compliance. The object-based storage interface (OSI), integrated into the substation's data logger, analyzes i-node attribute access frequencies. "More frequently accessed" real-time control command i-node read attributes are stored in the MRAM zone for instantaneous response. "Less frequently accessed" long-term energy consumption patterns or historical maintenance records i-node write attributes are allocated to the industrial HDD or secure audit zones.

graph TD
    A[SCADA Gateway/Grid Controller] -->|Telemetry/Control Objects w/ Metadata| B(Grid Data Channel)
    B --> C{Grid OSD Interface (Substation Data Logger)}
    C --> |Analyze i-node access frequencies (e.g., real-time control vs. historical)| D1(Hardened MRAM Critical Operations Zone)
    C --> |Allocate more frequent control reads| D2(Industrial HDD Historical Analytics Zone)
    C --> |Allocate less frequent long-term writes| D3(Immutable Secure Audit Zone)
    D1 --&gt; E[Smart Grid Storage Media]
    D2 --&gt; E
    D3 --&gt; E

4. Integration with Emerging Tech

Derivative 4.1: AI-Driven Dynamic Zone Reallocation and Predictive Optimization

Enabling Description:
The object-based storage interface (OSI) integrates an Artificial Intelligence (AI) module, specifically a deep reinforcement learning agent. This AI module continuously monitors I/O patterns, historical access frequencies for i-node attributes, storage media wear, temperature, and performance metrics across all zones (from IoT sensors, see Derivative 4.2). Instead of static rules, the AI agent dynamically re-learns and optimizes the allocation strategy for i-node attributes. It predicts future access frequencies and anticipates changes in performance attributes due to wear or environmental factors. Based on these predictions, the AI module not only allocates i-node attributes (read/write) to current zones but also intelligently initiates background migration of i-node attributes between zones to maintain optimal performance and reliability over time. It can even suggest dynamic re-partitioning or re-definition of zone boundaries based on observed workloads. The "analyzing the data object i-node attributes to determine one or more i-node attribute access frequencies" step is enhanced by predictive analytics, factoring in object age, user patterns, and application types.

graph TD
    A[Host System] -->|Data Objects w/ i-nodes| B(Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --> D[AI Module (Reinforcement Learning Agent)]
    D --> |Predictive Access Freq. & Media Health| E{Allocation Logic (Dynamic Policy)}
    E --> |Allocate/Migrate i-node Attributes| F(Multiple Media Zones)
    F --> |Real-time Performance/Health Data| D
    C --&gt; E
    E --&gt; G[Storage Media]
    F --&gt; G

Derivative 4.2: IoT Sensor-Enhanced Real-time Media Attribute Monitoring

Enabling Description:
Each physically separate media zone within the storage device is equipped with an array of embedded Internet of Things (IoT) sensors. These sensors provide real-time, granular telemetry on critical "zone attributes," including: NAND block wear levels, individual cell retention characteristics (for flash), magnetic head-media interface health, temperature gradients, vibration levels, read/write error rates, and even predictive indicators of impending component failure. This real-time sensor data is continuously streamed to the object-based storage interface (OSI). The OSI's attribute comparison logic (claim 236 in US9002795) is enhanced to incorporate these dynamic, real-time "sensed zone attributes" when making allocation decisions. For example, if a "more frequently accessed" zone shows higher-than-expected wear or increased read errors, the OSI can dynamically re-route incoming "i-node read attributes" to an alternative healthy zone, or trigger background scrub operations. The system constantly re-evaluates which zones "meet or exceed" requested storage attributes based on live data, rather than static, pre-sensed attributes.

graph TD
    A[Host System] -->|Data Objects w/ i-nodes| B(Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --> D[Allocation Logic (Real-time Adaptive)]
    D --> E(Multiple Media Zones)
    E --&gt; F[Storage Media]
    F --&gt; G[IoT Sensor Array (Temp, Wear, Errors)]
    G --> |Real-time Zone Attributes Feedback| D

Derivative 4.3: Blockchain-Verified Metadata Integrity and Provenance Tracking

Enabling Description:
For critical objects, the object-based storage interface (OSI) integrates with a private blockchain network to ensure the immutable logging of key i-node attribute states and allocation decisions. When "i-node write attributes" (e.g., data block pointers, creation timestamp, ownership) are allocated and written to a specific zone, a cryptographic hash of these attributes, along with the zone's identifier and the allocation timestamp, is recorded as a transaction on the blockchain. Subsequent updates to these write attributes (e.g., file growth, ownership change) trigger new blockchain transactions. This creates an auditable, tamper-proof chain of provenance for critical metadata. While the actual i-node attributes reside in their allocated zones, their integrity can be verified by comparing current hashes against the blockchain record. This is particularly valuable for regulatory compliance or intellectual property protection, enhancing the "reliability attributes" mentioned in the patent. The "analyzing the data object i-node attributes" step can also include checking their blockchain provenance.

