Abstract Cloud services have become increasingly popular for developing large-scale applications due to the abundance of resources they offer. The scalability and accessibility of these resources have made it easier for organizations of all sizes to develop and implement sophisticated and demanding applications to meet demand instantly. As monetary fees are involved in the use of the cloud, one of the challenges for application developers and operators is to balance their budget constraints with crucial quality attributes, such as availability. Industry standards usually default to simplified solutions that cannot simultaneously consider competing objectives. Our research addresses this challenge by proposing a Cost-Availability Aware Scaling (CAAS) approach that uses multi-objective optimization of availability and cost. We evaluate CAAS using two open-source microservices applications, yielding improved results compared to the industry standard CPU-based Autoscaler (AS). CAAS can find optimal system configurations with higher availability, between 1 and 2 nines on average, and reduced costs, 6% on average, with the first application, and 1 nine of availability on average, and reduced costs up to 18% on average, with the second application. The gap in the results between our model and the default AS suggests that operators can significantly improve the operation of their applications.

Abstract We consider a setting where applications, such as websites or games, need causal access to objects available in geo-replicated cloud data stores. Common ways of implementing causal consistency involve hiding objects while waiting for their dependencies or waiting for server replicas to synchronize. To minimize delays and retrieve objects faster, applications may try to reach different server replicas at once. This entails a cost because providers charge for each reading request, including reading misses where the causal copy of the object is unavailable. Therefore, latency and cost are conflicting goals, which we control by selecting where to read and when. We formulate this challenge as a multi-criteria optimization problem and propose five non-dominated reading strategies, four of which are Pareto optimal, in a setting constrained to two server replicas. We validate these solutions on the following real cloud storage services: AWS S3, DynamoDB and MongoDB. Savings of as much as 50% on reading costs, with no significant or even a positive impact on latency, demonstrate that both clients and cloud providers could benefit from richer services compatible with these retrieval strategies.

Cloud-based services are a current approach for developing large-scale applications with advantages such as flexibility, access to on-demand resources, and business agility. The overall application functionality results from complex interactions of many decoupled services, each having its operational specificity. Due to this complexity, the manual configuration of these systems is very arduous, error-prone and likely to impair the quality of service, leading to malfunctioning services, lowering availability and accruing costs. Identifying the optimal solution to simultaneously optimize availability and costs, whilst meeting service level objectives remains a challenge for professionals developing solutions using cloud services. This paper proposes a mathematical formulation of a bi-objective problem to identify the optimal set of solutions for the system configuration. Empirical evaluation of the proposed approach in a case study of a real industrial scenario results in an R-Squared of 0.85, an MSE of 0.021 and an optimization accuracy of 0.928. These methods can help practitioners to keep services at an optimum configuration enabling autonomic service operation, whilst improving availability and cost.

Microservice architectures have become very popular and widely adopted by the industry, because of the benefits they bring to the software development process and resulting systems, such as parallel development, modularity and scalability. However, as interfaces become more fine-grained and systems grown in size, complexity is moved from the component services to their interactions, eventually leading to intricate workflows that are hard to observe, visualize, and understand. This problem is compounded by the typically high workloads that produce intractable amounts of observation data. To deal with these challenges, operators need support from tools able to take in observation data, in particular tracing, and provide a fast and intuitive understanding of which components or workflows require attention and how are they affecting a module, service, instance, or the whole application. In this paper, we present the design of a microservice visualization application that can fill a gap that exists in leveraging tracing data, aggregating and navigating it in ways that are actionable for operators. Our application provides multiple views of the system and uses spatial and hierarchical navigation using flip zoom to simplify their exploration, while preserving context. Our application can provide a better understanding of the system than existing applications that lack navigability and do not preserve context when switching between different services, layers or views.

Microservice-based architectures feature function-ally independent, well-defined and fine-grained components suit-able for loosely coupled deployments and for building reli-able cloud-native applications. Despite the advantages of this approach, component interactions introduce complexity, thus turning boundary -spanning service operation into a daunting challenge. As systems grow in size, complexity can easily outgrow the cognitive capacity of human operators, who are unable to effectively diagnose faulty microservices. We address this problem by proposing a novel framework to diagnose faulty microservices. Through failure injection and an experimental assessment, our layered diagnosis framework using service response analysis, timing constraints, causality and a ranking algorithm from traces, is able to effectively diagnose faulty microservices. Empirical evaluation of the proposed approach, by examining 130 experi-ments in a representative microservice application in the presence of faults, shows that it can achieve approximately 89% specificity and 77% recall.