Scheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds

MapReduce has become a popular programming model for running data intensive applications on the cloud. Completion time goals or deadlines of MapReduce jobs set by users are becoming crucial in existing cloud-based data processing environments like Hadoop. There is a conflict between the scheduling MR jobs to meet deadlines and "data locality" (assigning tasks to nodes that contain their input data). To meet the deadline a task may be scheduled on a node without local input data for that task causing expensive data transfer from a remote node. In this paper, a novel scheduler is proposed to address the above problem which is primarily based on the dynamic resource reconfiguration approach. It has two components: 1) Resource Predictor: which dynamically determines the required number of Map/Reduce slots for every job to meet completion time guarantee; 2) Resource Reconfigurator: that adjusts the CPU resources while not violating completion time goals of the users by dynamically increasing or decreasing individual VMs to maximize data locality and also to maximize the use of resources within the system among the active jobs. The proposed scheduler has been evaluated against Fair Scheduler on virtual cluster built on a physical cluster of 20 machines. The results demonstrate a gain of about 12% increase in throughput of Job

CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

Data location aware scheduling for virtual Hadoop cluster deployment on private cloud computing environment

With the advancements of Internet-of-Things (IoT) and Machine-to-Machine Communications (M2M), the ability to generate massive amount of streaming data from sensory devices in distributed environment is inevitable. A common practice nowadays is to process these data in a high-performance computing infrastructure, such as cloud. Cloud platform has the ability to deploy Hadoop ecosystem on virtual clusters. In cloud configuration with different geographical regions, virtual machines (VMs) that are part of virtual cluster are placed randomly. Prior to processing, data have to be transferred to the regional sites with VMs for data locality purposes. In this paper, a provisioning strategy with data-location aware deployment for virtual cluster will be proposed, as to localize and provision the cluster near to the storage. The proposed mechanism reduces the network distance between virtual cluster and storage, resulting in reduced job completion times

Cost-Effective Resource Provisioning for MapReduce in a Cloud

This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. First, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Second, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Third, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura's core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80 percent reduction in the cloud compute infrastructure cost with upto 65 percent reduction in job response times

Autonomic Management And Performance Optimization For Cloud Computing Services

Cloud computing has become an increasingly important computing paradigm. It offers three levels of on-demand services to cloud users: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) . The success of cloud services heavily depends on the effectiveness of cloud management strategies. In this dissertation work, we aim to design and implement an automatic cloud management system to improve application performance, increase platform efficiency and optimize resource allocation. For large-scale multi-component applications, especially web-based cloud applica- tions, parameter setting is crucial to the service availability and quality. The increas- ing system complexity requires an automatic and efficient application configuration strategy. To improve the quality of application services, we propose a reinforcement learning(RL)-based autonomic configuration framework. It is able to adapt appli- cation parameter settings not only to the variations in workload, but also to the change of virtual resource allocation. The RL approach is enhanced with an efficient initialization policy to reduce the learning time for online decision. Experiments on Xen-based virtual cluster with TPC-W benchmarks show that the framework can drive applications into a optimal configuration in less than 25 iterations. For cloud platform service, one of the key challenges is to efficiently adapt the offered platforms to the virtualized environment, meanwhile maintaining their service features. MapReduce has become an important distributed parallel programming paradigm. Offering MapReduce cloud service presents an attractive usage model for enterprises. In a virtual MapReduce cluster, the interference between virtual machines (VMs) causes performance degradation of map and reduce tasks and renders existing data locality-aware task scheduling policy, like delay scheduling, no longer effective. On the other hand, virtualization offers an extra opportunity of data locality for co-hosted VMs. To address these issues, we present a task scheduling strategy to mitigate interference and meanwhile preserving task data locality for MapReduce applications. The strategy includes an interference-aware scheduling policy, based on a task performance prediction model, and an adaptive delay scheduling algorithm for data locality improvement. Experimental results on a 72-node Xen-based virtual cluster show that the scheduler is able to achieve a speedup of 1.5 to 6.5 times for individual jobs and yield an improvement of up to 1.9 times in system throughput in comparison with four other MapReduce schedulers. Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the fly to adapt to system dynamics. The in- terplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this work, we propose a framework for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advan- tages of Simplex method and RL method and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that the framework is able to drive a virtual server cluster into an optimal or near-optimal configuration state on the fly, in response to the change of workload. It improves the systems throughput by more than 30% over independent tuning strategies. In comparison with the coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25% to 40% throughput improvement

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice