面向云文件系统的隔离性度量方法研究

随着云计算的快速发展,云文件系统在云计算基础设施中扮演着越来越重要的角色。尽管目前业界已有不少面向云文件系统的性能评测工具,但大多数评测工具仅关注于传统的系统性能指标,比如IOPS和吞吐量,难以评估云文件系统在多租户环境下的性能隔离性。由于云环境I/O负载的动态性和异构性,所以准确评估云文件系统的隔离性变得更加具有挑战性。提出了一种新型的云文件系统隔离性度量模型,并在一个基准测试工具Porcupine中进行了实现。Porcupine通过模拟真实负载特征的I/O请求,实现对负载与性能的准确仿真并提高文件系统的测试效率。通过对Ceph文件系统的实验,验证了所提出的隔离性度量模型的有效性及准确性。     

大数据基准测试程序包构建方法研究

基准测试程序是评估计算机系统的关键测试工具。然而,大数据时代的到来使得开发大数据系统基准测试程序面临着更加严峻的挑战,当前学术界和产业界还不存在得到广泛认可的大数据基准测试程序包。文章利用实际的交通大数据系统构建了一个基于Hadoop平台的交通大数据基准测试程序包SIAT-Bench。通过选取多个层次属性量化了程序行为特征,采用聚类算法分析了不同程序-输入数据集对的相似性。根据聚类结果,为SIATBench选取了有代表性的程序和输入数据集。实验结果表明,SIAT-Bench在满足程序行为多样性的同时消除了基准测试集中的冗余。     

IOTSim: A simulator for analysing IoT applications

A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.     

RIoTBench: An IoT benchmark for distributed stream processing systems

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench , a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.     

CloudBM:云数据管理系统测试基准

随着云计算技术和云数据管理技术的不断发展,越来越多的云数据管理系统纷纷面世,为人们提供了一种以经济实用的方式管理海量数据的方案。面对各种各样纷繁复杂的云数据管理系统,如何结合不同的应用场景对它们进行全面详细的性能评价是目前亟待解决的挑战性问题。以电信业务中用户访问记录存储系统为应用背景,结合云数据管理系统的应用特点,设计了一个具有广泛代表性的测试基准,并对目前主流的云数据管理系统进行了实际测试。通过对测试结果进行对比分析,为用户提供了一个可供参考的性能评价结果,同时验证了该测试基准的可靠性和广泛适用性。     

大数据评测基准的研发现状与趋势

工业界、学术界,以及最终用户都急切需要一个大数据的评测基准, 用以评估现有的大数据系统,改进现有技术以及开发新的技术。回顾了近几年来大数据评测基准研发方面的主要工作。 对它们的特点和缺点进行了比较分析。在此基础上, 对研发新的大数据评测基准提出了一系列考虑因素:1)为了对整个大数据平台的不同子工具进行评测, 以及把大数据平台作为一个整体进行评测, 需要研发面向组件的评测基准和面向大数据平台整体的评测基准, 后者是前者的有机组合;2)工作负载除了SQL查询之外, 必须包含大数据分析任务所需要的各种复杂分析功能, 涵盖各类应用需求;3)在评测指标方面,除了性能指标(响应时间和吞吐量)之外, 还需要考虑其他指标的评测, 包括系统的可扩展性、容错性、节能性和安全性等。     

How much power is needed for a billion-thread high-throughput server?

With the advent of Internet services, big data and cloud computing, high-throughput computing has generated much research interest, especially on high-throughput cloud servers. However, three basic questions are still not satisfactorily answered: (1) What are the basic metrics (what throughput and high-throughput of what)? (2) What are the main factors most beneficial to increasing throughput? (3) Are there any fundamental constraints and how high can the throughput go? This article addresses these issues by utilizing the fifty-year progress in Little??s law, to reveal three fundamental relations among the seven basic quantities of throughput (??), number of active threads (L), waiting time (W), system power (P), thread energy (E), Watts per thread ??, threads per Joule ??. In addition to Little??s law L = ??W, we obtain P = ??E and ?? = L???, under reasonable assumptions. These equations help give a first order estimation of performance and power consumption targets for billion-thread cloud servers.     

