A methodology for full-system power modeling in heterogeneous data centers

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft

VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

Web Workload Generation According to the UniLoG Approach

Generating synthetic loads which are suffciently close to reality represents an important and challenging task in performance and quality-of-service (QoS) evaluations of computer networks and distributed systems. Here, the load to be generated represents sequences of requests at a well-defined service interface within a network node. The paper presents a tool (UniLoG.HTTP) which can be used in a flexible manner to generate realistic and representative server and network loads, in terms of access requests to Web servers as well as creation of typical Web traffic within a communication network. The paper describes the architecture of this load generator, the critical design decisions and solution approaches which allowed us to obtain the desired flexibility

Internet performance modeling: the state of the art at the turn of the century

Seemingly overnight, the Internet has gone from an academic experiment to a worldwide information matrix. Along the way, computer scientists have come to realize that understanding the performance of the Internet is a remarkably challenging and subtle problem. This challenge is all the more important because of the increasingly significant role the Internet has come to play in society. To take stock of the field of Internet performance modeling, the authors organized a workshop at Schloß Dagstuhl. This paper summarizes the results of discussions, both plenary and in small groups, that took place during the four-day workshop. It identifies successes, points to areas where more work is needed, and poses “Grand Challenges” for the performance evaluation community with respect to the Internet

Investigating Emerging Security Threats in Clouds and Data Centers

Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale of a data center also brings new security threats. This dissertation studies emerging security issues in clouds and data centers from different aspects, including low-level cooling infrastructures and different virtualization techniques such as container and virtual machine (VM). We first unveil a new vulnerability called reduced cooling redundancy that might be exploited to launch thermal attacks, resulting in severely worsened thermal conditions in a data center. Such a vulnerability is caused by the wide adoption of aggressive cooling energy saving policies. We conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can negatively impact the thermal conditions and reliability of victim servers, significantly raise the cooling cost, and even lead to cooling failures. Finally, we propose effective defenses to mitigate thermal attacks. We then perform a systematic study to understand the security implications of the information leakage in multi-tenancy container cloud services. Due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, a spectrum of system-wide host information is exposed to the containers, including host-system state information and individual process execution information. By exploiting such leaked host information, malicious adversaries can easily launch advanced attacks that can seriously affect the reliability of cloud services. Additionally, we discuss the root causes of the containers\u27 information leakage and propose a two-stage defense approach. The experimental results show that our defense is effective and incurs trivial performance overhead. Finally, we investigate security issues in the existing VM live migration approaches, especially the post-copy approach. While the entire live migration process relies upon reliable TCP connectivity for the transfer of the VM state, we demonstrate that the loss of TCP reliability leads to VM live migration failure. By intentionally aborting the TCP connection, attackers can cause unrecoverable memory inconsistency for post-copy, significantly increase service downtime, and degrade the running VM\u27s performance. From the offensive side, we present detailed techniques to reset the migration connection under heavy networking traffic. From the defensive side, we also propose effective protection to secure the live migration procedure

SRPT Scheduling for Web Servers

This note briey summarizes some results from two papers: [4] and [23]. These papers pose the following question: Is it possible to reduce the expected response time of every request at a web server, simply by changing the order in which we schedule the requests? In [4] we approach this question analytically via an M/G/1 queue. In [23] we approach the same question via implementation involving an Apache web server running on Linux

Energy Efficient Ethernet on MapReduce Clusters: Packet Coalescing To Improve 10GbE Links

An important challenge of modern data centers is to reduce energy consumption, of which a substantial proportion is due to the network. Switches and NICs supporting the recent energy efficient Ethernet (EEE) standard are now available, but current practice is to disable EEE in production use, since its effect on real world application performance is poorly understood. This paper contributes to this discussion by analyzing the impact of EEE on MapReduce workloads, in terms of performance overheads and energy savings. MapReduce is the central programming model of Apache Hadoop, one of the most widely used application frameworks in modern data centers. We find that, while 1GbE links (edge links) achieve good energy savings using the standard EEE implementation, optimum energy savings in the 10 GbE links (aggregation and core links) are only possible, if these links employ packet coalescing. Packet coalescing must, however, be carefully configured in order to avoid excessive performance degradation. With our new analysis of how the static parameters of packet coalescing perform under different cluster loads, we were able to cover both idle and heavy load periods that can exist on this type of environment. Finally, we evaluate our recommendation for packet coalescing for 10 GbE links using the energy-delay metric. This paper is an extension of our previous work [1], which was published in the Proceedings of the 40th Annual IEEE Conference on Local Computer Networks (LCN 2015).This work was supported in part by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant 610456 (EUROSERVER), in part by the Spanish Government through the Severo Ochoa programme (SEV-2011-00067 and SEV-2015-0493), in part by the Spanish Ministry of Economy a nd Competitiveness under Contract TIN2012-34557 and Contract TIN2015-65316-P, and in part by the Generalitat de Catalunya under Contract 2014-SGR-1051 and Contract 2014-SGR-1272.Peer ReviewedPostprint (author's final draft