Service-based Cloud Applications Research Articles

Dynamic Application Call Graph Formation and Service Identification in Cloud Data Centers

Monitoring distributed service-based cloud applications and understanding the interactions among the different components are crucial to diagnose and resolve performance issues. However, many existing cloud monitoring systems require sophisticated application and/or platform instrumentation and cannot be deployed on-demand, or they provide only partial functionality. In most cases, monitoring comes with a significant overhead. To overcome these shortcomings, this paper presents DyMonD, a holistic framework that Dynamically Monitors an application, Discovers the service components, and visualizes them together with some performance metrics such as throughput in the form of a call graph. DyMonD is completely decoupled from the internals of the applications and the services themselves, as it deploys monitoring agents transparently at the software switches within the network, extracts all necessary information from the messages exchanged by the components, and performs service identification by using deep learning on the network flows. Our evaluation shows that DyMonD has significantly less overhead than existing tools, reducing the monitoring-induced impact on response time by up to 89%, and reducing resource consumption such as CPU and memory usage by up to 75%. Furthermore, DyMond’s deep learning module identifies services up to 11% more accurately than competing models.

Dynamic resource provisioning for service-based cloud applications: A Bayesian learning approach

Deciding the correct extent of resources needed to run the various cloud services is always a challenge. Often in such dynamic environments, there is a tremendous need for accurate predictions and timely decision making methodologies to estimate the future demands within a minimal cost. This brings in a need to elucidate the research divergence for optimal dynamic resource provisioning that predicts the future enumerated resources on the support of application's type. This paper proposes a framework to provision the resources in an optimal way, by combining the concepts of autonomic computing, linear regression and Bayesian learning. The use of Bayesian learning to the proposed model helps in a proactive decision making process and provide a solid theoretical framework to estimate the future predictions using the prior information available. The autonomic resource provisioning framework proposed here is developed using CloudSim toolkit inspired by a cloud layer model. The efficacy of the proposed technique is evaluated using real world workload traces from google followed by the traces from Clarknet. The model is evaluated for various parameters namely – response time, SLA violations, virtual machine usage hours and cost. It is found that the proposed model lowers the overall cost by 31% with the increase in the usage of resources by 12% when compared with the other existing approaches.

Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey

The proliferation of services and service interactions within microservices and cloud-native applications, makes it harder to detect failures and to identify their possible root causes, which is, on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.

Dynamic Resource Provisioning for Service-Based Cloud Applications: A Bayesian Learning Approach

Dynamic Resource Provisioning for Service-Based Cloud Applications: A Bayesian Learning Approach

An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach

In cloud computing environment, resources can be dynamically provisioned on deman for cloud services The amount of the resources to be provisioned is determined during runtime according to the workload changes. Deciding the right amount of resources required to run the cloud services is not trivial, and it depends on the current workload of the cloud services. Therefore, it is necessary to predict the future demands to automatically provision resources in order to deal with fluctuating demands of the cloud services. In this paper, we propose a hybrid resource provisioning approach for cloud services that is based on a combination of the concept of the autonomic computing and the reinforcement learning (RL). Also, we present a framework for autonomic resource provisioning which is inspired by the cloud layer model. Finally, we evaluate the effectiveness of our approach under two real world workload traces. The experimental results show that the proposed approach reduces the total cost by up to 50%, and increases the resource utilization by up to 12% compared with the other approaches.

Rapid Development of Service-based Cloud Applications

Cloud application platforms gain popularity and have the potential to alter the way service-based cloud applications are developed involving utilisation of platform basic services. A platform basic service provides certain functionality and is usually offered via a web API. However, the diversification of the services and the available providers increase the challenge for the application developers to integrate them and deal with the heterogeneous providers' web APIs. Therefore, a new approach of developing applications should be adopted in which developers leverage multiple platform basic services independently from the target application platforms. To this end, the authors present a development framework assisting the design of service-based cloud applications. The objective of the framework is to enable the consistent integration of the services, and to allow the seamless use of the concrete providers. The optimal service provider each time can vary depending on criteria such as pricing, quality of service and can be determined based upon Big Data analysis approaches.