Memory Partitioning in Memcached: An Experimental Performance Analysis

Memcached is a popular component of modern Web architectures, which allows fast response times - a fundamental performance index for measuring the Quality of Experience of end-users - for serving popular objects. In this work, we study how memory partitioning in Memcached works and how it affects system performance in terms of hit rate. Memcached divides the memory into different classes proportionally to the percentage of requests for objects of different sizes. Once all the available memory has been allocated, reallocation is not possible or limited, a problem called “calcification”. Calcification constitutes a symptom indicating that current memory partitioning mechanisms require a more careful design. Using an experimental approach, we show the negative impact of calcification on an important performance metric, the hit rate. We then proceed to design and implement a new memory partitioning scheme, called PSA, which replaces that of vanilla Memcached. With PSA, Memcached achieves a higher hit rate than what is obtained with the default memory partitioning mechanism, even in the absence of calcification. Moreover, we show that PSA is capable of “adapting” to the dynamics of clients' requests and object size distributions, thus defeating the calcification problem.