Grid applications require allocating a large number of heterogeneous tasks to distributed resources. A good allocation is critical for efficient execution. However, many existing grid toolkits use matchmaking strategies that do not consider overall efficiency for the set of tasks to be run. We identify two families of resource allocation algorithms: task-based algorithms, that greedily allocate tasks to resources, and workflow-based algorithms, that search for an efficient allocation for the entire workflow. We compare the behavior of workflow-based algorithms and task-based algorithms, using simulations of workflows drawn from a real application and with varying ratios of computation cost to data transfer cost. We observe that workflow-based approaches have a potential to work better for data-intensive applications even when estimates about future tasks are inaccurate.

Abstract. Workflow management is emerging as an important service in Grid computing.

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Abstract—Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users ’ QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.

Recent research advocates asymmetric multi-core architectures, where cores in the same processor can have different performance. These architectures support single-threaded performance and multithreaded throughput at lower costs (e.g., die size and power). However, they also pose unique challenges to operating systems, which traditionally assume homogeneous hardware. This paper presents AMPS, an operating system scheduler that efficiently supports both SMPand NUMA-style performance-asymmetric architectures. AMPS contains three components: asymmetry-aware load balancing, fastercore-first scheduling, and NUMA-aware migration. We have implemented AMPS in Linux kernel 2.6.16 and used CPU clock modulation to emulate performance asymmetry on an SMP and NUMA system. For various workloads, we show that AMPS achieves a median speedup of 1.16 with a maximum of 1.44 over stock Linux on the SMP, and a median of 1.07 with a maximum of 2.61 on the NUMA system. Our results also show that AMPS improves fairness and repeatability of application performance measurements. 1.

Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems.

Applications implemented on critical systems are subject to both safety critical and real-time constraints. Classically, applications are specified as precedence task graphs that must be scheduled onto a given target multiprocessor heterogeneous architecture. We propose a new method for optimizing simultaneously two objectives: the execution time and the reliability of the schedule. The problem is decomposed in two successive steps: a spatial allocation during which the reliability is maximized (randomized algorithm), and a scheduling during which the makespan is minimized (list scheduling algorithm). It allows us to produce several trade-off solutions among which the user can choose the solution that fits the application’s requirements the best. Reliability is increased by replicating adequate tasks onto well chosen processors. Our fault model assumes that processors are fail-silent, that they are subject to transient failures, and that the occurrences of failures follow a constant parameter Poisson law. We assess and validate our method by running extensive simulations on both random graphs and actual application graphs. They show that it is competitive, in terms of makespan, compared to existing reference scheduling methods for heterogeneous processors (HEFT), while providing a better reliability. 1

Abstract — Applications to be executed in Grid computing environments become more and more complex and usually consist of multiple interdependent tasks. The coordinated execution of such tightly or loosely coupled tasks often requires simultaneous access to different Grid resources. This leads to the problem of resource co-allocation. Efficient and robust scheduling algorithms have to be developed that can cope with the Grid’s largescale distribution, a high number of competing and demanding applications, the inherent resource heterogeneity and the often limited view on resource availability. In this paper, we present two heuristic scheduling algorithms that are based on a well-known list scheduling algorithm and both support coallocation and advance resource reservation. Our first algorithm preserves the run-time efficiency of Greedy list schedulers while the second approach incorporates more sophisticated search techniques in order to achieve better results with respect to the performance metrics. Both algorithms have been implemented within a Grid simulation framework. An extensive simulation study was conducted to evaluate and compare the performance of both algorithms. It showed the general suitability of our enhanced list scheduling heuristics within heterogeneous Grid environments. I.

Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of inter-dependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to satisfy objective functions specified by users. Proper scheduling can have significant impact on the performance of the system. In this chapter, we investigate existing workflow scheduling algorithms developed and deployed by various Grid projects.

Abstract 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.