The emerging microservice architecture offers opportunities for accommodating delay-sensitive applications in edge. However, such applications are computation-intensive and energy-consuming, imposing great difficulties to edge servers with limited computing resources, energy supply, and cooling capabilities. To reduce delay and energy consumption in edge, efficient microservice orchestration is necessary, but significantly challenging. Due to frequent communications among multiple microservices, service deployment and request routing are tightly-coupled, which motivates a complex joint optimization problem. When considering multi-instance modeling and fine-grained orchestration for massive microservices, the difficulty is extremely enlarged. Nevertheless, previous work failed to address the above difficulties. Also, they neglected to balance delay and energy, especially lacking dynamic energy-saving abilities. Therefore, this paper minimizes energy and delay by jointly optimizing microservice deployment and request routing via multi-instance modeling, fine-grained orchestration, and dynamic adaptation. Our queuing network model enables accurate end-to-end time analysis covering queuing, computing, and communicating delays. We then propose a delay-aware reinforcement learning algorithm, which derives the static service deployment and routing decisions. Moreover, we design an energy-aware dynamic frequency scaling algorithm, which saves energy with fluctuating request patterns. Experiment results demonstrate that our approaches significantly outperform baseline algorithms in both delay and energy consumption.

Service mesh architectures are emerging as a promising microservice paradigm for developing online cloud applications. However, in large-scale microservice scenarios, frequent service communications, intricate call dependencies, and stringent latency requirements bring great pressure to efficient service mesh orchestration. In this case, the problems of service deployment and request routing based on service mesh architectures are tightly-coupled and interdependent, and cannot be effectively optimized individually, enlarging the difficulty for collaborative orchestration. When microservice multiplexing, parallel dependencies, and multi-instance modeling are considered, the difficulty is further aggravated. Nonetheless, most existing work failed to propose appropriate models and methods for the above challenges. Therefore, this article studies the large-scale service mesh orchestration with probabilistic routing and constrained bandwidths for parallel call graphs. We leverage the open Jackson queuing network theory to capture crucial microservices and analyze request processing, queuing, and communication latency for massive user requests in a fine-grained way. Then, this article proposes an efficient three-stage heuristic, which achieves elegant multi-instance consolidation and probabilistic multi-queue routing to reduce response latency and cost. We also provide the algorithm complexity and mathematical analysis of the performance. Finally, extensive trace-driven experiments are performed to validate the superiority of our proposed algorithm over other baselines.

The eco-driving strategy that targets driving speed optimization is recognized as a promising technique to improve vehicle energy efficiency. However, it is difficult to achieve real-time eco-driving control of hybrid electric vehicle (HEV) since the speed optimization and powertrain energy management should be resolved simultaneously. This paper proposes a hierarchical control architecture consisting of learning-based velocity planner and real-time energy management system. In the upper stage, Proximal Policy optimization (PPO) agent is trained to generate acceleration which meets multiple control objectives. The lower stage adopts Equivalent Consumption Minimization Strategy (ECMS) for real-time power split control considering powertrain dynamics. Finally, the eco-driving simulations of six signalized intersections in Nanjing are conducted. Compared with two different rule-based strategies, the proposed control architecture can achieve at least 7.39% of fuel economy saving and avoid a significant drop in the battery state of charge at the expense of higher than 5% of travel time. Simulation results also prove that the proposed strategy has an energy-saving potential in unseen scenarios.