Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one application may contain hundreds of microservices, which constitute an enormous search space that is unfeasible to explore exhaustively. Thus, we propose RAMBO, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals. Experiments conducted on a real microservice workload demonstrate that RAMBO can correctly characterize each microservice and efficiently discover Pareto-optimal solutions. We envision that the proposed methodology and results will benefit future resource planning, cluster orchestration, and job scheduling.

Process technology-driven performance and energy efficiency improvements have slowed down as we approach physical design limits. General-purpose manycore architectures attempt to circumvent this challenge, but they have a significant performance and energy-efficient gap compared to special-purpose solutions. Domain-specific architectures (DSAs), an instance of heterogeneous architectures, efficiently combine general-purpose cores and specialized hardware accelerators to boost energy efficiency and provide programming flexibility. Indeed, the hardware, software, and systems aspects in DSAs are highly tailored to maximize the energy efficiency of applications in a target domain. As DSAs and their conceptualization advance rapidly, there is a strong need to understand the research problems that need immediate attention. This article discusses the primary research directions in the design and runtime management of DSAs. Then, it surveys some promising approaches and highlights the outstanding research needs.