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In this work, three important and necessary design decisions, (1) workload partitioning, (2) node assignment, and (3) task execution order, are identified for real-time divisible load scheduling.
From our practical experience, the key determinants to success in this endeavor are adherence to the following principles: (1) Design for change; (2) Provide for storage subsystem I/O coordination; (3) Employ workload partitioning and load balancing techniques; (4) Employ caching; (5) Schedule the workload; and (6) Understand the workload.
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Section 'Mathematical formulation of scalable workload-driven partitioning scheme', explains how scalable workload-driven partitioning is formulated mathematically.
The quality of the scalable workload-driven partitioning algorithm is assessed using TPC-C workload [20].
Section 'Design of scalable workload-driven partitioning', presents the design of the scalable workload-driven partitioning and partitioning strategy.
Fig. 2 Scalable Workload-Driven Partitioning Fig. 3 Design of scalable workload-driven partitioning based on data access patterns.
Demonstration of detailed experiments that show the effectiveness of workload-driven partitioning scheme in forming partitions, that balance the workload among the partitions is described.
Section 'Comparison of static, dynamic and scalable workload-driven partitioning' discusses comparison of static, dynamic and scalable workload-driven partitioning.
Scalable Workload-Driven Partitioning (Partitioning based on Data Access Patterns) Scalable workload-driven partitioning is not static or dynamic partitioning scheme.
After analyzing the performance of scalable workload-driven partitioning in Amazon SimpleDB, it is comprehended that scalable workload-driven partitioning has got higher throughput and low response time in Amazon SimpleDB.
Scalable workload-driven partitioning is done over a set of warehouses (wid as the partitioning key), and for a given workload.
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