Maintaining SLOs of cloud-native applications via self-adaptive resource sharing

With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented

Toward sustainable serverless computing

Although serverless computing generally involves executing short-lived “functions,” the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charged for per-subsecond use of computational resources rather than the coarse-grained charging that is common with virtual machines and containers. To ensure that the startup times of serverless functions do not discourage their use, resource providers need to keep these functions hot, often by passing in synthetic data. We describe the real power consumption characteristics of serverless, based on execution traces reported in the literature, and describe potential strategies (some adopted from existing VM and container-based approaches) that can be used to reduce the energy overheads of serverless execution. Our analysis is, purposefully, biased toward the use of machine learning workloads because: (1) workloads are increasingly being used widely across different applications; (2) functions that implement machine learning algorithms can range in complexity from long-running (deep learning) versus short-running (inference only), enabling us to consider serverless across a variety of possible execution behaviors. The general findings are easily translatable to other domains.PostprintPeer reviewe