Distributed Data Load Balancing for Scalable Key-Value Cache Systems

In recent years, in-memory key-value cache systems have become increasingly popular in tackling real-time and interactive data processing tasks. Caching systems are often used to help with the temporary storage and processing of data. Due to skewed and dynamic workload patterns, e.g. data increase/decrease or request changes in read/write ratio, it can cause load imbalance and degrade performance of caching systems. Migrating data is often essential for balancing load in distributed storage systems. However, it can be difficult to determine when to move data, where to move data, and how much data to move. This depends on the resources required, e.g. CPU, memory and bandwidth, as well as polices on data movement. Since frequent and global rebalance of systems may affect the QoS of applications utilizing caching systems, it is necessary to minimize system imbalances whilst considering the total migration cost. We propose a novel distributed load balancing method for the mainstream Cloud-based data framework (Redis Cluster). We show how distributed graph clustering through load balancing can be used to exploit varying rebalancing scenarios comprising local and global needs. During the rebalancing process, three phrases are adopted -random walk matching load balancing, local round-robin migration and data migration between the trigger node and new added servers. Our experiments show that the proposed approach can reduce migration time compared with other approach by 30s and load imbalance degree can be reduced by 4X when the locality degree reaches 50% whilst achieving high throughput.