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

Adaptive Failure-Aware Scheduling for Hadoop

Given the dynamic nature of cloud environments, failures are the norm rather than the exception in data centers powering cloud frameworks. Despite the diversity of integrated recovery mechanisms in cloud frameworks, their schedulers still generate poor scheduling decisions leading to tasks' failures due to unforeseen events such as unpredicted demands of services or hardware outages. Traditionally, simulation and analytical modeling have been widely used to analyze the impact of the scheduling decisions on the failures rates. However, they cannot provide accurate results and exhaustive coverage of the cloud systems especially when failures occur. In this thesis, we present new approaches for modeling and verifying an adaptive failure-aware scheduling algorithm for Hadoop to early detect these failures and to reschedule tasks according to changes in the cloud. Hadoop is the framework of choice on many off-the-shelf clusters in the cloud to process data-intensive applications by efficiently running them across distributed multiple machines. The proposed scheduling algorithm for Hadoop relies on predictions made by machine learning algorithms trained on previously executed tasks and data collected from the Hadoop environment. To further improve Hadoop scheduling decisions on the fly, we use reinforcement learning techniques to select an appropriate scheduling action for a scheduled task. Furthermore, we propose an adaptive algorithm to dynamically detect failures of nodes in Hadoop. We implement the above approaches in ATLAS: an AdapTive Failure-Aware Scheduling algorithm that can be built on top of existing Hadoop schedulers. To illustrate the usefulness and benefits of ATLAS, we conduct a large empirical study on a Hadoop cluster deployed on Amazon Elastic MapReduce (EMR) to compare the performance of ATLAS to those of three Hadoop scheduling algorithms (FIFO, Fair, and Capacity). Results show that ATLAS outperforms these scheduling algorithms in terms of failures' rates, execution times, and resources utilization. Finally, we propose a new methodology to formally identify the impact of the scheduling decisions of Hadoop on the failures rates. We use model checking to verify some of the most important scheduling properties in Hadoop (schedulability, resources-deadlock freeness, and fairness) and provide possible strategies to avoid their occurrences in ATLAS. The formal verification of the Hadoop scheduler allows to identify more tasks failures and hence reduce the number of failures in ATLAS

Resources management architecture and algorithms for virtualized IVR applications in cloud environment

Interactive Voice Response (IVR) applications are ubiquitous nowadays. IVR is a telephony technology that allows interactions with a wide range of automated information systems via a telephone keypad or voice commands. Cloud computing is a newly emerging paradigm that hosts and provides services over the Internet with many inherent benefits. It has three major service models: Infrastructure as a service (IaaS), Platform as a service (PaaS), and Software as a Service (SaaS). Cloud computing is based on the virtualization technology that enables the co-existence of entities in general on the same substrates. These entities may be operating systems co-existing on the same hardware, applications co-existing on the same operating system, or even full-blown networks co-existing on the same routers. The key benefit is efficiency through the sharing of physical resources. Several multimedia applications are provided in cloud environments nowadays. However, to the best of our knowledge, there is no architecture that creates and manages IVR applications in cloud environment. Therefore, we propose to develop a new virtualized architecture that can create, deploy and manage IVR applications in cloud environment. We also propose two new algorithms for resources management and task scheduling as an essential part of resource sharing in such environment

Task Scheduling in Big Data Platforms: A Systematic Literature Review

Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both research and industrial communities that allow expressing and processing distributed computations on massive amounts of data. Multiple scheduling algorithms have been proposed to ensure that short interactive jobs, large batch jobs, and guaranteed-capacity production jobs running on these frameworks can deliver results quickly while maintaining a high throughput. However, only a few works have examined the effectiveness of these algorithms. Objective: The Evidence-based Software Engineering (EBSE) paradigm and its core tool, i.e., the Systematic Literature Review (SLR), have been introduced to the Software Engineering community in 2004 to help researchers systematically and objectively gather and aggregate research evidences about different topics. In this paper, we conduct a SLR of task scheduling algorithms that have been proposed for big data platforms. Method: We analyse the design decisions of different scheduling models proposed in the literature for Hadoop, Spark, Storm, and Mesos over the period between 2005 and 2016. We provide a research taxonomy for succinct classification of these scheduling models. We also compare the algorithms in terms of performance, resources utilization, and failure recovery mechanisms. Results: Our searches identifies 586 studies from journals, conferences and workshops having the highest quality in this field. This SLR reports about different types of scheduling models (dynamic, constrained, and adaptive) and the main motivations behind them (including data locality, workload balancing, resources utilization, and energy efficiency). A discussion of some open issues and future challenges pertaining to improving the current studies is provided

ATLAS: An Adaptive Failure-aware Scheduler for Hadoop

Abstract Hadoop has become the de facto standard for processing large data in today's cloud environment. The performance of Hadoop in the cloud has a direct impact on many important applications ranging from web analytic, web indexing, image and document processing to high-performance scientific computing. However, because of the scale, complexity and dynamic nature of the cloud, failures are common and these failures often impact the performance of jobs running in Hadoop. Although Hadoop possesses built-in failure detection and recovery mechanisms, several scheduled jobs still fail because of unforeseen events in the cloud environment. A single task failure can cause the failure of the whole job and unpredictable job running times. In this paper, we propose ATLAS (AdapTive faiLure-Aware Scheduler), a new scheduler for Hadoop that can adapt its scheduling decisions to events occurring in the cloud environment. Using statistical models, ATLAS predicts task failures and adjusts its scheduling decisions on the fly to reduce task failure occurrences. We implement ATLAS in the Hadoop framework of Amazon Elastic MapReduce (EMR) and perform a case study to compare its performance with those of the FIFO, Fair and Capacity schedulers. Results show that ATLAS can reduce the percentage of failed jobs by up to 28% and the percentage of failed tasks by up to 39%, and the total execution time of jobs by 10 minutes on average. ATLAS also reduces CPU and memory usages

Etude experimentale de l'ecoulement turbulent parietal produit par l'impact d'un jet frappant normalement un disque coaxial en rotation

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc