Geo-scale Analytics Query Scheduling Under Bandwidth Constraints
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Solution Overview
Problem
Conventional systems for computing analytics over geographically distributed data face challenges due to limited bandwidth and regulatory constraints, particularly when transferring large volumes of data across trans-oceanic links, which can lead to increased costs and data discard, and pose privacy concerns.
Innovation Solution
The system optimizes geo-scale analytics by generating query execution plans that schedule tasks across multiple data centers based on bandwidth costs, fault-tolerance constraints, and regulatory constraints, using replication strategies and pseudo-distributed measurements to minimize data transfer while ensuring compliance and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If geographically distributed data is transferred to a central location for analytics, then centralized processing can be performed, but bandwidth costs increase and regulatory constraints are violated
Solution Approach 1:
The patent segments the centralized analytics system into distributed query execution across multiple data centers. Instead of transferring all data to one location, the system divides the analytics workload and executes queries locally at each data center, then aggregates results. This eliminates the need for massive data transfers while maintaining analytical capabilities.
Solution Approach 2:
The patent inverts the conventional approach by instead of bringing data to the processing location, bringing the processing capability to the data location. Query execution is performed at each data center where data resides, and results are consolidated, reversing the traditional data movement paradigm.
2Productivity
If geographically distributed data is transferred to a central location, then analytics can be performed, but data transfer time increases
Solution Approach 1:
The analytics workload is segmented into local query execution tasks performed at each data center simultaneously, rather than sequential data transfer and centralized processing. This parallel execution model dramatically reduces the time required to complete analytics operations.
Solution Approach 2:
The system performs preliminary data partitioning and query planning before execution, organizing data and computation tasks in advance so that query execution can proceed immediately at each data center without waiting for data transfers.
3Reliability
If data is replicated across multiple data centers, then fault-tolerance improves, but bandwidth costs for data transfer increase
Solution Approach 1:
The patent applies local quality by replicating only the specific data partitions needed for each query at the appropriate data centers, rather than uniformly replicating all data everywhere. This selective replication minimizes bandwidth consumption while ensuring data availability for fault tolerance.
Solution Approach 2:
The system performs partial replication of data partitions based on query patterns and fault-tolerance requirements, replicating only what is necessary rather than complete redundancy. This balanced approach achieves reliability without excessive bandwidth consumption.
Data Source
AI summary
Various technologies described herein pertain to controlling geo-scale analytics with bandwidth and regulatory constraints. An analytical query (e.g., a recurrent analytical query, a non-recurrent analytical query, etc.) to be executed over distributed data in data partitions stored in a plurality of data centers can be received. Moreover, a query execution plan for the analytical query can be generated, where the query execution plan includes tasks. Further, replication strategies for the data partitions can be determined. A replication strategy for a particular data partition can specify one or more data centers to which the particular data partition is to be replicated if the particular data partition is to be replicated. The tasks of the query execution plan for the analytical query can further be scheduled to the data centers based on the replication strategies for the data partitions. The analytical query can be part of a workload of analytical queries.