Serverless edge computing, characterized by fine-grained resource allocation and rapid task scheduling, is effectively implemented in edge clouds to support a diverse array of Deep Neural Network (DNN) based inference tasks. However, before executing inference tasks, the system needs to load DNN models into a container, a process known as cold start. The cold start introduces significant latency, thereby prolonging the task completion time. In particular, model loading time constitutes approximately 50%–70% of the overall task lifecycle, making it comparable to the duration of inference execution. Fortunately, we notice that some layers are required by multiple models and only need to be loaded once if these tasks are scheduled onto one server, i.e., DNN layer sharing. However, the computational resource differences among edge servers and their limited memory capacity make task scheduling and layer loading decisions particularly challenging. Therefore, to fully leverage the potential of DNN layer sharing, we investigate the Layer Sharing aware Task Scheduling (LSTS) problem with the goal of minimizing the task completion time. We formulate it into a Quadratic Integer Programming (QIP) problem and linearize it into an Integer Linear Programming (ILP) form, which is then proved as NP-hard. To tackle the computation complexity, we propose a Randomized Rounding-based Layer-Sharing-Aware Task Scheduling algorithm (LSTS-RR). Through comprehensive experimental evaluations, we confirm the effectiveness of our algorithm, as it reduces task completion time by more than 36% compared to other state-of-the-art approaches across a range of widely recognized DNN models.

Edge-End Consortium, with the integration of edge computing servers and end devices like IoT devices, has emerged as a promising infrastructure for on-site computing power provision. But the high heterogeneity has raised unprecedented challenges in its resource management and task scheduling. Both WebAssembly and Container provide lightweight and portable way to consolidate and deploy microservices to combat the heterogeneity problem. However, the inherent advantages and disadvantages of WebAssembly and Container make it nontrivial to decide the microservice consolidation way and the deployment site, especially in the consideration of dependency between microservices. In this paper, we investigate the problem of mixed deployment of WebAssembly and Container based dependent microservices to strike a balance between communication and deployment cost toward high overall cost efficiency. We first cast this problem into a Quadratic Integer Programming (QIP) formulation and prove it as NP-hard. We then introduce a Randomized Rounding WebAssembly and Containerized Microservice Deployment (RR-WCMD) algorithm with polynomial computation complexity and guaranteed performance efficiency. Experiment results show that RR-WCMD can significantly lower the cost by an average of 34% in comparison with state-of-theart algorithms, thanks to the joint consideration of consolidation way and deployment site.

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.