Predicting Scheduling Failures in the Cloud

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes

The energy scheduling problem: Industrial case-study and constraint propagation techniques

This paper deals with production scheduling involving energy constraints, typically electrical energy. We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem,the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via tree search.Finally,computational results are provided

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS