The Publish/Subscribe (pub/sub) paradigm is widely used in the Internet of Things (IoT). Standalone sensors, wearables, and other devices act as producers that publish messages to consumers such as edge servers or even other IoT devices. Selecting and configuring a pub/sub protocol for an IoT system requires considering network requirements, device reliability, and required Quality-of-Service guarantees. Pub/sub benchmarking suites can help compare expected behavior of various protocols, implementations, and network configurations. However, current pub/sub benchmarks focus primarily on stress testing systems assuming mostly static configurations of homogeneous publishers which are not representative of real-world IoT deployments. To address this, we present PSMark, a distributed, multi-protocol benchmark for evaluating topic-filtered pub/sub systems under workloads representative of real-world IoT environments. PS-Mark supports (i) workloads representative of heterogeneous IoT device deployments including variations in device communication parameters, (ii) evaluation of distributed IoT deployments with multiple data aggregation servers, (iii) cross-protocol measurements across MQTT and DDS, with extensibility to additional protocols, and (iv) a modular design for adding additional metrics and interfaces. We further construct twelve IoT-focused workloads derived from seven real-world datasets in the domains of manufacturing, healthcare, smart homes, and smart cities. Finally, we benchmark five popular MQTT brokers and one DDS implementation using PSMark and analyze their performance across multiple testbeds and Quality-of-Service settings.

Distributed monitoring is an essential functionality to allow large cluster federations to efficiently schedule applications on a set of available geo-distributed resources. However, periodically reporting the precise status of each available server is both unnecessary to allow accurate scheduling and unscalable when the number of servers grows. This paper proposes Acala, an aggregate monitoring framework for geo-distributed Kubernetes cluster federations which aims to provide the management cluster with aggregated information about the entire cluster instead of individual servers. Based on actual deployment under a controlled environment in the geo-distributed Grid’5000 testbed, our evaluations show that Acala reduces the cross-cluster network traffic by up to 97% and the scrape duration by up to 55% in the single member cluster experiment. Our solution also decreases cross-cluster network traffic by 95% and memory resource consumption by 83% in multiple member cluster scenarios. A comparison of scheduling efficiency with and without data aggregation shows that aggregation has minimal effects on the system’s scheduling function. These results indicate that our approach is superior to the existing solution and is suitable to handle large-scale geo-distributed Kubernetes cluster federation environments.

Monitoring plays a vital role in geo-distributed cluster federation environments to accurately schedule applications across geographically dispersed computing resources. However, using a fixed frequency for collecting monitoring data from clusters may waste network bandwidth and is not necessary for ensuring accurate scheduling. In this paper, we propose Adaptive Prometheus Federation (AdapPF), an extension of the widely-used open-source monitoring tool, Prometheus, and its feature, Prometheus Federation. AdapPF aims to dynamically adjust the collection frequency of monitoring data for each cluster in geo-distributed cluster federations. Based on actual deployment in the geo-distributed Grid'5000 testbed, our evaluations demonstrate that AdapPF can achieve comparable results to Prometheus Federation with 5-seconds scrape interval while reducing cross-cluster network traffic by 36%.

Using Software-Defined Networking (SDN) in edge computing environments allows for more flexible flow monitoring than traditional networking methods. In SDN, the controller collects statistics from all switches and can communicate with switches to dynamically manage the entire network. However, monitoring per-flow or per-switch mechanisms to obtain the flow statistics from all of the switches may significantly increase bandwidth costs between switches and the control plane. In this paper, we propose a Bandwidth Cost First (BCF) algorithm to reduce the number of monitored switches and therefore lower the monitoring cost. The experiment results show that our algorithm outperforms the existing technique by reducing the number of monitored switches by 56%, leading to a reduction in bandwidth overhead of 41% and switch processing delay by 25%.

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.

Network Function Virtualization (NFV) pushes the hardware-based network functions to generic servers as software and brings a highly flexible for deployment. The availability of Virtual Machines (VMs) enables the dynamic placement of Virtual Network Functions (VNFs) on demand, and it can reduce a large number of manual configuration processes that increase deployment efficiency. However, some services require more than one VNF to process. Therefore, the network flows need to traverse a set of sequential network functions called Service Function Chain (SFC). How to efficiently route traffic along service function chain and place VNFs in a network under operational constraints is a crucial issue. In this paper, we must overcome two challenges: (1) determining a flow path that traverses suitable network functions in the required order to meet the requirement of services, and (2) considering network loading and other dynamic characteristics when traffic is routed through existing VNFs. Thus, we present methods to solve the routing and placement problems for the service function chain. Our solutions transform the network representation to a virtual layered graph that considers NFV processing latency and allows conventional shortest path algorithms to solve the problem. We are not only pursuing high success rates to serve more flows but also taking into account the execution time of the algorithms.