Resource management framework for collaborative computing systems over multiple virtual machines

A resource management framework for collaborative computing systems over multiple virtual machines (CCSMVM) is presented to increase the performance of computing systems by improving the resource utilization, which has constructed a scalable computing environment for resource on-demand utilization. We design a resource management framework based on the advantages of some components in grid computing platform, virtualized platform and cloud computing platform to reduce computing systems overheads and maintain workloads balancing with the supporting of virtual appliance, Xen API, applications virtualization and so on. The content of collaborate computing, the basis of virtualized resource management and some key technologies including resource planning, resource allocation, resource adjustment and resource release and collaborative computing scheduling are designed in detail. A prototype is designed, and some experiments have verified the correctness and feasibility of our prototype. System evaluations show that the time in resource allocation and resource release is proportional to the quantity of virtual machines, but not the time in the virtual machines migrations. CCSMVM has higher CPU utilization and better performance than other systems, such as Eucalyptus 2.0, Globus4.0, et al. It is concluded that CCSMVM can accelerate the execution of systems by improving average CPU utilization from the results of comparative analysis with other systems, so it is better than others. Our study on resource management framework has some significance to the optimization of the performance in virtual computing systems.     

一种基于硬件计数器的虚拟机性能干扰估算方法

虚拟化技术已成为云计算平台中的关键性支撑技术.它极大地提高了数据中心的资源利用率,降低了管理成本和能源消耗,但同时也为数据中心带来了新的问题——性能干扰.同一平台上的多虚拟机过度竞争某一底层硬件资源(如CPU,Cache等),会造成虚拟机性能严重下降;而出于安全性和可移植性的考虑,底层平台管理者需要尽量避免侵入式监测上层虚拟机,因而,如何透明而有效地从底层估算虚拟机性能干扰,成为虚拟化平台管理者必须面临的一个挑战.为应对以上挑战,提出了一种基于硬件计数器的虚拟机性能干扰估算方法.硬件计数器是程序运行期间产生的硬件事件信息(如CPU时间片、缓存失效次数等),已有工作主要利用大规模分布式系统任务相似性查找产生异常硬件计数器数据的节点,而没有探究硬件事件变化与性能干扰之间的直接关系.通过实验研究发现,硬件计数器(last level cache misses rates,简称LLC misses rates)与不同资源需求的应用性能干扰存在不同的关联关系;以此建立虚拟机性能干扰估算模型,估算虚拟机性能.实验结果表明:该方法可以有效地预测CPU密集型应用和网络密集型应用的性能干扰大小,并仅为系统带来小于10%的开销.     

Artificial bee colony based energy‐aware resource utilization technique for cloud computing

Cloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.     

Application kernels: HPC resources performance monitoring and variance analysis

Application kernels are computationally lightweight benchmarks or applications run repeatedly on high performance computing (HPC) clusters in order to track the Quality of Service (QoS) provided to the users. They have been successful in detecting a variety of hardware and software issues, some severe, that have subsequently been corrected, resulting in improved system performance and throughput. In this work, the application kernels performance monitoring module of eXtreme Data Metrics on Demand (XDMoD) is described. Through the XDMoD framework, the application kernels have been run repetitively on the Texas Advanced Computing Center's Stampede and Lonestar4 clusters for a total of over 14,000 jobs. This provides a body of data on the HPC clusters operation that can be used to statistically analyze how the application performance, as measured by metrics such as execution time and communication bandwidth, is affected by the cluster's workload. We discuss metric distributions, carry out regression and correlation analyses, and use a PCA study to describe the variance and relate the variance to factors such as the spatial distribution of the application in the cluster. Ultimately, these types of analyses can be used to improve the application kernel mechanism, which in turn results in improved QoS of the HPC infrastructure that is delivered to the end users. Copyright © 2015 John Wiley & Sons, Ltd.     

