A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms

International audienceMixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches and show that our modifications lead to significant improvements. We then perform a comparison of the modified algorithms in simulation over a wide range of application and platform conditions. We find that although both approaches have advantages, one of them is most likely he most appropriate for the majority of users

A Comparison of Scheduling Approaches for Mixed-Parallel Applications on Heterogeneous Platforms

International audienceMixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches and show that our modifications lead to significant improvements. We then perform a comparison of the modified algorithms in simulation over a wide range of application and platform conditions. We find that although both approaches have advantages, one of them is most likely he most appropriate for the majority of users

Elastic neural network method for load prediction in cloud computing grid

Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches

De l'ordonnancement des applications multi-niveaux

8 pagesNational audienceSous l'impulsion des besoins applicatifs, les moyens de calcul sont de plus en plus puissants. Cette évolution se fait notamment grâce à des architectures de plus en plus parallèles. Dans ce contexte, la portabilité des performances des applications HPC très optimisées est problématique. Le présent article est motivé par l'exemple d'une application HPC appelée HLW (High-Level Waste). Nous présentons un modèle de la structure d'HLW indépendant de l'architecture d'exécution. Nous généralisons ensuite ce modèle en introduisant les \emph{applications multi-niveaux} : des applications constituées d'un ensemble de tâches indépendantes ayant une probabilité de déclencher l'apparition d'une nouvelle tâche lorsqu'elles terminent. On s'intéresse ensuite à l'ordonnancement de telles tâches dans le cas où elles sont modelables, suivent la loi d'Amdahl et où l'architecture d'exécution est homogène. Nous proposons ensuite une famille d'algorithmes et évaluons ses performances à travers des simulations. Finalement, on sélectionne l'algorithme ayant les meilleures performances. Cet algorithme constitue une amélioration par rapport à l'ordonnancement par défaut d'HLW à la fois en terme de performance et d'indépendance par rapport aux paramètres de l'architecture

Scheduling Dynamic Workflows onto Clusters of Clusters using Postponing

International audienceIn this article, we revisit the problem of scheduling dynamically generated directed acyclic graphs (DAGs) of multi-processor tasks (M-tasks). A DAG is a basic model for expressing workflows applications where each node represents a task of the workflow. We present a novel algorithm (DMHEFT) for scheduling dynamically generated DAGs onto a heterogeneous collection of clusters. The scheduling decisions are based on the predicted runtime of an M-task as well as the estimation of the redistribution costs between data-dependent tasks. The algorithm also takes care of unfavorable placements of M-tasks by considering the postponing of ready tasks even if idle processors are available. We evaluate the scheduling algorithm by comparing the resulting makespans to the results obtained by using other scheduling algorithms, such as RePA and MHEFT

Mixed Data-Parallel Scheduling for Distributed Continuous Integration

International audienceIn this paper, we consider the problem of schedul- ing a special kind of mixed data-parallel applications arising in the context of continuous integration. Continuous integration (CI) is a software engineering technique, which consists in re- building and testing interdependent software components as soon as developers modify them. The CI tool is able to provide quick feedback to the developers, which allows them to fix the bug soon after it has been introduced. The CI process can be described as a DAG where nodes represent package build tasks, and edges represent dependencies among these packages; build tasks themselves can in turn be run in parallel. Thus, CI can be viewed as a mixed data-parallel application. A crucial point for a successful CI process is its ability to provide quick feedback. Thus, makespan minimization is the main goal. Our contribution is twofold. First we provide and analyze a large dataset corresponding to a build DAG. Second, we compare the performance of several scheduling heuristics on this dataset

Optimizing performance of workflow executions under authorization control

“Business processes or workflows are often used to model enterprise or scientific applications. It has received considerable attention to automate workflow executions on computing resources. However, many workflow scenarios still involve human activities and consist of a mixture of human tasks and computing tasks. Human involvement introduces security and authorization concerns, requiring restrictions on who is allowed to perform which tasks at what time. Role- Based Access Control (RBAC) is a popular authorization mechanism. In RBAC, the authorization concepts such as roles and permissions are defined, and various authorization constraints are supported, including separation of duty, temporal constraints, etc. Under RBAC, users are assigned to certain roles, while the roles are associated with prescribed permissions. When we assess resource capacities, or evaluate the performance of workflow executions on supporting platforms, it is often assumed that when a task is allocated to a resource, the resource will accept the task and start the execution once a processor becomes available. However, when the authorization policies are taken into account,” this assumption may not be true and the situation becomes more complex. For example, when a task arrives, a valid and activated role has to be assigned to a task before the task can start execution. The deployed authorization constraints may delay the workflow execution due to the roles’ availability, or other restrictions on the role assignments, which will consequently have negative impact on application performance. When the authorization constraints are present to restrict the workflow executions, it entails new research issues that have not been studied yet in conventional workflow management. This thesis aims to investigate these new research issues. First, it is important to know whether a feasible authorization solution can be found to enable the executions of all tasks in a workflow, i.e., check the feasibility of the deployed authorization constraints. This thesis studies the issue of the feasibility checking and models the feasibility checking problem as a constraints satisfaction problem. Second, it is useful to know when the performance of workflow executions will not be affected by the given authorization constraints. This thesis proposes the methods to determine the time durations when the given authorization constraints do not have impact. Third, when the authorization constraints do have the performance impact, how can we quantitatively analyse and determine the impact? When there are multiple choices to assign the roles to the tasks, will different choices lead to the different performance impact? If so, can we find an optimal way to conduct the task-role assignments so that the performance impact is minimized? This thesis proposes the method to analyze the delay caused by the authorization constraints if the workflow arrives beyond the non-impact time duration calculated above. Through the analysis of the delay, we realize that the authorization method, i.e., the method to select the roles to assign to the tasks affects the length of the delay caused by the authorization constraints. Based on this finding, we propose an optimal authorization method, called the Global Authorization Aware (GAA) method. Fourth, a key reason why authorization constraints may have impact on performance is because the authorization control directs the tasks to some particular roles. Then how to determine the level of workload directed to each role given a set of authorization constraints? This thesis conducts the theoretical analysis about how the authorization constraints direct the workload to the roles, and proposes the methods to calculate the arriving rate of the requests directed to each role under the role, temporal and cardinality constraints. Finally, the amount of resources allocated to support each individual role may have impact on the execution performance of the workflows. Therefore, it is desired to develop the strategies to determine the adequate amount of resources when the authorization control is present in the system. This thesis presents the methods to allocate the appropriate quantity for resources, including both human resources and computing resources. Different features of human resources and computing resources are taken into account. For human resources, the objective is to maximize the performance subject to the budgets to hire the human resources, while for computing resources, the strategy aims to allocate adequate amount of computing resources to meet the QoS requirements