Demetris Trihinas

JCatascopia: Monitoring Elastically Adaptive Applications in the Cloud

Over the past decade, Cloud Computing has rapidly become a widely accepted paradigm with core concepts such as elasticity, scalability and on demand automatic resource provisioning emerging as next generation Cloud service-must have-properties. Automatic resource provisioning for Cloud applications is not a trivial task, requiring for both the applications and platform, to be constantly monitored, capturing information at various levels and time granularity. In this paper we describe the challenges that occur when monitoring elastically adaptive Cloud applications and to address these issues we present JCatascopia, a fully automated, multi-layer, interoperable Cloud Monitoring System. Experiments on different production Cloud platforms show that JCatascopia is a Monitoring System capable of supporting a fully automated Cloud resource provisioning system with proven interoperability, scalability and low runtime footprint. Most importantly, JCatascopia is able to adapt in a fully automatic manner when elasticity actions are enforced to an application deployment.

ADVISE – A Framework for Evaluating Cloud Service Elasticity Behavior

Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions.

AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices

Real-time data processing while the velocity and volume of data generated keep increasing, as well as, energy-efficiency are great challenges of big data streaming which have transitioned to the Internet of Things (IoT) realm. In this paper, we introduce AdaM, a lightweight adaptive monitoring framework for smart battery-powered IoT devices with limited processing capabilities. AdaM, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the metric stream. Results on real-world testbeds, show that AdaM achieves a balance between efficiency and accuracy. Specifically, AdaM is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy.

Managing and Monitoring Elastic Cloud Applications

Next generation Cloud applications present elastic features and rapidly scale their comprised resources. Consequently, managing and monitoring Cloud applications is becoming a challenge. This paper showcases the functionality and novel features of: (i) c-Eclipse, a framework for describing Cloud applications along with their elasticity requirements and deploying them on any IaaS provider; and (ii) JCatascopia, a fully-automated, multi-layer, interoperable Cloud monitoring system. Particularly, we demonstrate how a user can manage the full lifecycle of a three-tier web application and observe, in real-time, how an elasticity management platform automatically scales the application based on various user-defined elasticity requirements, workloads and performance metrics.

Enabling Interoperable Cloud Application Management through an Open Source Ecosystem

Cloud computing enables on-demand provisioning of computing resources to IT solutions, following a utility-based approach. Technology and standardization developments in traditional public utilities enable customers to seamlessly migrate across utility providers without being required to make changes to their home appliances. Dubbed as the fifth utility, cloud computing customers should have user-friendly tools and mechanisms at their disposal, which preserve application deployments across different resource providers. Here, the authors present current efforts to develop an open source Cloud Application Management Framework (CAMF) based on the Eclipse Rich Client Platform. This framework facilitates cloud application lifecycle management in a vendor-neutral approach.

c-Eclipse: An Open-Source Management Framework for Cloud Applications

Cloud application portability and optimal resource allocation are of great importance in the realm of Cloud infrastructure provisioning. c-Eclipse is an open-source Cloud Application Management Framework through which users are able to define the description, deployment and management phases of their Cloud applications in a clean and intuitive graphical manner. It is built on top of the well-established Eclipse platform and it adheres to two highly desirable features of Cloud applications: portability and elasticity. In particular, c-Eclipse implements the open, non-proprietary OASIS TOSCA specification for describing the provision, deployment and re-contextualization of applications across different Cloud infrastructures, thereby ensuring application portability. Furthermore, c-Eclipse enables Cloud users to specify elasticity policies that describe how the deployed virtualized resources must be elastically adapted at runtime to match the needs of a dynamic application-workload. In this paper, we introduce the architecture and implementation of c-Eclipse, and describe its key characteristics via a use-case scenario that involves a user creating a description of a 3-tier Cloud application, enriching it with appropriate elasticity policies, submitting it for deployment to two different Cloud providers and, finally, monitoring its execution.

