Predictive Antivirus Scanning in Distributed File Storage
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Solution Overview
Problem
Current anti-virus scanning in distributed file systems introduces latency and wastes resources due to blind scanning of all files, often during non-peak hours, without considering client behavior patterns, leading to inefficient use of compute resources and delayed access to frequently accessed files.
Innovation Solution
Implement predictive analytics using machine learning to determine the sequence of files likely to be accessed based on historical patterns, queuing these files for anti-virus scanning during the processing time of the initially accessed file, thereby reducing latency and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-virus scanning is performed before reading a file, then file security is ensured, but file access latency increases
Solution Approach 1:
The system performs anti-virus scanning in advance by predicting which files will be accessed based on historical patterns and client behavior. Files are scanned before they are actually requested, so when a file is accessed, the scanning is already complete or in progress, eliminating the latency that would otherwise occur at access time.
Solution Approach 2:
The system skips the traditional sequential process where files are scanned only when accessed. Instead, it rushes through the scanning of predicted files in advance during system idle periods or overlapping with other operations, so the security check is already done by the time the file is needed.
2Reliability
If anti-virus scanning is performed on all files, then comprehensive security coverage is achieved, but compute resources are wasted
Solution Approach 1:
The system applies different scanning strategies to different files based on their characteristics and predicted importance. Frequently accessed files are scanned in real-time before access, while less important files are scanned during background operations or skipped if low-risk. This localized approach ensures security where needed while conserving resources.
Solution Approach 2:
Instead of scanning all files equally, the system performs partial scanning on a selective basis. It uses machine learning to identify which files are most likely to be accessed and prioritizes scanning those files, performing excessive action only where necessary for security while reducing or eliminating scanning for less critical files.
3Ease of operation
If background anti-virus scanning is scheduled during evening hours, then user impact is minimized, but frequently accessed files may not be scanned in real-time
Solution Approach 1:
The system dynamically adjusts the scanning schedule based on actual file access patterns rather than using fixed evening-hour scheduling. Machine learning models continuously learn from access patterns and adjust which files are scanned, when they are scanned, and in what priority, allowing the system to adapt to changing user behaviors and ensure frequently accessed files are always scanned in real-time.
Solution Approach 2:
The system implements feedback loops where access patterns are continuously monitored and fed back into the machine learning models. This feedback mechanism allows the system to learn from actual usage and adjust scanning priorities dynamically, ensuring that files that become frequently accessed are automatically prioritized for real-time scanning while maintaining low user impact.
Data Source
AI summary
Implementations are provided herein for enhanced antivirus scanning in distributed file storage systems. In embodiments, we disclose using predictive, heuristic data to determine prospectively a sequence of files that may be called by a client based upon relationships between an initial filed called by a client and other files stored in the distributed file system. The sequence of files likely to be called in the future could, in some embodiments, be determined using machine language trained to analyze past client behavior patterns. Once a predictive file sequence has been determined, embodiments herein send files likely to be called in the very near future to an antivirus server for processing during a time period that overlaps the AV processing period for the original file that was called.