Analysis of Server Throughput For Managed Big Data Analytics Frameworks

Abstract

Managed big data frameworks, such as Apache Spark and Giraph demand a large amount of memory per core to process massive volume datasets effectively. The memory pressure that arises from the big data processing leads to high garbage collection (GC) overhead. Big data analytics frameworks attempt to remove this overhead by offloading objects to storage devices. At the same time, infrastructure providers, trying to address the same problem, attribute more memory to increase memory per instance leaving cores underutilized. For frameworks, trying to avoid GC through offloading to storage devices leads to high Serialization/Deserialization (S/D) overhead. For infrastructure, the result is that resource usage is decreased. These limitations prevent managed big data frameworks from effectively utilizing the CPU thus leading to low server throughput. We conduct a methodological analysis of server throughput for managed big data analytics frameworks. More specifically, we examine, whether reducing GC and S/D can help increase the effective CPU utilization of the server. We use a system called TeraHeap that moves objects from the Java managed heap (H1) to a secondary heap over a fast storage device (H2) to reduce the GC overhead and eliminate S/D over data. We focus on analyzing the system's performance under the co-location of multiple memory-bound instances to utilize all available DRAM and study server throughput. Our detailed methodology includes choosing the DRAM budget for each instance and how to distribute this budget among H1 and Page Cache (PC). We try two different distributions for the DRAM budget, one with more H1 and one with more PC to study the needs of both approaches. We evaluate both techniques under 3 different memory-per-core scenarios using Spark and Giraph with native JVM or JVM with TeraHeap. We do this to check throughput changes when memory capacity increases.