Experimental evaluation of loss perception in continuous media

Perception of multimedia quality, specified by quality-of-service (QoS) metrics, can be used by system designers to optimize customer satisfaction within resource bounds enforced by general-purpose computing platforms. Media losses, rate variations and transient synchronization losses have been suspected to affect human perception of multimedia quality. This paper presents metrics to measure such defects, and results of a series of user experiments that justify such speculations. Results of the study provide bounds on losses, rate variations and transient synchronization losses as a function of user satisfaction, in the form of Likert values. It is shown how these results can be used by algorithm designers of underlying multimedia systems.     

Accurately Measuring Denial of Service in Simulation and Testbed Experiments

Researchers in the denial-of-service (DoS) field lack accurate, quantitative, and versatile metrics to measure service denial in simulation and testbed experiments. Without such metrics, it is impossible to measure severity of various attacks, quantify success of proposed defenses, and compare their performance. Existing DoS metrics equate service denial with slow communication, low throughput, high resource utilization, and high loss rate. These metrics are not versatile because they fail to monitor all traffic parameters that signal service degradation. They are not quantitative because they fail to specify exact ranges of parameter values that correspond to good or poor service quality. Finally, they are not accurate since they were not proven to correspond to human perception of service denial. We propose several DoS impact metrics that measure the quality of service experienced by users during an attack. Our metrics are quantitative: they map QoS requirements for several applications into measurable traffic parameters with acceptable, scientifically determined thresholds. They are versatile: they apply to a wide range of attack scenarios, which we demonstrate via testbed experiments and simulations. We also prove metrics' accuracy through testing with human users.     

Review of performance metrics for green data centers: a taxonomy study

Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.     

A joint feature selection framework for multivariate resource usage prediction in cloud servers using stability and prediction performance

Resource provisioning in cloud servers depends on future resource utilization of different jobs. As resource utilization trends vary dynamically, effective resource provisioning requires prediction of future resource utilization. The problem becomes more complicated as performance metrics related to one resource may depend on utilization of other resources also. In this paper, different multivariate frameworks are proposed for improving the future resource metric prediction in cloud. Different techniques for identifying the set of resource metrics relevant for the prediction of desired resource metric are analyzed. The proposed multivariate feature selection and prediction frameworks are validated for CPU utilization prediction in Google cluster trace. Joint analysis based on the prediction performance of the multivariate framework as well as its stability is used for selecting the most suitable feature selection framework. The results of the joint analysis indicate that features selected using the Granger causality technique perform best for multivariate resource usage prediction.     

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.     

A Grid resource brokering strategy based on resource and network performance in Grid

To achieve high performance distributed data access and computing in Grid environment, monitoring of resource and network performance is vital. Our proposed Grid network monitoring architecture is modeled by the Grid scheduler. The proposed Grid network monitoring retrieves network metrics using sensors as network monitoring tools. The mobile agents are migrated to start the sensors to measure the network metrics in all Grid Resources from the Resource Broker. The raw data provided by the monitoring tools is used to produce a high level view of the Grid through the set of internal cost functions. The network cost function is formed by combining various network metrics such as bandwidth, Round Trip Time, jitter and packet loss to measure the network performance. This paper presents the Grid Resource Brokering strategy which analyzes the network metrics along with the resource metrics for the selection of the Grid resource to submit the job and the proposed approach is integrated with CARE Resource Broker (CRB) for job submission. The experimental results are evident for the minimization of job completion time for the submitted job. The simulation results also prove that the more number of jobs are completed with the proposed strategy which influences the better utilization of the Grid resources.     

Design and implementation of an adaptive job allocation strategy for heterogeneous multi‐cluster computing systems

Cluster computing is an attractive approach to provide high‐performance computing for solving large‐scale applications. Owing to the advances in processor and networking technology, expanding clusters have resulted in the system heterogeneity; thus, it is crucial to dispatch jobs to heterogeneous computing resources for better resource utilization. In this paper, we propose a new job allocation system for heterogeneous multi‐cluster environments named the Adaptive Job Allocation Strategy (AJAS), in which a self‐scheduling scheme is applied in the scheduler to dispatch jobs to the most appropriate computing resources. Our strategy focuses on increasing resource utility by dispatching jobs to computing nodes with similar performance capacities. By doing so, execution times among all nodes can be equalized. The experimental results show that AJAS can improve the system performance. Copyright © 2011 John Wiley & Sons, Ltd.     

