Hamoun Ghanbari

Exploring Alternative Approaches to Implement an Elasticity Policy

An elasticity policy governs how and when resources (e.g., application server instances at the PaaS layer) are added to and/or removed from a cloud environment. The elasticity policy can be implemented as a conventional control loop or as a set of heuristic rules. In the control-theoretic approach, complex constructs such as tracking filters, estimators, regulators, and controllers are utilized. In the heuristic, rule-based approach, various alerts(e.g., events) are defined on instance metrics (e.g., CPU utilization), which are then aggregated at a global scale in order to make provisioning decisions for a given application tier. This work provides an overview of our experiences designing and working with both approaches to construct an auto scaler for simple applications. We enumerate different criteria such as design complexity, ease of comprehension, and maintenance upon which we form an informal comparison between the different methods. We conclude with a brief discussion of how these approaches can be used in the governance of resources to better meet a high-level goal over time.

Optimal autoscaling in a IaaS cloud

An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs). The mechanism of adding and removing resources at runtime is referred to as autoscaling. The process of autoscaling is automated through the use of a management component referred to as an autoscaler. This paper introduces a novel autoscaling approach in which both cloud and application dynamics are modeled in the context of a stochastic, model predictive control problem. The approach exploits trade-off between satisfying performance related objectives for the consumers application while minimizing their cost. Simulation results are presented demonstrating the efficacy of this new approach.

Autonomic load-testing framework

In this paper, we present a method for performance testing of transactional systems. The methods models the system under test, finds the software and hardware bottlenecks and generate the workloads that saturate them. The framework is autonomic, the model and workloads are determined during the performance test execution by measuring the system performance, fitting a performance model and by analytically computing the number and mix of users that will saturate the bottlenecks. We model the software system using a two-layer queuing model and use analytical techniques to find the workload mixes that change the bottlenecks in the system. Those workload mixes become stress vectors and initial starting points for the stress test cases. The rest of test cases are generated based on a feedback loop that drives the software system towards the worst case behaviour.

Feedback-based optimization of a private cloud

The optimization problem addressed by this paper involves the allocation of resources in a private cloud such that cost to the provider is minimized (through the maximization of resource sharing) while attempting to meet all client application requirements (as specified in the SLAs). At the heart of any optimization based resource allocation algorithm, there are two models: one that relates the application level quality of service to the given set of resources and one that maps a given service level and resource consumption to profit metrics. In this paper we investigate the optimization loop in which each applications performance model is dynamically updated at runtime to adapt to the changes in the system. These changes could be perturbations in the environment that had not been included in the model. Through experimentation we show that using these tracking models in the optimization loop will result in a more accurate optimization and thus result in the generation of greater profit.

Tracking adaptive performance models using dynamic clustering of user classes

Estimation techniques have been largely applied to track hidden performance parameters (e.g. service demands) of web based software systems. In this paper we investigate dynamic multiclass modeling of such systems, with variable classes of service, aiming at finding a low complexity model yet with enough accuracy. We propose a combination of clustering algorithm and tracking filter for effective grouping of classes of services. The tracking estimator is based on a layered queuing model with parameters for CPU demands and the user load intensity of each class of service. Clustering uses the K-means algorithm. The target application is autonomic control of web clusters, where changes occur at different rates and amplitudes and at random time instants. Experiments show that the tracking is effective, and reveal good filter settings for different variations.

Model-based performance testing (NIER track)

In this paper, we present a method for performance testing of transactional systems. The methods models the system under test, finds the software and hardware bottlenecks and generate the workloads that saturate them. The framework is adaptive, the model and workloads are determined during the performance test execution by measuring the system performance, fitting a performance model and by analytically computing the number and mix of users that will saturate the bottlenecks. We model the software system using a two layers queuing model and use analytical techniques to find the workload mixes that change the bottlenecks in the system. Those workload mixes become stress vectors and initial starting points for the stress test cases. The rest of test cases are generated based on a feedback loop that drives the software system towards the worst case behaviour.

Hogna: a platform for self-adaptive applications in cloud environments

We propose Hogna, a platform for deploying self-managing web applications on cloud. The platform enables the deployment of the applications based on the automation of a set of operations (starting instances, installing necessary software and configuring the instances, etc.), and then the continuous monitoring of the health of the applications. The gathered monitoring data is analyzed using a performance model and an action plan is created and executed. Any components involved (for monitoring, analyzing, planning and deployment changes) can be customized to fit the needs of the application and/or researcher.

Designing Adaptive Applications Deployed on Cloud Environments

Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In a cloud environment, a designer has to consider and evaluate different control points, that is, those variables that affect the quality of the software system. This article presents a methodology for designing adaptive systems in cloud environments. The proposed methodology consists of several phases that take high-level stakeholders’ adaptation goals and transform them into lower-level MAPE-K loop control points. The MAPE-K loops are then activated at runtime using search-based algorithms. Our methodology includes the elicitation, ranking, and evaluation of control points, all meant to enable a runtime search-based adaptation. We conducted several experiments to evaluate the different phases of our methodology and to validate the runtime adaptation efficiency.

Identifying implicitly declared self-tuning behavior through dynamic analysis

Autonomic computing programming models explicitly address self management properties by introducing the notion of “Autonomic Element. However, most of currently developed systems do not employ autonomic self-managing programming paradigms. Thus, a current challenge is to find mechanisms to identify the self-tuning behavior and self-tuning parameters which have implicitly been declared using non-autonomic elements, and to expose them for monitoring or to an analysis framework. Static analysis, although it shows a good potential, it results in many false positives. In this paper, we provide a mechanism to identify the tuning parameters more accurately through dynamic analysis.

A Data Platform for the Highway Traffic Data

Both short and long term information of the transportation network is needed by commuters and planners. In order to obtain this information, there is a pressing need to consolidate, mine and analyze data collected from multiple sources. To enable these activities under a single umbrella, we propose a data platform in this position paper that transforms data into information. Finally, we discuss the research challenges facing our platform.