Cristina Boeres

A cluster-based strategy for scheduling task on heterogeneous processors

Efficient task scheduling is fundamental for parallel applications to achieve good performance on distributed systems. While extensive work exists for scheduling tasks on homogeneous processors, fewer algorithms exist for the more common problem of scheduling in heterogeneous processor environments. In this paper, we propose coupling a replication-based clustering heuristic for homogeneous processors, with a mechanism to map the generated clusters to the heterogeneous environment. Experimental results show that this strategy compares favourably in terms of the makespan with traditional list scheduling approaches to this problem, particularly when communication costs are high.

Hybrid task scheduling: integrating static and dynamic heuristics

Researchers are constantly looking for ways to improve the execution time of parallel applications on distributed systems. Although compile-time static scheduling heuristics employ complex mechanisms, the quality of their schedules are handicapped by estimated run-time costs. On the other hand, while dynamic schedulers use actual run-time costs, they have to be of low complexity in order to reduce the scheduling overhead. We investigate the viability of integrating these two approaches into a hybrid scheduling framework. The relationship between static schedulers, dynamic heuristics and scheduling events are examined. The results show that a hybrid scheduler can indeed improve the schedules produced by good traditional static list scheduling algorithms.

EasyGrid: towards a framework for the automatic Grid enabling of legacy MPI applications

One of the goals of the Grid is to aggregate collections of shared, heterogeneous, and distributed resources to provide computational ‘power’ to parallel applications. However, designing applications capable of exploiting this potential with ease remains a challenge. This paper outlines the EasyGrid methodology for the efficient and robust execution of (legacy) MPI programs across distributed computing clusters. The principal objective of this work is to identify the application‐oriented middleware necessary for, as well as to develop a framework to automatically generate, system‐aware applications capable of executing in dynamic, unstable, distributed environments such as computational Grids. Copyright

Distributed and dynamic self-scheduling of parallel MPI Grid applications

The execution of distributed applications on the Grid is already a reality. However, as both the number of applications grow and Grids increase in scale, the efficient utilization of the available but shared heterogeneous resources will become increasingly essential to the Grids successful maturity. Furthermore, it is unclear whether existing Grid management systems are capable of meeting this challenge. The EasyGrid middleware is a hierarchically distributed application management system (AMS) that is embedded into MPI applications to autonomously orchestrate their execution efficiently in computational Grids. The overhead of employing a distinct AMS to make each application system aware brings at least two benefits. First, the adopted policies can be tailored to the specific needs of each application, leading to improved performance. Second, distributing the management effort among executing applications makes Grid management more scalable. This article focuses on scheduling policies of an AMS for a particular class of application, describing a low intrusion implementation of a hybrid scheduling strategy designed to elicit good performance even in dynamic environments such as Grids. Using application‐specific scheduling policies, near‐optimal runtimes highlight the advantages of self‐scheduling when executing one or more system aware applications on a Grid. Copyright

MOS-Based Rate Adaption for VoIP Sources

This paper proposes an algorithm for the adaptive adjustment of the transmission rate of VoIP sources based on the voice quality estimated at the receiver. This adjustment is achieved through the appropriate use of differing voice codecs, as the conditions of the network change, in order to maintain an efficient utilization of the available resources. To validate our proposal realistically, we have made an effort to simulate VoIP calls using sources that follow Bradys model of human conversations. We investigate the effects of the proposed model on the aggregate network traffic and compare the results with existing related work. Simulation results show that the proposed algorithm makes better use of the available bandwidth, achieving superior performance in comparison to similar works.

Managing the execution of large scale MPI applications on computational grids

Computational grids aim to aggregate significant numbers of resources to provide sufficient, but low cost, computational power to an ever growing variety applications. Writing applications capable of executing efficiently in these grid environments is however extremely difficult for inexperienced users. The grids geographically distributed resources are typically heterogeneous, non-dedicated, and are offered without any performance or availability guarantees. This work investigates an alternative approach (based on smarter system-aware applications) to solve the problem of developing and managing the execution of grid applications efficiently. Results show that these system-aware MPI applications are indeed faster than their conventional implementations and easily grid enabled.

A versatile cost modelling approach for multicomputer task scheduling

Abstract In general, scheduling models only consider the message delay or latency as the dominant communication parameter. However, in many of the current generation of parallel systems, latency is negligible compared to the CPU penalties for the communication-related activities that are incurred whenever pairs of dependent tasks on distinct processors need to communicate. This work considers a model where the CPU penalty , which is associated with sending and receiving, communication events , is an additional (potentially dominant) communication parameter. A multi-stage scheduling approach (MSA) is proposed which takes both of these types communication parameters into account. This scheduling approach can be customised to classes of parallel systems according to their communication performance characteristics by varying the order in which the rules (which guide the strategy) are applied.

Dynamic self-scheduling for parallel applications with task dependencies

As grids are in essence heterogeneous, dynamic, shared and distributed environments, managing these kinds of platforms efficiently is extremely complex. Few transparent grid management systems have been developed to cope with these characteristics simultaneously and therefore both new and existing applications must be modified to execute efficiently. A promising scalable approach to deal with these intricacies is the design of self-managing or autonomic applications. Autonomic applications adapt their execution accordingly by considering knowledge about their own behaviour and environmental conditions. This paper focuses on the dynamic scheduling that provides the self-optimizing ability in autonomic applications. Being distributed, collaborative and pro-active, the proposed hierarchical scheduling infrastructure addresses important issues to enable an efficient execution in a computational grid. Unlike other approaches, the cooperative, hybrid and application-specific strategy deals effectively with task dependencies. Several experiments have been analyzed in real grid environments highlighting the efficiency and scalability of the proposed infrastructure. This paper presents an intra-site dynamic scheduling heuristic for tightly coupled parallel applications represented by DAGs.

An EasyGrid portal for scheduling system‐aware applications on computational Grids

One of the objectives of computational Grids is to offer applications the collective computational power of distributed but typically shared heterogeneous resources. Unfortunately, efficiently harnessing the performance potential of such systems (i.e. how and where applications should execute on the Grid) is a challenging endeavor due principally to the very distributed, shared and heterogeneous nature of the resources involved. A crucial step towards solving this problem is the need to identify both an appropriate scheduling model and scheduling algorithm(s). This paper presents a tool to aid the design and evaluation of scheduling policies suitable for efficient execution of system‐aware parallel applications on computational Grids. Copyright

Hybrid evolutionary static scheduling for heterogeneous systems

The complexity of the static scheduling problem on heterogeneous resources has motivated the development of low complexity heuristics such as list scheduling. However, the greedy characteristic of such heuristics can, in many cases, generate poor results. This work proposes the integration of list scheduling heuristics with search mechanisms based on both genetic algorithms and GRASP, to efficiently schedule tasks on distributed systems. The results show that the hybrid approach is robust and can converge quickly to good quality solutions.