Omar Abdul-Rahman

Towards Understanding the Usage Behavior of Google Cloud Users: The Mice and Elephants Phenomenon

In the era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. In an attempt to understand user behavior in constructing applications and interacting with typical cloud infrastructures, we analyzed a large utilization dataset of Google cluster. In the present paper, we consider user behavior in composing applications from the perspective of topology, maximum requested computational resources, and workload type. We model user dynamic behavior around the users session view. Mass-Count disparity metrics are used to investigate the characteristics of underlying statistical models and to characterize users into distinct groups according to their composition and behavioral classes and patterns. The present study reveals interesting insight into the heterogeneous structure of the Google cloud workload.

Evaluation on the Performance Fluctuation of Hadoop Jobs in the Cloud

Cloud computing is widely used as a computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application execution time before running the application in order to guarantee the application performance or choose a suitable cloud for the user application. However, the application performance in the cloud sometime fluctuates, which makes the estimation of the application performance difficult. In this paper, we present the experimental results obtained in investigating the fluctuation of the application execution time in the cloud. We investigated the performance fluctuation of Hadoop jobs in both a public cloud and a private cloud over three months. The obtained experimental results indicate that the fluctuation is smaller than expected, except for some cases for irregular jobs with significantly long execution times. Moreover, the application performance is independent of the time of day and the day of the week. Finally, we also discuss the reasons for the existence of these irregular jobs through a detailed analysis of job execution traces.

An adaptive resolution hybrid binary-real coded genetic algorithm

In genetic algorithms (GAs), is it better to use binary encoding schemes or floating point encoding schemes? In this article, we try to tackle this controversial question by proposing a novel algorithm that divides the computational power between two cooperative versions of GAs. These are a binary-coded GA (bGA) and a real-coded GA (rGA). The evolutionary search is primarily led by the bGA, which identifies promising regions in the search space, while the rGA increases the quality of the solutions obtained by conducting an exhaustive search throughout these regions. The resolution factor (R), which has a value that is increasingly adapted during the search, controls the interactions between the two versions. We conducted comparison experiments employing a typical benchmark function to prove the feasibility of the algorithm under the critical scenarios of increasing problem dimensions and decreasing precision power.

Multi-Level Autonomic Architecture for the Management of Virtualized Application Environments in Cloud Platforms

resource management in cloud platforms becomes an increasingly complex and daunting task surrounded by various challenges of stringent QoS requirements, service availability guaranteeing and escalating overhead of the infrastructure that resulted from operation costs and ecological effects. Virtualization adds a greater flexibility to the resource manager in addressing such challenges. However, it imposes a further challenge of added management complexity. So, in this brief paper, we attempt to address still an open question of how to employ virtualization techniques effectively to realize a resource manager that intelligently adapts cloud platforms resource usage to satisfy the conflicting objectives of running applications and underlying cloud infrastructures by proposing a novel multi-level architecture which relays on a hybrid virtualization framework. We describe its functional components and dataflow and highlight the next steps that we will adopt in order to realize it and evaluate its feasibility and effectiveness.

Performance of Hadoop Application on Hybrid Cloud

Hadoop is an open-source software framework for distributed computing that is widely used to develop large-scale data processing applications, such as big data applications. Hadoop application programs are normally run on in-house or cloud computing platforms. Recently, a hybrid cloud composed of in-house and remote cloud computing platforms has been found to be capable of sustaining a certain level of application performance. In this paper, we discuss the performance of a Hadoop application program running on such hybrid clouds. We will begin by presenting the performance model used to estimate the execution time of a Hadoop application program running on a hybrid cloud. Then, we will show the results of experiments conducted on hybrid cloud test beds. These experimental results revealed that the performance levels of the Hadoop application programs running on the hybrid cloud were application type dependent, and that performance improvements could be expected by using a remote cloud computing platform in conjunction with in-house computing platforms for certain types of applications. Furthermore, the results showed that our performance model captured the performance trend of the application programs on the hybrid cloud. However, room for improvement still exists in the performance model, particularly for the shuffle phase.

Multi-layered Architecture for the Management of Virtualized Application Environments within Inter-cloud Platforms

Resource allocation is an active direction of research that is drawing interest within academic and technological circles. Resource allocation imposes numerous challenges. This is especially true for Inter-Clouds, a recent paradigm for horizontal expansion and integration of disparate and heterogeneous cloud platforms. In an attempt to realize an efficient resource management system, this work-in-progress paper proposes and describes a new multi-layered management framework to address the tasks of virtualized resource control, dynamic resource provisioning, life-cycle management and resource exchange within Inter-Cloud environments.

Google Users as Sequences:A Robust Hierarchical Cluster Analysis Study

In this era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. By understanding user behavior in constructing applications and interacting with typical cloud infrastructures, cloud managers can develop better systems that improve the users’ experience. In this paper, we analyze a large dataset of a Google cluster to characterize the users into distinct groups of similar usage behavior. We used a wide range of measured metrics to model user behavior in composing applications from the perspective of actions around application architecting, capacity planning, and workload type planning and to model user interaction behavior around the session view. The trajectories of users’ actions are represented as sequences using categorical and proportional encoding schemes. We used techniques from the sequence analysis paradigm to quantify dissimilarity among users. We employed a robust cluster analysis procedure based on the agglomerative hierarchical methods to optimally classify users into 12 classes. We used a variety of formal indices and visual aids to confirm the quality and stability of the outcomes. By visual inspection, we regrouped the obtained clusters into 5 main groups that reveal interesting insights about the characteristics which underline different groups’ utilization behavior.