Miquel A. Senar

Adaptive Scheduling for Master-Worker Applications on the Computational Grid

We address the problem of how many workers should be allocated for executing a distributed application that follows the master-worker paradigm, and how to assign tasks to workers in order to maximize resource efficiency and minimize application execution time. We propose a simple but effective scheduling strategy that dynamically measures the execution times of tasks and uses this information to dynamically adjust the number of workers to achieve a desirable efficiency, minimizing the impact in loss of speedup. The scheduling strategy has been implemented using an extended version of MW, a runtime library that allows quick and easy development of master-worker computations on a computational grid. We report on an initial set of experiments that we have conducted on a Condor pool using our extended version of MW to evaluate the effectiveness of the scheduling strategy.

An asynchronous and iterative load balancing algorithm for discrete load model

Diffusion algorithms are some of the most popular algorithms for dynamic load balancing in which loads move from heavily loaded processors to lightly loaded neighbor processors. To achieve a global load balance in a parallel computer, the algorithm is iterated until the load difference between any two processors is smaller than a specified value. Therefore, one fundamental property to be studied is algorithm convergence. Several analytical works on the convergence of different diffusion load balancing algorithms have been carried out, but they treat loads as non-negative real quantities. In this paper, we describe the Diffusion Algorithm Searching Unbalanced Domains (DASUD) algorithm, which uses loads as non-negative integer values and, unlike existing algorithms, reaches a local balance situation where the maximum load difference between any two processor in the set of neighbor processors for each processor is one load unit. The convergence property of an asynchronous implementation of DASUD using integer loads is proven theoretically.

Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems

Scheduling is an important factor for the efficient execution of computational workflows on grid environments. A large number of static scheduling heuristics has been presented in the literature. These algorithms allocate tasks before job execution starts and assume a precise knowledge of timing information, which may be difficult to obtain in general. To overcome this limitation of static strategies, dynamic scheduling strategies may be needed for a changing environment such as the grid. While they incur runtime overheads, they may better adapt to timing changes during job execution. In this work, we analyse five well-known heuristics (min-min, max-min, sufferage, HEFT and random) when used as static and dynamic scheduling strategies in a grid environment in which computing resources exhibit congruent performance differences. The analysis shows that non-list based heuristics are more sensitive than list-based heuristics to inaccuracies in timing information. Static list-based heuristics perform well in the presence of low or moderate inaccuracies. Dynamic versions of these heuristics may be needed only in environments where high inaccuracies are observed. Our analysis also shows that list-based heuristics significantly outperform non-list based heuristics in all cases and, therefore, constitute the most suitable strategies by which to schedule workflows either statically or dynamically

First Prototype of the CrossGrid Testbed

The CrossGrid project is developing new grid middleware components, tools and applications with a special focus on parallel and interactive computing. In order to support the development effort and provide a test infrastructure, an international grid testbed has been deployed across 9 countries. Through the deployment of the testbed and its supporting services, CrossGrid is also contributing to another important project objective, the expansion of the grid coverage in Europe. This paper describes the status of the CrossGrid testbed.

Performance comparison of dynamic load-balancing strategies for distributed computing

The DASUD (Diffusion Algorithm Searching Unbalanced Domains) algorithm belongs to the nearest-neighbours class and operates in a diffusion scheme where a processor balances its load with all its neighbours. DASUD detects unbalanced domains and performs local exchange of load between processors to achieve global balancing. The DASUD algorithm has been evaluated by comparison with another well-known strategy, namely, the SID (Sender Initiated Diffusion) algorithm across a range of network topologies including ring, torus and hypercube where the number of processors varies from 8 to 128. From the experiments we have observed that DASUD outperforms the other strategy as it provides the best trade-of-between the balance degree obtained at the final state and the number of iterations required to reach such a state. DASUD is able to coerce any initial load distribution into a highly balanced global state and also exhibits good scalability properties.

Resource Management for Interactive Jobs in a Grid Environment

Most recent grid middleware technologies have been aimed at the execution of sequential batch jobs. However, some users require interactive access when running jobs on grid sites. Execution of these applications on a grid environment is a challenging problem that requires the cooperation of several middleware tools and services. Additional problems arise when this interactive support is intended for parallel applications, which may run remotely across several sites. We provide transparent and reliable support for such applications. Our solution, based on the notion of split execution and interposition agents, allows running applications on a remote machine while some I/O operations are sent back to a home machine. The paper describes how we have applied interposition agents transparently to interactive applications and also describes a simple multiprogramming mechanism that is used to start interactive applications as fast as possible even under heavy occupancy of resources. We provide a performance evaluation of the key elements involved in the execution of interactive jobs

The Convergence of Realistic Distributed Load-Balancing Algorithms

We give a general model of partially asynchronous, distributed load-balancing algorithms for the discrete load model in parallel computers, where the processor loads are treated as non-negative integers. We prove that all load-balancing algorithms in this model are finite. This means that all load-balancing algorithms based on this model are guaranteed to reach a stable situation at a certain time (which depends on the particular algorithm) at which no load will be sent from one processor to another. With an additional assumption, we prove that the largest load difference between any two processors, in the final stable situation of the load-balancing algorithms in this model, is upper-bounded by the diameter of the topology.

Evaluation of an Adaptive Scheduling Strategy for Master-Worker Applications on Clusters of Workstations

We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both the resource efficiency and the application performance. We propose a simple scheduling strategy that dynamically measures application execution time and uses these measurements to automatically adjust the number of allocated processors to achieve the desirable efficiency, minimizing the impact in loss of speedup. The effectiveness of the proposed strategy has been assessed by means of simulation experiments in which several scheduling policies were compared. We have observed that our strategy obtains similar results to other strategies that use a priori information about the application, and we have derived a set of empirical rules that can be used to dynamically adjust the number of processors allocated to the application.

The EU-CrossGrid Approach for Grid Application Scheduling

This paper presents the approach being followed to implement scheduling components that are integrated as part of the EU CrossGrid project. The purpose of these components is to provide a basis for supporting the efficient execution of distributed interactive applications on Grid environments. When a user submits a job, the scheduling services search for the most suitable resources to run the application and take subsequent steps to ensure a reliable launching of the application. All these actions are carried out according to user-defined preferences.

A new model for static mapping of parallel applications with task and data parallelism

The efficient mapping of parallel tasks is essential in order to exploit the gain from parallelisation. In this work, we focus on modelling and mapping message-passing applications that are defined by the programmer with an arbitrary interaction pattern among tasks. A new model is proposed, known as TTIG (Temporal Task Interaction Graph), which captures not only computation and communication costs, but also the percentages of concurrency between tasks. From this model, a mapping strategy is developed that minimises expected execution time by properly exploiting task parallelism. The effectiveness of this approach has been proven for a real image-processing application on a cluster of PCs.