Coupling energy efficiency and quality for consolidation of cloud workloads

Efficient usage of IT equipment in Data Centers requires modulating their power-consumption according to the actual workload. The most effective strategy to this aim consists in consolidating as many applications as possible on the smallest number of servers, so that idle devices can be shut down or put in low-power states. Usually, this process is driven by computing and networking resources requested by each application (e.g., CPU and RAM) and applies a certain degree of overcommitment, assuming that such resources are not fully used continuously. However, this approach is critical with real workload patterns, which usually change over time; as a matter of fact, consolidation in real scenarios often leads to either low efficiency or violations of Quality of Service (QoS) constraints, depending on the level of overcommitment. In this paper, we investigate a novel consolidation strategy based on an enhanced system model for the Infrastructure-as-a-Service cloud paradigm, which targets a better trade-off between Energy Efficiency and Quality of Service. We explicitly target modular cloud applications, which design is split into multiple components deployed in Virtual Machine (VM)s or containers. Our consolidation strategy allows to “freeze” parts of the application which are not currently used, making them available when requested with minimal latency. This improves energy saving with respect to other approaches, especially when idle VMs are present for backup or redundancy purposes, without degrading the service level. We compare multiple heuristics available in Optaplanner to solve our consolidation problem, and investigate improvements with respect to a more traditional approach. Our evaluation includes both simulations and experimentation in a real test-bed. The comparison shows that the Late Acceptance algorithm on average finds better solutions than other alternatives and energy efficiency improves up to 40% with respect to more conventional strategies, with deterioration of QoS indexes below 1%.