Scheduling big data applications within advance reservation framework in optical grids

Providing QoS for big data applications requires a way to reserve computing and networking resources in advance. Within advance reservation framework, a multi-domain scheduling process is carried out in a top down hierarchical way across multiple hierarchical levels. This ensures that each domain executes intra-domain scheduling algorithm to co-schedule its own computing and networking resources while coordinating the scheduling at the inter-domain level. Within this process, we introduce two algorithms: iterative scheduling algorithm and K-shortest paths algorithm. We conducted a comprehensive performance evaluation study considering several metrics that reflect both grid system and grid user goals. The results demonstrated the advantages of the proposed scheduling process. Moreover, the results highlight the importance of using the iterative scheduling and K-shortest paths algorithms especially for data intensive applications.     

CEPSim: Modelling and simulation of Complex Event Processing systems in cloud environments

The emergence of Big Data has had profound impacts on how data are stored and processed. As technologies created to process continuous streams of data with low latency, Complex Event Processing (CEP) and Stream Processing (SP) have often been related to the Big Data velocity dimension and used in this context. Many modern CEP and SP systems leverage cloud environments to provide the low latency and scalability required by Big Data applications, yet validating these systems at the required scale is a research problem per se. Cloud computing simulators have been used as a tool to facilitate reproducible and repeatable experiments in clouds. Nevertheless, existing simulators are mostly based on simple application and simulation models that are not appropriate for CEP or for SP. This article presents CEPSim, a simulator for CEP and SP systems in cloud environments. CEPSim proposes a query model based on Directed Acyclic Graphs (DAGs) and introduces a simulation algorithm based on a novel abstraction called event sets. CEPSim is highly customizable and can be used to analyse the performance and scalability of user-defined queries and to evaluate the effects of various query processing strategies. Experimental results show that CEPSim can simulate existing systems in large Big Data scenarios with accuracy and precision.     

No-reference synthetic image quality assessment with convolutional neural network and local image saliency

Depth-image-based rendering (DIBR) is widely used in 3DTV, free-viewpoint video, and interactive 3D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, particularly geometric distortions induced by object dis-occlusion. Ensuring the quality of synthetic images is critical to maintaining adequate system service. However, traditional 2D image quality metrics are ineffective for evaluating synthetic images as they are not sensitive to geometric distortion. In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights. Due to the lack of existing training data, we construct a new DIBR synthetic image dataset as part of our contribution. Experiments were conducted on both the public benchmark IRCCyN/IVC DIBR image dataset and our own dataset. Results demonstrate that our proposed metric outperforms traditional 2D image quality metrics and state-of-the-art DIBR-related metrics.

     

OpenStack Neat: a framework for dynamic and energy‐efficient consolidation of virtual machines in OpenStack clouds

Dynamic consolidation of virtual machines (VMs) is an efficient approach for improving the utilization of physical resources and reducing energy consumption in cloud data centers. Despite the large volume of research published on this topic, there are very few open‐source software systems implementing dynamic VM consolidation. In this paper, we propose an architecture and open‐source implementation of OpenStack Neat, a framework for dynamic VM consolidation in OpenStack clouds. OpenStack Neat can be configured to use custom VM consolidation algorithms and transparently integrates with existing OpenStack deployments without the necessity of modifying their configuration. In addition, to foster and encourage further research efforts in the area of dynamic VM consolidation, we propose a benchmark suite for evaluating and comparing dynamic VM consolidation algorithms. The proposed benchmark suite comprises OpenStack Neat as the base software framework, a set of real‐world workload traces, performance metrics and evaluation methodology. As an application of the proposed benchmark suite, we conduct an experimental evaluation of OpenStack Neat and several dynamic VM consolidation algorithms on a five‐node testbed, which shows significant benefits of dynamic VM consolidation resulting in up to 33% energy savings. Copyright © 2014 John Wiley & Sons, Ltd.     

A multi-dimensional job scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling systems based on a single-resource optimization, like processors, fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of several resource dimensions. Thus, the scheduling systems have to fully use all resources. In this paper, we address the problem of multi-resource scheduling via multi-capacity bin-packing. We propose the application of multi-capacity-aware resource scheduling at host selection layer and queuing mechanism layer of a scheduling system. The experimental results demonstrate performance improvements of scheduling in terms of waittime and slowdown metrics.     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

Data adapter for querying and transformation between SQL and NoSQL database

As the growing of applications with big data in cloud computing become popular, many existing systems expect to expand their service to support the explosive increase of data. We propose a data adapter system to support hybrid database architecture including a relational database (RDB) and NoSQL database. It can support query from application and deal with database transformation at the same time. We provide three modes of query approach in data adapter system: blocking transformation mode (BT mode), blocking dump mode (BD mode), and direct access mode (DA mode). We provide a data synchronization mechanism and describe the design and implementation in detail. This paper focuses on velocity with proposed three modes and partly variety with data stored in RDB, NoSQL database and temporary files. With the proposed data adapter system, we can provide a seamless mechanism to use RDB and NoSQL database at the same time.     

