Rizos Sakellariou

A taxonomy of grid monitoring systems

Monitoring is the act of collecting information concerning the characteristics and status of resources of interest. Monitoring grid resources is a lively research area given the challenges and manifold applications. The aim of this paper is to advance the understanding of grid monitoring by introducing the involved concepts, requirements, phases, and related standardisation activities, including Global Grid Forums Grid Monitoring Architecture. Based on a refinement of the latter, the paper proposes a taxonomy of grid monitoring systems, which is employed to classify a wide range of projects and frameworks. The value of the offered taxonomy lies in that it captures a given systems scope, scalability, generality and flexibility. The paper concludes with, among others, a discussion of the considered systems, as well as directions for future research.

A hybrid heuristic for DAG scheduling on heterogeneous systems

Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations in the generated schedule. To minimize such variations, we present a novel heuristic for DAG scheduling, which is based upon solving a series of independent task scheduling problems. A novel heuristic for the latter problem is also included. Both heuristics compare favourably with other related heuristics.

Scheduling workflows with budget constraints

Grids are emerging as a promising solution for resource and computation demanding applications. However, the heterogeneity of resources in Grid computing, complicates resource management and scheduling of applications. In addition, the commercialization of the Grid requires policies that can take into account user requirements, and budget considerations in particular. This paper considers a basic model for workflow applications modelled as Directed Acyclic Graphs (DAGs) and investigates heuristics that allow to schedule the nodes of the DAG (or tasks of a workflow) onto resources in a way that satisfies a budget constraint and is still optimized for overall time. Two different approaches are implemented, evaluated and presented using four different types of basic DAGs.

Euro-Par 2001 Parallel Processing

A software component framework is one where an application designer programs by composing well understood and tested “components” rather than writing large volumes of not-very-reusable code. The software industry has been using component technology to build desktop applications for about ten years now. More recently this idea has been extended to application in distributed systems with frameworks like the Corba Component Model and Enterprise Java Beans. With the advent of Grid computing, high performance applications may be distributed over a wide area network of compute and data servers. Also “peerto-peer” applications exploit vast amounts of parallelism exploiting the resources of thousands of servers. In this talk we look at the problem of building a component technology for scientific applications. The common component architecture project seeks to build a framework that allows software components runing on a massively parallel computers to be linked together to form wide-area, high performance application services that may be accessed from desktop applications. This problem is far from being solved and the talk will describe progress to date and outline some of the difficult problems that remain to be solved. R. Sakellariou et al. (Eds.): Euro-Par 2001, LNCS 2150, pp. 5–5, 2001. c

Scheduling multiple DAGs onto heterogeneous systems

The problem of scheduling a single DAG onto heterogeneous systems has been studied extensively. In this paper, we focus on the problem of scheduling more than one DAG at the same time onto a set of heterogeneous resources. The aim is not only to optimize the overall makespan, but also to achieve fairness, defined on the basis of the slowdown that each DAG would experience as a result of competing for resources with other DAGs. Two policies particularly focussing to deliver fairness are presented and evaluated along with another four policies that can be used to schedule multiple DAGs.

Distributed Query Processing on the Grid

Distributed query processing (DQP) has been widely used in data intensive applications where data of relevance to users is stored in multiple locations. This paper argues: (i) that DQP can be important in the Grid, as a means of providing high-level, declarative languages for integrating data access and analysis; and (ii) that the Grid provides resource management facilities that are useful to developers of DQP systems. As well as discussing and illustrating how DQP technologies can be deployed within the Grid, the paper describes a prototype implementation of a DQP system running over Globus.

A low-cost rescheduling policy for efficient mapping of workflows on grid systems

Workflow management is emerging as an important service in Grid computing. A simple model that can be used for the representation of certain workflows is a directed acyclic graph. Although many heuristics have been proposed to schedule such graphs on heterogeneous environments, most of them assume accurate prediction of computation and communication costs. This limits their direct applicability to a dynamically changing environment, such as the Grid. In this environment, an initial schedule may be built based on estimates, but run-time rescheduling may be needed to improve application performance. This paper presents a low-cost rescheduling policy, which considers rescheduling at a few, carefully selected points during the execution. This policy achieves performance results, which are comparable with those achieved by a policy that dynamically attempts to reschedule before the execution of every task.

Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources

In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are data- intensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitational- wave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in disk- space constrained environments, but can also improve the overall workflow performance.

POEMS: end-to-end performance design of large parallel adaptive computational systems

The POEMS project is creating an environment for end-to-end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. Toward this end, the POEMS framework supports composition of component models from these different domains into an end-to-end system model. This composition can be specified using a generalized graph model of a parallel system, together with interface specifications that carry information about component behaviors and evaluation methods. The POEMS Specification Language compiler will generate an end-to-end system model automatically from such a specification. The components of the target system may be modeled using different modeling paradigms and at various levels of detail. Therefore, evaluation of a POEMS end-to-end system model may require a variety of evaluation tools including specialized equation solvers, queuing network solvers, and discrete event simulators. A single application representation based on static and dynamic task graphs serves as a common workload representation for all these modeling approaches. Sophisticated parallelizing compiler techniques allow this representation to be generated automatically for a given parallel program. POEMS includes a library of predefined analytical and simulation component models of the different domains and a knowledge base that describes performance properties of widely used algorithms. The paper provides an overview of the POEMS methodology and illustrates several of its key components. The modeling capabilities are demonstrated by predicting the performance of alternative configurations of Sweep3D, a benchmark for evaluating wavefront application technologies and high-performance, parallel architectures.

An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm

This paper considers the Heterogeneous Earliest Finish Time (HEFT) algorithm for scheduling the tasks of an application, represented by a directed acyclic graph, onto a bounded number of heterogeneous machines. We focus on the appropriate selection of the weight for the nodes and edges of the graph, and experiment with a number of different schemes for computing these weights. Our findings indicate that the length of the schedule produced may be affected significantly by the scheme used, and suggest that the mean value based approach used by HEFT may not be a particularly good choice.