Issam Al-Azzoni

Dynamic scheduling for heterogeneous Desktop Grids

Desktop grids have emerged as an important methodology to harness the idle cycles of millions of participant desktop PCs over the Internet. However, to effectively utilize the resources of a desktop grid, it is necessary to use scheduling policies suitable for such systems. A scheduling policy must be applicable to large-scale systems involving large numbers of machines. Also, the policy must be fault-aware in the sense that it copes with resource volatility. Further adding to the complexity of scheduling for desktop grids is the inherent heterogeneity of such systems. Sub-optimal performance would result if the scheduling policy does not take into account information on heterogeneity. In this paper, we suggest and develop several scheduling policies for desktop grid systems involving different levels of heterogeneity. In particular, we propose a policy which utilizes the solution to a linear programming problem which maximizes system capacity. We consider parallel applications that consist of independent tasks.

Linear Programming-Based Affinity Scheduling of Independent Tasks on Heterogeneous Computing Systems

Resource management systems (RMS) are an important component in heterogeneous computing (HC) systems. One of the jobs of an RMS is the mapping of arriving tasks onto the machines of the HC system. Many different mapping heuristics have been proposed in recent years. However, most of these heuristics suffer from several limitations. One of these limitations is the performance degradation that results from using outdated global information about the status of all machines in the HC system. This paper proposes several heuristics which address this limitation by only requiring partial information in making the mapping decisions. These heuristics utilize the solution to a linear programming (LP) problem which maximizes the system capacity. Simulation results show that our heuristics perform very competitively while requiring dramatically less information.

Power-aware linear programming based scheduling for heterogeneous computer clusters

Several power-aware scheduling policies have been proposed for homogeneous clusters. In this work, we propose a new policy for heterogeneous clusters. Our simulation experiments show that using our proposed policy results in significant reduction in energy consumption while performing very competitively in heterogeneous clusters.

MARO - MinDrift affinity routing for resource management in heterogeneous computing systems

This paper deals with designing effective resource management strategies for systems of heterogeneous computers. Each computer is represented as an abstract server, capable of serving different task demands at different rates. We consider a system with I types of independent Poisson task demand arrival streams and J parallel servers with independent non-identical processing time distributions for each arrival type. The decision of routing each type i task immediately upon arrival to a server j is made by comparing the state information of a subset of the J servers. We show that choosing the subset according to a linear programming (LP) problem which maximizes the system capacity can not only significantly reduce the amount of state information required in making the routing decision, but also yield shorter total mean queue length (and hence mean time in system) compared with the policies requiring global state information. In addition, we explore means of limiting flexibility to further reduce the required state information.

Power-Aware Linear Programming based Scheduling for heterogeneous computer clusters

Several power-aware scheduling policies have been proposed for homogeneous clusters. In this work, we propose a new policy for heterogeneous clusters. Our simulation experiments show that using our proposed policy results in significant reduction in energy consumption while performing very competitively in heterogeneous clusters.

MGST: A framework for performance evaluation of Desktop Grids

Desktop Grids are rapidly gaining popularity as a costeffective computing platform for the execution of applications with extensive computing needs. As opposed to grids and clusters, these systems are characterized by having a non-dedicated infrastructure. These unique characteristics need to be considered in developing resource management strategies for Desktop Grids. Several frameworks for the performance evaluation of resource management strategies have been suggested for grids. However, similar projects for Desktop Grids are still lacking. This paper presents MGST, the first performance testing framework for Desktop Grids. We discuss the design of the tool and show how it can be used to analyze and improve the performance of an existing Desktop Grid scheduling policy.

Cost-Aware Performance Modeling of Multi-tier Web Applications in the Cloud

Typical web applications employ a multi-tier architecture. Traditionally, a pool of physical servers is used to host web applications. To handle the dynamic workloads which characterize today’s web applications, several authors have proposed schemes for dynamic resource provisioning. Such schemes add more servers during peak loads and remove servers during other times. Advances in cloud computing technologies have created new perspectives for real-time dynamic provisioning. The elastic nature of cloud computing systems allows system administrators to quickly scale resources to respond to unexpected load changes. In such systems, dynamic provisioning is not only concerned with meeting Service Level agreements, but also must take into account monetary costs. In this paper, we exploit performance modeling in the context of cloud computing (Amazon EC2). Having such performance models enables understanding the trade-off between performance and cost, a cornerstone in developing dynamic provisioning performance management schemes.

Performance evaluation for software migration

Advances in technology and economical pressure have forced many organizations to consider the migration of their legacy systems to newer platforms. Legacy systems typically provide mission critical services vital for an organizations business needs. These systems are usually very large and highly complex with little or no documentation. Furthermore, fewer people can understand and maintain these systems. While several techniques exist to verify the functionality of the migrated system, the literature is still lacking methods to effectively assess the performance impact of software migration. In this paper, we propose a new method designed specifically to address performance evaluation in software migration projects. The new method uses simple models and incorporates techniques for model validation and resource demand mapping for performance evaluation and capacity planning.

Abstract only: performance evaluation for software migration

Advances in technology and economical pressure have forced many organizations to consider the migration of their legacy systems to newer platforms. Legacy systems typically provide mission critical services vital for an organizations business needs. These systems are usually very large and highly complex with little or no documentation. Furthermore, fewer people can understand and maintain these systems. While several techniques exist to verify the functionality of the migrated system, the literature is still lacking methods to effectively assess the performance impact of software migration. In this paper, we propose a new method designed specifically to address performance evaluation in software migration projects. The new method uses simple models and incorporates techniques for model validation and resource demand mapping for performance evaluation and capacity planning.

ATL Transformation of Queueing Networks to Queueing Petri Nets.

This paper presents an approach for model transformation from Queueing Network Models (QNMs) into Queueing Petri Nets (QPNs). This would open up the benefits of QPNs in analyzing the performance of QNMs. We present metamodels for QNMs and QPNs, and then present the transformation rules in the ATL model transformation language. To validate our approach, we apply it to analyze the performance of a QNM and compare the results with those obtained using analytic methods. Although the approach is presented using ATL and Ecore meta modeling language in the context of the Eclipse Modeling Project, it can be realized using other modeling frameworks and languages.