Dynamic service selection in workflows using performance data

An approach to dynamic workflow management and optimisation using near-realtime performance data is presented. Strategies are discussed for choosing an optimal service (based on user-specified criteria) from several semantically equivalent Web services. Such an approach may involve finding "similar" services, by first pruning the set of discovered services based on service metadata, and subsequently selecting an optimal service based on performance data. The current implementation of the prototype workflow framework is described, and demonstrated with a simple workflow. Performance results are presented that show the performance benefits of dynamic service selection. A statistical analysis based on the first order statistic is used to investigate the likely improvement in service response time arising from dynamic service selection

Using Forecasting to Predict Long-term Resource Utilization for Web Services

Researchers have spent years understanding resource utilization to improve scheduling, load balancing, and system management through short-term prediction of resource utilization. Early research focused primarily on single operating systems; later, interest shifted to distributed systems and, finally, into web services. In each case researchers sought to more effectively use available resources. Since schedulers are required to manage the execution of multiple programs every second, short-term prediction has focused on time frames ranging from fractions of a second to several minutes. The recent increase in the number of research studies about web services has occurred because of the explosive growth and reliance on these services by most businesses. As demand has moved from static to dynamic content, the load on machine resources has grown exponentially, periodically resulting in temporary loss of service. To address these short-term denial-of-service issues, researchers have tried short-term prediction to manage scheduling of service requests. What researchers have not considered is that the same methods used for single step short-term prediction can also be used for long-term prediction if a coarse granularity of samples is used. Instead of using one or more samples per second, a coarser aggregate of minutes or hours more accurately emulates the long-term patterns. This research has shown that simple moving averages and exponential moving averages as a prediction technique can be used to more accurately predict hourly, daily, and weekly seasonal patterns of resource utilization for web servers. Additionally, this research provides a foundation where using a resource prediction within a confidence interval range could be more useful to an administrator or system software than a single prediction point. When the focus shifts to a range, a set of probabilities can establish normal function within that system. For distributed systems, it will provide the ability to notify other systems when resource utilization is no longer normal before that system is unable to issue a notice of overloading. For web systems it can be used to provide a warning, permitting the instantiation of a second system to begin load balancing during unscheduled heavy loads. In both cases, the availability of the system can be improved by predicting a resource utilization level and the confidence interval within which that resource use has historically fallen

Putting the User at the Centre of the Grid: Simplifying Usability and Resource Selection for High Performance Computing

Computer simulation is finding a role in an increasing number of scientific disciplines, concomitant with the rise in available computing power. Realizing this inevitably re- quires access to computational power beyond the desktop, making use of clusters, supercomputers, data repositories, networks and distributed aggregations of these re- sources. Accessing one such resource entails a number of usability and security prob- lems; when multiple geographically distributed resources are involved, the difficulty is compounded. However, usability is an all too often neglected aspect of computing on e-infrastructures, although it is one of the principal factors militating against the widespread uptake of distributed computing. The usability problems are twofold: the user needs to know how to execute the applications they need to use on a particular resource, and also to gain access to suit- able resources to run their workloads as they need them. In this thesis we present our solutions to these two problems. Firstly we propose a new model of e-infrastructure resource interaction, which we call the user–application interaction model, designed to simplify executing application on high performance computing resources. We describe the implementation of this model in the Application Hosting Environment, which pro- vides a Software as a Service layer on top of distributed e-infrastructure resources. We compare the usability of our system with commonly deployed middleware tools using five usability metrics. Our middleware and security solutions are judged to be more usable than other commonly deployed middleware tools. We go on to describe the requirements for a resource trading platform that allows users to purchase access to resources within a distributed e-infrastructure. We present the implementation of this Resource Allocation Market Place as a distributed multi- agent system, and show how it provides a highly flexible, efficient tool to schedule workflows across high performance computing resources

Predictive resource scheduling in computational grids

The integration of clusters of computers into computational grids has recently gained the attention of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. The paper describes an architecture and an implementation for a predictive grid scheduling framework which relies on Kalman filter theory to predict future CPU resource utilisation. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20% of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators. © 2007 IEEE

Predictive Resource Scheduling in Computational Grids

The integration of clusters of computers into computational grids has recently gained the atten- tion of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. We describe a formal framework that uses Kalman filter theory to predict future CPU resource utilisation. This ability to predict future resource utilisation forms the basis for significantly improved grid scheduling decisions. The paper describes the architecture for such a prediction and grid scheduling framework and its implementation using Condor. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20% of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators

Predictive Resource Scheduling in Computational Grids ∗

The integration of clusters of computers into computational grids has recently gained the attention of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. The paper describes an architecture and an implementation for a predictive grid scheduling framework which relies on Kalman filter theory to predict future CPU resource utilisation. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20 % of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators.