Serverless computing, underpinned by an event-driven approach with transient stateless containers, significantly enhances resource efficiency and simplifies function development. To maintain an acceptable Quality of Service (QoS) agreed in the Service Level Agreement (SLA), service providers need to improve the response latency while considering resource efficiency. However, cold-start delays in container initialization often lead to considerable latency in these applications. Existing mitigation strategies, such as pre-warming and function caching, are inadequate due to workload skewness and oscillation across edge nodes. These limitations are particularly critical in resource-constrained edge environments. We must jointly consider multiple factors, such as node status, function resource requirement and function popularity. To overcome these limitations, this paper presents FaasOrc , a bi-level function orchestration framework to mitigate workload skewness and oscillation across edge nodes. FaasOrc uses a cluster-level scheduler to schedule requests and a node-level manager to detect popular functions. Our comprehensive evaluation, consisting of two parts: simulations and a real-system prototype over Knative , benchmarks the proposed solution against existing scheduling and caching strategies. The findings highlight our method’s capability to reduce the response latency by 31.4%.

Edge–cloud computing infrastructures are increasingly widespread as they combine the flexibility of cloud-native development tools with the performance and security of distributed computing environments. Function-as-a-Service has emerged as a powerful abstraction that overcomes the limitations of a micro-service architecture. However, it generally does not support stateful functions, making it unsuitable for many practical applications in, e.g., Internet of Things (IoT) and real-time analytics. In this paper, we explore a novel paradigm, based on stateful asynchronous agents, that goes beyond traditional serverless computing. We focus on several key technical aspects: programming model, deployment procedures, design of a flexible compute node, and state management. We illustrate our paradigm using the EDGELESS platform as a concrete implementation of this stateful agents’ pattern. We report proof-of-concept experiment results obtained in a testbed with heterogeneous resource-constrained edge nodes that showcase some distinguishing features of our platform: scalable management of lightweight function instances, the advantage of keeping the state local at function instances, and delegated orchestration to enable a third-party agent to make migration decisions in a group of local nodes.

Serverless computing is regarded as a good match for distributed edge infrastructures. However, bringing the function-as-a-service model to a highly dynamic, distributed, and heterogeneous pool of resources has its fair amount of challenges. Allocation of functions to the proper resource is an essential operation that avoids over-provisioning and the relative waste of computational resources. In this work, we propose Dike, a bi-level function scheduling and resource allocation framework designed to meet end-to-end latency SLOs (service-level objectives). Dike leverages deep reinforcement learning to balance resource provisioning and monetary cost by incorporating both composite cost and SLO violations into the reward. Extensive simulations with real-world production workloads demonstrate the superiority of Dike. Experimental results show that the proposed algorithms approximate the results of state-of-the-art ILP solver within a factor of 1.08 while dramatically reducing the scheduling time.

With the widespread expansion of telecommunication networks, the increase in the number and complexity of base stations has led to an exponential growth in the volume of alarms. Traditional alarm prediction based on expert experience or rules has posed significant challenges due to the demand for engineers’ expertise and workload. It has become imperative to enhance efficiency by employing data-driven approaches for network alarm prognosis. In this paper, a data-driven alarm prediction model is proposed to support the alarm prognosis in base stations. To improve model performance, the proposed approach utilises ensemble deep learning methods to address the heterogeneity and highly imbalanced alarm dataset. The model is trained and validated using a dataset provided by British Telecom (BT) group. The validation results demonstrate that the proposed method achieves a top-5 accuracy of up to 90% in predicting alarms across 170 categories on the validation set.

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

Serverless edge computing adopts an event-based paradigm that provides back-end services and dynamically provisions resources as needed, resulting in efficient resource utilization. To improve the end-to-end latency and revenue, service providers need to optimize the number and placement of serverless containers while considering the system cost (i.e., latency cost and container running cost) incurred by the provisioning. The particular reason for this circumstance is that frequently creating and destroying containers not only increases the system cost but also degrades the time responsiveness due to the cold-start process. Function caching is a common approach to mitigate the coldstart issue. However, function caching requires extra hardware resources and hence incurs extra system costs. Furthermore, the dynamic and bursty nature of serverless invocations remains an under-explored area. Hence, it is vitally important for service providers to conduct a context-aware request distribution and container caching policy for serverless edge computing. In this paper, we study the request distribution and container caching problem in serverless edge computing. We prove the proposed problem is NP-hard and hence difficult to find a global optimal solution. We jointly consider the distributed and resourceconstrained nature of edge computing and propose an optimized request distribution algorithm that adapts to the dynamics of serverless invocations with a theoretical performance guarantee. Also, we propose a context-aware probabilistic caching policy that incorporates a number of characteristics of serverless invocations. Via simulation and implementation results, we demonstrate the superiority of the proposed algorithm by outperforming existing caching policies in terms of the overall system cost and cold-start frequency by up to 62.1% and 69.1%, respectively.

With the ever-growing demand for low-latency network applications, edge computing emerges as a new paradigm that provides computation and storage resources in close proximity to end-users. Many research efforts have resorted to network function virtualization, wherein network applications are provisioned as service function chains at edge clouds. However, due to the traffic dynamics and limited resource capacity at the network edge, how to efficiently embed service chains with latency optimization and resource efficiency remains as a challenging problem. As most existing research efforts largely overlook the bottle-necked resources of VNFs in the VNF scaling, we seek a more realistic approach to provisioning VNF instances across multiple edge clouds. Also, given the limited resources at the edge, it is of significant importance to improve the VNF utilization rate. Specifically, we formulate the VNF scaling problem as an integer linear programming (ILP) problem, aiming to minimize the end-to-end latency for service function chains. To solve this problem, we devise a novel bottleneck-aware algorithm that manages the number and deployment of newly created instances. After that, we propose an online algorithm for traffic steering to improve the utilization rates of VNF instances and avoid congestion on hotspot links. The proposed algorithm is shown to provide good performance by trace-driven simulation in real-world topologies.

Future networks are expected to support cross-domain, cost-aware and fine-grained services in an efficient and flexible manner. Service Function Chaining (SFC) has been introduced as a promising approach to deliver these services. In the literature, centralized resource orchestration is usually employed to process SFC requests and manage computing and network resources. However, centralized approaches inhibit the scalability and domain autonomy in multi-domain networks. They also neglect location and hardware dependencies of service chains. In this paper, we propose Distributed Federated Service Chaining (DFSC), a framework for orchestrating and maintaining SFC placement in a distributed fashion while sharing only a minimal amount of domain information and control. First, a deployment cost minimization problem is formulated as an Integer Linear Programming (ILP) problem with fine-grained constraints for location and hardware dependencies. We show that this problem is NP-hard. Then, a placement algorithm is devised to use information only on inter-domain paths and border nodes. Our extensive experimental results demonstrate that DFSC efficiently optimizes the deployment cost, supports domain autonomy and enables faster decision-making. The results also show that DFSC finds solutions within a factor 1.15 of the optimal solution on average. Compared to a centralized approach in the literature, DFSC reduces the deployment cost by up to 20% and uses 70% less decision-making time.