MEC: The Memory Elasticity Controller

The dynamic nature of application and service requirements has lead the cloud community to invest in the study and development of elasticity features that have the ability to re-dimension resource capacities dynamically. Both online applications under unpredictable workloads or scientific workflows with different datasets require autonomic scaling in order to avoid either performance degradation due to an insufficient resource capacity or paying for additional, sub-utilized and possibly unnecessary capacity. In an effort to improve utilization, focus has recently turned to vertical elasticity where the processing, memory or storage capacity of a single virtual machine (VM) is adjusted in accordance with the application's needs. Given the increasing influence of memory availability on performance, this work presents the main features of the Memory Elasticity Controller (MEC). This VM allocation tool aims to improve the throughput of jobs or workflow tasks by continuously and judiciously calibrating the amount of host memory allocated to each VM in accordance with that VM's respective job's changing run time requirements while, at the same time, trying to avoid compromising the job's performance. This paper presents the tool's architecture and describes the functionality adopted to manage and provide vertical memory elasticity to concurrently executing VMs on a server. Results show that MEC is able to provide both resource providers and applications with an additional opportunity to improve throughput and performance.