graph TD
    A[Host System] -->|Data Objects w/ i-nodes| B(Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --> D[Allocation Logic]
    D --> E(Multiple Media Zones)
    E --> F[Storage Media]
    C --> |Cryptographic Hash of i-node Attributes & Allocation Decisions| G(Blockchain Ledger)
    G --&gt; |Integrity Verification| C
    E --&gt; G

5. The "Inverse" or Failure Mode

Derivative 5.1: Fail-Safe Critical Metadata Isolation and Recovery

Enabling Description:
The object-based storage device is engineered with a dedicated "fail-safe" media zone for critical i-node attributes, such as data block pointers, checksums, and essential security metadata. This zone utilizes the most resilient and redundant storage technology available within the device (e.g., mirrored SLC NAND, battery-backed MRAM, or triple-redundant storage on separate platters). In normal operation, "less frequently accessed i-node write attributes" are still allocated based on access frequency, but the most critical subset of these write attributes is always mirrored or prioritized to this fail-safe zone, regardless of its primary access frequency, overriding the typical allocation logic. Upon detection of an impending failure (e.g., via IoT sensors, see Derivative 4.2), the object-based storage interface (OSI) immediately isolates the fail-safe zone and initiates a rapid snapshot and export of its contents to an external recovery medium or redundant device. This ensures that even if the primary storage media is compromised, the essential metadata required to reconstruct or identify the data objects remains intact and recoverable.

stateDiagram
    state Normal_Operation {
        [*] --> Allocate_iNode_Attributes
        Allocate_iNode_Attributes --> Monitor_Media_Health
        Monitor_Media_Health --> Allocate_iNode_Attributes : No Failure Detected
    }
    state Failure_Detected {
        Monitor_Media_Health --> Isolate_FailSafe_Zone
        Isolate_FailSafe_Zone --> Snapshot_Critical_Metadata
        Snapshot_Critical_Metadata --> Export_Recovery_Media
        Export_Recovery_Media --> Recover_Data_Objects : Recovery Initiated
        Isolate_FailSafe_Zone --> Degraded_Mode : Performance reduced
    }

    Allocate_iNode_Attributes : OSI allocates read/write attributes to zones.\nCritical write attributes mirrored to Fail-Safe Zone.
    Monitor_Media_Health : IoT sensors monitor all zones.
    Isolate_FailSafe_Zone : Power down non-critical zones; secure Fail-Safe Zone.
    Snapshot_Critical_Metadata : Copy Fail-Safe Zone contents.
    Export_Recovery_Media : Transmit snapshot to external storage.
    Degraded_Mode : Limited R/W using only resilient zones.
    Recover_Data_Objects : Reconstruct objects using critical metadata.

    note right of Isolate_FailSafe_Zone
        The Fail-Safe Zone has highest redundancy (e.g., MRAM, mirrored SLC)
        and is always prioritized for critical i-node write attributes.
    end note

Derivative 5.2: Adaptive Low-Power / Limited-Functionality Mode

Enabling Description:
The storage device implements an adaptive low-power or limited-functionality mode, triggered by system-level power constraints (e.g., battery mode in mobile devices, grid outage in smart grid) or user-defined policies. In this mode, the object-based storage interface (OSI) dynamically reconfigures its i-node attribute allocation strategy. Instead of optimizing for peak performance or reliability, the allocation prioritizes energy efficiency. "More frequently accessed i-node read attributes" are migrated to or exclusively served from the lowest-power consumption zone (e.g., a small, always-on static RAM buffer or a slow, spin-down HDD zone). "Less frequently accessed i-node write attributes" are coalesced and written in large, infrequent batches to energy-efficient, high-density zones, minimizing active component time. The OSI may temporarily disable certain high-performance features (e.g., advanced caching, background scanning) and only performs essential attribute updates. The "differing zone attributes" now explicitly include power consumption characteristics alongside performance. When power is restored or demand decreases, the system transparently transitions back to full-functionality mode.

sequenceDiagram
    participant H as Host System
    participant O as Object-Based Storage Interface (OSI)
    participant Z as Storage Media Zones
    participant P as Power Management Unit

    H->>O: Data Object w/ i-node (Normal Ops)
    O->>Z: Allocate i-node Attributes (Perf Optimized)
    P->>O: Signal Low-Power Condition (e.g., Battery)
    O->>O: Enter Low-Power Mode
    O->>Z: Reallocate i-node Attributes (Power Optimized)
    O->>Z: Coalesce Less Frequent Writes
    O->>Z: Serve Frequent Reads from Low-Power Zone
    P->>O: Signal Full-Power Restoration
    O->>O: Exit Low-Power Mode
    O->>Z: Reallocate i-node Attributes (Perf Optimized)

Derivative 5.3: Controlled Degradation / Sandboxed Failure Mode for Testing

Enabling Description:
A version of the object-based storage device is developed for testing and validation, featuring a "controlled degradation" mode. In this mode, specific media zones can be programmatically designated as "failure-prone" or "performance-degraded." The object-based storage interface (OSI) is configured to intentionally allocate "less frequently accessed i-node write attributes" or even "more frequently accessed i-node read attributes" to these degraded zones, simulating real-world aging or fault conditions. This allows for rigorous testing of the system's fault tolerance, error correction, and recovery mechanisms in a controlled environment. For instance, a "sandboxed failure zone" (e.g., a specific set of NAND blocks with induced wear or a magnetic track with simulated defects) can be used to store test i-node attributes, allowing developers to observe how the OSI detects, reports, and mitigates data integrity issues without affecting production data. This mode is critical for developing and validating the fuzzy logic or reallocation algorithms described in the patent (FIG. 2).