PARMON: a portable and scalable monitoring system for clusters

Workstation/PC clusters have become a cost‐effective solution for high performance computing. C‐DAC's PARAM 10000 (or OpenFrame, internal code name) is a large cluster of high‐performance workstations interconnected through low‐latency and high bandwidth networks. The management and control of such a huge system is a tedious and challenging task since workstations/PCs are typically designed to work as a standalone system rather than part of a cluster. We have designed and developed a tool called PARMON that allows effective monitoring and control of large clusters. It supports the monitoring of critical system resource activities and their utilization at three different levels: entire system, node and component level. It also allows the monitoring of multiple instances of the same component; for instance, multiple processors in SMP type cluster nodes. PARMON is a portable, flexible, interactive, scalable, location‐transparent, and comprehensive environment based on client–server technology. The major components of PARMON are parmon‐server—system resource activities and utilization information provider and parmon‐client—a GUI based client responsible for interacting with parmon‐server and users for data gathering in real‐time and presenting information graphically for visualization. The client is developed as a Java application and the server is developed as a multithreaded server using C and POSIX/Solaris threads since Java does not support interfaces to access system internals. PARMON is regularly used to monitor PARAM 10000 supercomputer, a cluster of 48+ Ultra‐4 workstations powered by the Solaris operating system. The recent popularity of Beowulf‐class clusters (dedicated Linux clusters) in terms of price–performance ratio has motivated us to port PARMON to Linux (accomplished by porting system dependent portions of parmon‐server). This enables management/monitoring of both Solaris and Linux‐based clusters (federated clusters) through a single user interface. Copyright © 2000 John Wiley & Sons, Ltd.     

An optimization approach for decentralized QoS‐based scheduling based on utility and pricing in Grid computing

This paper presents an optimization approach for decentralized Quality of Service (QoS)‐based scheduling based on utility and pricing in Grid computing. The paper assumes that the quality dimensions can be easily formulated as utility functions to express quality preferences for each task agent. The utility values are calculated by the user‐supplied utility function that can be formulated with the task parameters. The QoS constraint Grid resource scheduling problem is formulated into a utility optimization problem. The QoS‐based Grid resource scheduling optimization is decomposed into two subproblems by applying the Lagrangian method. In the Grid, a Grid task agent acts as a consumer paying for the Grid resource and the resource providers receive profits from task agents. A pricing‐based QoS scheduling algorithm is used to perform optimally decentralized QoS‐based resource scheduling. The experiments investigate the effect of the QoS metrics on the global utility and compare the performance of the proposed algorithm with other economical Grid resource scheduling algorithms. Copyright © 2006 John Wiley & Sons, Ltd.     

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

     

Object-oriented product metrics: A generic framework

In spite of considerable prior research, a generic framework has not emerged for structuring work on object-oriented (OO) metrics. We propose such a framework (Generic Framework) for object-oriented product metrics. The framework captures the generic structure of the underlying metrics space (Metrics Space) based on a mereological and set theoretic perspective of the building blocks of OO systems and relational measurement theory. We validate the framework by applying it to a repository of about 350 product metrics. The validation shows that the framework does, indeed, capture the underlying metrics space, and can be useful in identifying gaps and additional metrics that can extend the manner in which Metrics Space is currently populated.     

Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment

Energy consumption in cloud data centers is increasing as the use of such services increases. It is necessary to propose new methods of decreasing energy consumption. Green cloud computing helps to reduce energy consumption and significantly decreases both operating costs and greenhouse gas emissions. Scheduling the enormous number of user-submitted workflow tasks is an important aspect of cloud computing. Resources in cloud data centers should compute these tasks using energy efficient techniques. This paper proposed a new energy-aware scheduling algorithm for time-constrained workflow tasks using the DVFS method in which the host reduces the operating frequency using different voltage levels. The goal of this research is to reduce energy consumption and SLA violations and improve resource utilization. The simulation results show that the proposed method performs more efficiently when evaluating metrics such as energy utilization, average execution time, average resource utilization and average SLA violation.     

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high‐performance applications, such as local clusters, high‐performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads; hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.