Adaptive sharding system for optimized storage and retrieval of cloud data

Abstract

An adaptive sharding system (100) for optimized storage and retrieval of cloud data, comprising the following: A data analytics module configured to collect and analyze data access patterns and predict future access trends; a dynamic shard allocation module configured to partition data into adaptive shards, prioritizing frequently accessed data for low-latency zones and less critical data for cost-effective regions, with dynamic shard boundary adjustments; a fault tolerance mechanism that uses replication and erasure coding to ensure data availability across multiple servers; an integration interface for seamless integration with cloud platforms via APIs; a scalability mechanism that allows automatic system expansion when resources are added.

Description

[0001] The present invention relates to the field of cloud computing and data storage technologies, in particular to an adaptive sharding system for optimized storage and retrieval of cloud data.

[0002] The exponential growth of data in modern applications has created a pressing need for scalable and efficient storage solutions. Traditional sharding techniques based on static partitioning often fail to adapt to fluctuating access patterns, leading to performance bottlenecks and inefficient resource utilization. As cloud-based systems are deployed in industries such as e-commerce, healthcare, and finance, the need for intelligent, real-time data distribution mechanisms is critical to maintaining performance and scalability in dynamic environments.

[0003] Existing solutions such as consistent hashing and static shard allocation meet basic scalability requirements but cannot adapt to changing workloads. For example, systems such as Amazon DynamoDB and Google Bigtable use predefined strategies that struggle with uneven data distribution and require manual intervention to rebalance shards when workloads shift. Similarly, tools such as HDFS and Elasticsearch focus on large-scale storage or flexibility but lack the automation required for real-time optimization. Microsoft Azure Cosmos DB offers automatic scaling but remains constrained by static policies.This gap highlights the need for a machine learning-based system that dynamically adapts shard configurations to optimize performance, ensure fault tolerance, and reduce operational costs.To solve the problem, the present invention provides an adaptive sharding system for optimized cloud data storage and retrieval.

[0004] The system uses machine learning to monitor and analyze data access patterns, enabling real-time adjustments to shard configurations.

[0005] The system ensures high fault tolerance and reliability.

[0006] The system optimizes the retrieval speed.

[0007] The system scales automatically by reallocating shards or adding resources.

[0008] The system reduces operating costs.

[0009] The system has dynamic adaptability for optimized data distribution.

[0010] The system has reduced operating costs through resource efficiency.

[0011] In one embodiment, the system introduces a dynamic approach to managing cloud data storage and retrieval. The system uses machine learning to continuously monitor and analyze data access patterns, enabling the creation and adaptation of data shards in real time. Unlike traditional sharding methods, this invention adapts to changing workloads without requiring manual reconfiguration, ensuring even resource distribution and high performance. The system's fault-tolerant design includes redundancy and error-checking protocols to prevent data loss. Furthermore, it integrates with existing cloud platforms, providing a seamless and scalable solution for modern data-intensive applications.By combining adaptability, fault tolerance, and cost-effectiveness, this system is the answer to critical challenges in cloud data management.

[0012] In one embodiment, the system integrates seamlessly with existing cloud platforms via APIs, making it easy to adopt into various operational ecosystems. By addressing key challenges such as uneven resource distribution, high latency, and escalating storage costs, the Adaptive Sharding System provides a cost-effective, scalable, and robust solution for modern data-intensive industries.

[0013] The invention is explained again below with reference to the figure. It shows: Fig. : shows a block diagram of an adaptive sharding system (100) for optimized cloud data storage and retrieval

[0014] Fig.shows a block diagram of an adaptive sharding system (100) for optimized cloud data storage and retrieval. The adaptive sharding system (100) comprises several key components designed to improve the performance, scalability, and cost-effectiveness of cloud data storage. These include the data analytics module, which continuously collects and analyzes metrics about data access patterns such as frequency, geographic origin, and request types. Using machine learning algorithms, the module predicts future access trends and determines the most efficient shard configurations. Based on this analysis, the system dynamically divides the data into adaptive shards, prioritizing frequently accessed data in low-latency zones and storing less important data in cost-efficient regions.The system also dynamically adjusts shard boundaries to accommodate workload shifts and ensure optimal performance.

[0015] Furthermore, the system integrates fault-tolerance mechanisms such as redundancy through replication and erasure coding, where redundant shards are distributed across multiple servers to ensure data availability in the event of failures. It integrates seamlessly with major cloud platforms via APIs, enabling easy adoption. The system's architecture also enables linear scalability, meaning capacity expands and improves automatically and without manual intervention as resources are added. In experimental tests with a 1 TB dataset in a multi-cloud environment, the prototype demonstrated a 30 percent improvement in data retrieval speed, a 25 percent reduction in storage costs, and a 40 percent reduction in error recovery time, thus proving its efficiency and robustness. List of reference symbols 100 systems

Claims

[1] An adaptive sharding system (100) for optimized cloud data storage and retrieval, comprising: A data analysis module configured to collect and analyze data access patterns and predict future access trends; a dynamic shard allocation module configured to partition data into adaptive shards, prioritizing frequently accessed data for low-latency zones and less critical data for cost-effective regions, with dynamic shard boundary adjustments; a fault-tolerance mechanism that uses replication and erasure coding to ensure data availability across multiple servers; an integration interface for seamless integration with cloud platforms via APIs; a scalability mechanism that allows automatic system expansion as resources are added. [2] The system (100) of claim 1, wherein the data analysis module is further configured to track and analyze geographic data access patterns to optimize data placement in regional storage zones. [3] The system (100) of claim 1, wherein the integration interface supports integration with multiple cloud platforms simultaneously, enabling storage and retrieval of data in multiple clouds. [4] The system (100) of claim 1, wherein the scalability mechanism enables horizontal scaling by adding additional storage nodes without interrupting ongoing data operations. [5] The system (100) of claim 1, wherein the fault tolerance mechanism further comprises real-time monitoring and automatic failover to backup shards in case of server or network failures. [6] The system (100) of claim 1, wherein the system uses a tiered storage model with hot, warm, and cold storage tiers based on the frequency and criticality of data access. [7] The system (100) of claim 1, wherein the machine learning algorithm module continuously updates the shard configurations in real time based on ongoing data access patterns and system performance metrics.

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