HaRMony: Heterogeneous-Reliability Memory and QoS-Aware Energy Management on Virtualized Servers

The explosive growth of data increases the storage needs, especially within servers, making DRAM responsible for more than 40% of the total system power. Such a reality has made researchers focus on energy saving schemes that relax the pessimistic DRAM circuit parameters at the cost of potential faults. In an effort to limit the resultant risk of critical data disruption, new methods were introduced that split DRAM into domains with varying reliability and power. The benefits of such schemes may have been showcased on simulators but have neither been implemented on real systems with a complete software stack, nor have been combined with any energy-reliability OS management policies. In this paper, we are the first to implement and evaluate HaRMony, a heterogeneous-reliability memory framework, in conjunction with QoS-aware energy management policies on a server with a complete virtualization stack. HaRMony overcomes the practical restrictions stemming from default hardware specifications, which were neglected in prior works, by introducing a software-based memory interleaving scheme. Furthermore, we expose the capabilities of HaRMony to the QEMU-KVM hypervisor through two unique policies. The first policy enables the hypervisor to seek the most power efficient DRAM circuit parameters based on the server availability requested by the user. The second policy enables users to exploit the inherent application error-resiliency by allowing them to limit the error protection mechanisms and allocate data structures on variably-reliable memory domains. Our evaluation shows that HaRMony reduces the performance overhead incurred due to disabling hardware interleaving from 29.3% down to 1.1% and leads to 17.7% DRAM energy savings and 8.6% total system energy savings on average in case of native execution of 28 benchmarks on an ARMv8-based server. Finally, we demonstrate that our QoS-aware scaling governor integrated with QEMU-KVM can dynamically scale the DRAM parameters, while reducing the system energy by 8.4% and meeting the targeted QoS even under extreme temperatures.