Reducing Memory Fragmentation with Performance-Optimized Dynamic Memory Allocators in Network Applications

Abstract

The needs for run-time data storage in modern wired and wireless network applications are increasing. Additionally, the nature of these applications is very dynamic, resulting in heavy reliance to dynamic memory allocation. The most significant problem in dynamic memory allocation is fragmentation, which can cause the system to run out of memory and crash, if it is left unchecked. The available dynamic memory allocation solutions are provided by the real time Operating Systems used in embedded or general-purpose systems. These state-of-the-art dynamic memory allocators are designed to satisfy the run-time memory requests of a wide range of applications. Contrary to most applications, network applications need to allocate too many different memory sizes (e.g. hundreds different sizes for packets) and have an extremely dynamic allocation and de-allocation behavior (e.g. unpredictable web-browsing activity). Therefore, the performance and the de-fragmentation efficiency of these allocators is limited. In this paper, we analyze all the important issues of fragmentation and the ways to reduce it in network applications, while keeping the performance of the dynamic memory allocator unaffected or even improving it. We propose highly customized dynamic memory allocators, which can be configured for specific network needs. We assess the effectiveness of the proposed approach in two representative real-life case studies of wired and wireless network applications. Finally, we show very significant reduction in memory fragmentation and increase in performance compared to state-of-the-art dynamic memory allocators utilized by real-time Operating Systems.