A metrics suite of cloud computing adoption readiness

Recent research on cloud computing adoption suggests the lack of a deep understanding of its benefits by managers and organizations. We present a firm-level cloud computing readiness metrics suite and assess its applicability for various cloud computing service types. We propose four relevant categories for firm-level adoption readiness, including technology and performance, organization and strategy, economic and valuation, and regulatory and environmental dimensions. We further define sub-categories and measures for each. Our evidence of the appropriateness of the metrics suite is derived based on a series of empirical cases developed from our project work, which encompasses input from field interviews, business press sources, industry white papers, non-governmental organizations, and government agency sources. We also assess how the application of the metrics suite supports organizational users of cloud computing.     

ComMapReduce: An improvement of MapReduce with lightweight communication mechanisms

As a parallel programming framework, MapReduce can process scalable and parallel applications with large scale datasets. The executions of Mappers and Reducers are independent of each other. There is no communication among Mappers, neither among Reducers. When the amount of final results is much smaller than the original data, it is a waste of time processing the unpromising intermediate data. We observe that this waste can be significantly reduced by simple communication mechanisms to enhance the performance of MapReduce. In this paper, we propose ComMapReduce, an efficient framework that extends and improves MapReduce for big data applications in the cloud. ComMapReduce can effectively obtain certain shared information with efficient lightweight communication mechanisms. Three basic communication strategies, Lazy, Eager and Hybrid, and two optimization communication strategies, Prepositive and Postpositive, are proposed to obtain the shared information and effectively process big data applications. We also illustrate the implementations of three typical applications with large scale datasets on ComMapReduce. Our extensive experiments demonstrate that ComMapReduce outperforms MapReduce in all metrics without affecting the existing characteristics of MapReduce.     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

Defining quality metrics for graph clustering evaluation

Evaluation of clustering has significant importance in various applications of expert and intelligent systems. Clusters are evaluated in terms of quality and accuracy. Measuring quality is a unsupervised approach that completely depends on edges, whereas measuring accuracy is a supervised approach that measures similarity between the real clustering and the predicted clustering. Accuracy cannot be measured for most of the real-world networks since real clustering is unavailable. Thus, it will be advantageous from the viewpoint of expert systems to develop a quality metric that can assure certain level of accuracy along with the quality of clustering.In this paper we have proposed a set of three quality metrics for graph clustering that have the ability to ensure accuracy along with the quality. The effectiveness of the metrics has been evaluated on benchmark graphs as well as on real-world networks and compared with existing metrics. Results indicate competency of the suggested metrics while dealing with accuracy, which will definitely improve the decision-making in expert and intelligent systems. We have also shown that our metrics satisfy all of the six quality-related properties.     

Towards effective offloading mechanisms in fog computing

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

     

Metrics and techniques for quantifying performance isolation in cloud environments

The cloud computing paradigm enables the provision of cost efficient IT-services by leveraging economies of scale and sharing data center resources efficiently among multiple independent applications and customers. However, the sharing of resources leads to possible interference between users and performance problems are one of the major obstacles for potential cloud customers. Consequently, it is one of the primary goals of cloud service providers to have different customers and their hosted applications isolated as much as possible in terms of the performance they observe. To make different offerings, comparable with regards to their performance isolation capabilities, a representative metric is needed to quantify the level of performance isolation in cloud environments. Such a metric should allow to measure externally by running benchmarks from the outside treating the cloud as a black box. In this article, we propose three different types of novel metrics for quantifying the performance isolation of cloud-based systems.We consider four new approaches to achieve performance isolation in Software-as-a-Service (SaaS) offerings and evaluate them based on the proposed metrics as part of a simulation-based case study. To demonstrate the effectiveness and practical applicability of the proposed metrics for quantifying the performance isolation in various scenarios, we present a second case study evaluating performance isolation of the hypervisor Xen.