# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 5 Platform Resilience Mechanisms
### Traffic Management and Fault Tolerance
#### 5.1.1 Network Infrastructure Adaptation and Fault Tolerance
To validate this operation, we sent data in compliance with the routes defined using the network policies and observed the platform's proper operation. When attempting to bypass a service by sending it via an incorrect path, the platform restricts the passage of information. Figure 5 shows the network policies to enter data into the platform and connect internally to the microservices. The network policies, in this case, allow the creation of a bridge between both namespaces and find the services by using their names and not the IP addresses, avoiding connectivity problems when IP addresses change. Each policy is created with a configuration file shown in Listing 1. Figure 4: Microservice HPA control. ## 7 Related Work

We propose a novel distributed computing platform that leverages resilience mechanisms to provide application fault tolerance. Although we found a number of works related to fault tolerance on cloud computing systems, few of those seem to tackle the specific problem of providing fault tolerance to high-latency analytics applications. Our platform is based on a microservices architecture that allows the distribution of high-latency analytics applications based on the rapid provision and reuse of self-contained services. In [64] [PERSON] et al. present an architecture for fault-tolerant IoT services deployed on edge and cloud infrastructures. This approach is based on integrating Kafka and Kubernetes technologies to provide a mechanism to tackle hardware and network failures. The platform is tested by deploying a surveillance camera system. Our approach leverages fault-tolerance capabilities integrated within Kafka and Kubernetes to provide a completely fault-tolerant pipeline, and was tested using high-latency analytics applications. Additionally, our approach incorporates resilience mechanisms such as the autoscaling component to adapt infrastructure to increased workload automatically. [PERSON] et al. [65] propose a framework that leverages state-of-the-art cloud technologies to support the deployment of artificial intelligence services with high transmission latency and high bandwidth consumption on edge-cloud infrastructures. They focus on partitioning and distributing Deep Neural Networks (DNN) to improve response times. Our approach proposes a platform based on a microservices architecture that allows the partitioning of high-latency analytics applications, improving performance. In addition, our approach provides a set of predefined and reusable microservices that facilitates the fast construction and deployment of new resilient applications. Given the memory size and processing capability of today's commodity machines, it is inevitable to run distributed machine learning (DML) on multiple machines [66]. [PERSON] et al., propose a scalable, high-performance, and fault-tolerant Distributed Machine Learning (DML) network architecture on top of Ethernet and commodity devices. BML builds on BCube topology and runs a fully-distributed gradient synchronization algorithm. In our approach the goal of the Kubernetes orchestration cluster is also to process machine learning datasets, node/link failures were simulated, and fault tolerances and traffic management were achieved to keep services performance. One of the most critical challenges of the Internet of Things (IoT) is to provide real-time services. [PERSON] et al., propose in [67] a comprehensive SDN-based fault-tolerant architecture in IoT environments. In the proposed scheme, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the main and backup non-overlapping paths between network equipment. When comparing the suggested scheme to two policies for constructing routes from source to destination, it is found that the proposed method improves service quality parameters such as packet loss, latency, and packet jitter while reducing error recovery time. In our work, SDN technology provides a fault

Figure 5: Network policy.