graph TD
    A[Test Host System] -->|Test Data Objects w/ i-nodes| B(Data Channel)
    B --> C{Object-Based Storage Interface (OSI)}
    C --> |Command: Enable Controlled Degradation Mode| D[Diagnostic/Test Module]
    D --> |Define/Simulate Faults in Zone X| E(Normal Media Zones)
    D --> |Define/Simulate Faults in Zone Y| F(Sandboxed Failure Zone)
    C --> |Allocate Test i-node Attributes to Degraded Zones| E
    C --> F
    C --> |Monitor Fault Handling & Recovery| G[Logging & Reporting]

Combination Prior Art Scenarios with Open-Source Standards

These scenarios illustrate how the concepts of US Patent 9002795 could be combined with widely adopted open-source standards, further broadening the scope of prior art.

1. Integration with Ceph Object Storage (RADOS)

Enabling Description:
The method of US9002795 is integrated into a node participating in a Ceph object storage cluster, specifically within a Ceph OSD (Object Storage Daemon) operating on a physical storage device. In this scenario, the "host system" can be a Ceph client (e.g., using librados or a Ceph block device driver), which sends data objects to the Ceph cluster. An individual Ceph OSD, embodying the storage device of US9002795, internally manages its local storage media with multiple media zones having differing zone attributes. The "object-based storage interface" within the Ceph OSD analyzes the i-node attributes (or Ceph object metadata, which functions similarly to i-node attributes for Ceph objects) associated with incoming Ceph objects. It then schedules the storage of these metadata portions within its local, physically separated media zones based on determined access frequencies. For instance, Ceph object metadata related to frequently accessed data (e.g., for hot objects) could be placed in high-performance local zones (e.g., SSD tiers within the OSD), while metadata for colder, less frequently accessed objects could be placed in lower-performance, higher-capacity zones (e.g., HDD tiers). This offloads granular metadata placement intelligence from the distributed Ceph cluster layer to the individual OSD device, leveraging the device's intimate knowledge of its physical media characteristics.

2. Utilization with NVMe-oF and Zoned Namespace (ZNS) SSDs

Enabling Description:
The object-based data allocation method of US9002795 is implemented within an NVMe-oF target that exposes a Zoned Namespace (ZNS) SSD. The "host system" is a remote compute node connecting over an NVMe-oF fabric (e.g., RDMA, TCP). The "storage device" is the ZNS SSD, where its physically distinct "zones" (sequential write required zones) naturally correspond to the "multiple media zones with differing zone attributes of storage performance." The "object-based storage interface" is integrated into the NVMe controller (or a dedicated proxy controller) of the ZNS SSD. This interface receives data objects (which, in a ZNS context, might be logical units or streams that the host wishes to group) and their associated i-node attributes (or equivalent object metadata) via NVMe-oF commands. It then analyzes the i-node attribute access frequencies. For example, i-node read attributes for hot data are allocated to ZNS zones that are known to reside on faster, lower-latency segments of the NAND flash or have higher provisioned over-provisioning for faster garbage collection. Less frequently accessed i-node write attributes are allocated to ZNS zones on higher-capacity, potentially slower NAND segments, optimizing for endurance or write amplification characteristics of the ZNS drive. The NVMe-oF interface allows the remote host to explicitly or implicitly provide "requested storage attributes" that guide the ZNS device's internal attribute placement.

3. Enhancing Linux Kernel File Systems (e.g., Btrfs/ZFS) with OSD Capabilities

Enabling Description:
A Linux kernel file system, such as Btrfs or ZFS, is configured to utilize an underlying object-based storage device (OSD) that implements the principles of US9002795 as its block device. Instead of directly managing raw blocks, the file system interacts with the OSD through an enhanced object-based API. When the file system creates or modifies files, it sends "data objects containing i-node attributes" (or equivalent metadata structures that Btrfs/ZFS manage) to the OSD. The OSD's "object-based storage interface" then receives these i-node attributes. Critically, the file system can provide "hints" (analogous to requested storage attributes) to the OSD regarding the expected access patterns or criticality of certain metadata. The OSD, having full knowledge of its "multiple media zones with differing zone attributes of storage performance," analyzes the i-node attributes, determines their access frequencies (and potentially correlates with the file system's hints), and intelligently allocates the i-node read and write attributes to physically separate zones. For instance, the Btrfs metadata tree, or ZFS's ZIL (ZFS Intent Log), could have its i-node-like attributes split and stored by the OSD, with frequently accessed parts in high-performance zones and less critical or less frequently accessed parts in more resilient or higher-capacity zones, thereby optimizing the underlying storage for the file system's complex metadata management without the file system needing direct physical media knowledge.