Monitoring Elastically Adaptive Multi-Cloud Services

Automatic resource provisioning is a challenging and complex task. It requires for applications, services and underlying platforms to be continuously monitored at multiple levels and time intervals. The complex nature of this task lays in the ability of the monitoring system to automatically detect runtime configurations in a cloud service due to elasticity action enforcement. Moreover, with the adoption of open cloud standards and library stacks, cloud consumers are now able to migrate their applications or even distribute them across multiple cloud domains. However, current cloud monitoring tools are either bounded to specific cloud platforms or limit their portability to provide elasticity support. In this article, we describe the challenges when monitoring elastically adaptive multi-cloud services. We then introduce a novel automated, modular, multi-layer and portable cloud monitoring framework. Experiments on multiple clouds and real-life applications show that our framework is capable of automatically adapting when elasticity actions are enforced to either the cloud service or to the monitoring topology. Furthermore, it is recoverable from faults introduced in the monitoring configuration with proven scalability and low runtime footprint. Most importantly, our framework is able to reduce network traffic by 41 percent, and consequently the monitoring cost, which is both billable and noticeable in large-scale multi-cloud services.

Evaluating Cloud Service Elasticity Behavior

To optimize the cost and performance of complex cloud services under dynamic requirements, workflows and diverse cloud offerings, we rely on different elasticity control processes. An elasticity control process, when being enforced, produces effects in different parts of the cloud service. These effects normally evolve in time and depend on workload characteristics, and on the actions within the elasticity control process enforced. Therefore, understanding the effects on the behavior of the cloud service is of utter importance for runtime decision-making process, when controlling cloud service elasticity. In this paper, we present a novel methodology and a framework for estimating and evaluating cloud service elasticity behaviors. To estimate the elasticity behavior, we collect information concerning service structure, deployment, service runtime, control processes, and cloud infrastructure. Based on this information, we utilize clustering techniques to identify cloud service elasticity behavior, in time, and for different parts of the service. Knowledge about such behavior is utilized within a cloud service elasticity controller to substantially improve the selection and execution of elasticity control processes. These elasticity behavior estimations are successfully being used by our elasticity controller, in order to improve runtime decision quality. We evaluate our framework with three real-world cloud services in different application domains. Experiments show that we are able to estimate the behavior in 89.5% of the cases. Moreover, we have observed improvements in our elasticity controller, which takes better control decisions, and does not exhibit control oscillations.

ADMin: Adaptive monitoring dissemination for the Internet of Things

As more knowledge is vastly added to the devices fuelling the Internet of Things (IoT) energy efficiency and real-time data processing are great challenges that must be tackled. In this paper, we introduce ADMin, a low-cost IoT framework that reduces on device energy consumption and the volume of data disseminated across the network. This is achieved by efficiently adapting the rate at which IoT devices disseminate monitoring streams based on run-time knowledge of the stream evolution, variability and seasonal behavior. Rather than transmitting the entire stream, ADMin favors sending updates for its estimation model from which values can be inferred, triggering dissemination only when shifts in the stream evolution are detected. Results on real-life testbeds, show that ADMin is able to reduce energy consumption by at least 83%, data volume by 71%, shift detection delays by 61% while maintaining accuracy above 91% in comparison to other IoT frameworks.

Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe

Cloud computing offers the opportunity to improve efficiency with cloud providers offering consumers the ability to automatically scale their applications to meet exact demands. However, “auto-scaling” is usually provided to consumers in the form of metric threshold rules which are not capable of determining whether a scaling alert is issued due to an actual change in the demand of the application or due to short-lived bursts evident in monitoring data. The latter, can lead to unjustified scaling actions and thus, significant costs. In this paper, we introduce AdaFrame, a novel library which supports the decision-making of rule-based elasticity controllers to timely detect actual runtime changes in the monitorable load of cloud services. Results on real-life testbeds deployed on AWS, show that AdaFrame is able to correctly identify scaling actions and in contrast to the AWS auto-scaler, is able to lower detection delay by at least 63%.