Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Hydrological Models as Web Services: An Implementation using OGC Standards

<p>Presentation for the HIC 2012 - 10th International Conference on Hydroinformatics. "Understanding Changing Climate and Environment and Finding Solutions" Hamburg, Germany July 14-18, 2012</p> <p> </p

Analyzing the Effects of Load Distribution Algorithms on Energy Consumption of Servers in Cloud Data Centers

Cloud computing has become an important driver for IT service provisioning in recent years. It offers additional flexibility to both customers and IT service providers, but also comes along with new challenges for providers. One of the major challenges for providers is the reduction of energy consumption since today, already more than 50% of operational costs in data centers account for energy. A possible way to reduce these costs is to efficiently distribute load within the data center. Although the effect of load distribution algorithms on energy consumption is a topic of recent research, an analysis-framework for evaluating arbitrary load distribution algorithms with regard to their effects on the energy consumption of cloud data centers is still nonexistent. Therefore, in this contribution, a concept of a simulation-based, quantitative analysis-framework for load distribution algorithms in cloud environments with respect to the energy consumption of data centers is developed and evaluated

Early Observations on Performance of Google Compute Engine for Scientific Computing

Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCE's scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazon's EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.Comment: Proceedings of the 5th International Conference on Cloud Computing Technologies and Science (CloudCom 2013), pp. 1-8, Bristol, UK, December 2-5, 201

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%