Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters

Modular software architecture for flexible reservation mechanisms on heterogeneous resources

Management, allocation and scheduling of heterogeneous resources for complex distributed real-time applications is a chal- lenging problem. Timing constraints of applications may be fulfilled by a proper use of real-time scheduling policies, admission control and enforcement of timing constraints. However, it is not easy to design basic infrastructure services that allow for an easy access to the allocation of multiple heterogeneous resources in a distributed environment. In this paper, we present a middleware for providing distributed soft real-time applications with a uniform API for reserving heterogeneous resources with real-time scheduling capabilities in a distributed environment. The architecture relies on standard POSIX OS facilities, such as time management and standard TCP/IP networking services, and it is designed around CORBA, in order to facilitate modularity, flexibility and portability of the applications using it. However, real-time scheduling is supported by proper extensions at the kernel-level, plugged within the framework by means of dedicated resource managers. Our current implementation on Linux supports reservation of CPU, disk and network bandwidth. However, additional resource managers supporting alternative real-time schedulers for these resources, as well as additional types of resources, may be easily added. We present experimental results gathered on both synthetic applications and a real multimedia video streaming case study, showing advantages deriving from the use of the proposed middleware. Finally, overhead figures are reported, showing sustainability of the approach for a wide class of complex, distributed, soft real-time applications

I/O Schedulers for Proportionality and Stability on Flash-Based SSDs in Multi-Tenant Environments

The use of flash based Solid State Drives (SSDs) has expanded rapidly into the cloud computing environment. In cloud computing, ensuring the service level objective (SLO) of each server is the major criterion in designing a system. In particular, eliminating performance interference among virtual machines (VMs) on shared storage is a key challenge. However, studies on SSD performance to guarantee SLO in such environments are limited. In this paper, we present analysis of I/O behavior for a shared SSD as storage in terms of proportionality and stability. We show that performance SLOs of SSD based storage systems being shared by VMs or tasks are not satisfactory. We present and analyze the reasons behind the unexpected behavior through examining the components of SSDs such as channels, DRAM buffer, and Native Command Queuing (NCQ). We introduce two novel SSD-aware host level I/O schedulers on Linux, called A & x002B;CFQ and H & x002B;BFQ, based on our analysis and findings. Through experiments on Linux, we analyze I/O proportionality and stability in multi-tenant environments. In addition, through experiments using real workloads, we analyze the performance interference between workloads on a shared SSD. We then show that the proposed I/O schedulers almost eliminate the interference effect seen in CFQ and BFQ, while still providing I/O proportionality and stability for various I/O weighted scenarios

Nu@ge: Towards a solidary and responsible cloud computing service

Best Paper AwardInternational audienceThe adoption of cloud computing is still limited by several legal concerns from companies. One of those reasons is the data sovereignty, as data can be physically host in sensible locations, resulting in a lack of control for companies. In this paper, we present the Nu@ge project aimed at building a federation of container-sized datacenter on the French territory. Nu@ge provides a software stack that enables companies to put independent datacenters in cooperation in a national mesh. Additionally, a prototype of a container-sized datacenter has been validated and patented

Rethinking the design and implementation of the i/o software stack for high-performance computing

Current I/O stack for high-performance computing is composed of multiple software layers in order to hide users from complexity of I/O performance optimization. However, the design and implementation of a specific layer is usually carried out separately with limited consideration of its impact on other layers, which could result in suboptimal I/O performance because data access locality is weakened, if not lost, on hard disk, a widely used storage medium in high-end storage systems. In this dissertation, we experimentally demonstrated such issues in four different layers, including operating system process management layer and MPI-IO middleware layer on compute server side, and parallel file system layer and disk I/O scheduling layer on data server side. This dissertation makes four contributions towards solving each of the issues. First, we propose a data-driven execution model for DualPar to explore opportunity of effective I/O scheduling to alleviate I/O bottleneck via cooperation between the I/O and process schedulers. Its novelty is on the ability to obtain a pool of pre-sorted requests to I/O scheduler in its data-driven execution mode by using process pre-execution and prefetching techniques. Second, realizing that well-formed locality for an MPI program by using collective I/O can be seriously compromised by non-determinism in process scheduling, we proposed Resonant I/O, to match the data request pattern with the pattern of file striping over multiple data servers to improve disk efficiency. Third, since the conventional practice for I/O parallelism using file striping may compromise on-disk data access locality, we proposed IOrchestrator scheduling framework which is implemented in PVFS2 parallel file system to improve I/O performance of multi-node storage systems by orchestrating I/O services among programs when such inter-data-server coordination is dynamically determined to be cost effective. Fourth, we developed iTransformer, a scheme that employs a small SSD to schedule requests for the data on disk. Being less space constrained than with more expensive DRAM, iTransformer can buffer larger amounts of dirty data before writing it back to the disk, or prefetch a larger volume of data in a batch into the SSD. In both cases high disk efficiency can be maintained for highly concurrent requests

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads

Chronos: Failure-Aware Scheduling in Shared Hadoop Clusters

International audienceHadoop emerged as the de facto state-of-the-art system for MapReduce-based data analytics. The reliability of Hadoop systems depends in part on how well they handle failures. Currently, Hadoop handles machine failures by re-executing all the tasks of the failed machines (i.e., executing recovery tasks). Unfortunately, this elegant solution is entirely entrusted to the core of Hadoop and hidden from Hadoop schedulers. The unawareness of failures therefore may prevent Hadoop schedulers from operating correctly towards meeting their objectives (e.g., fairness, job priority) and can significantly impact the performance of MapReduce applications. This paper presents Chronos, a failure-aware scheduling strategy that enables an early yet smart action for fast failure recovery while still operating within a specific scheduler objective. Upon failure detection, rather than waiting an uncertain amount of time to get resources for recovery tasks, Chronos leverages a lightweight preemption technique to carefully allocate these resources. In addition, Chronos considers data locality when scheduling recovery tasks to further improve the performance. We demonstrate the utility of Chronos by combining it with Fifo and Fair schedulers. The experimental results show that Chronos recovers to a correct scheduling behavior within a couple of seconds only and reduces the job completion times by up to 55% compared to state-of-the-art schedulers