2,972 research outputs found
An Experimental Evaluation of Datacenter Workloads On Low-Power Embedded Micro Servers
This paper presents a comprehensive evaluation of an ultra-low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5Ă— improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.This research was supported in part by NSF grant 13-20209.Ope
ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization
ROOT is an object-oriented C++ framework conceived in the high-energy physics
(HEP) community, designed for storing and analyzing petabytes of data in an
efficient way. Any instance of a C++ class can be stored into a ROOT file in a
machine-independent compressed binary format. In ROOT the TTree object
container is optimized for statistical data analysis over very large data sets
by using vertical data storage techniques. These containers can span a large
number of files on local disks, the web, or a number of different shared file
systems. In order to analyze this data, the user can chose out of a wide set of
mathematical and statistical functions, including linear algebra classes,
numerical algorithms such as integration and minimization, and various methods
for performing regression analysis (fitting). In particular, ROOT offers
packages for complex data modeling and fitting, as well as multivariate
classification based on machine learning techniques. A central piece in these
analysis tools are the histogram classes which provide binning of one- and
multi-dimensional data. Results can be saved in high-quality graphical formats
like Postscript and PDF or in bitmap formats like JPG or GIF. The result can
also be stored into ROOT macros that allow a full recreation and rework of the
graphics. Users typically create their analysis macros step by step, making use
of the interactive C++ interpreter CINT, while running over small data samples.
Once the development is finished, they can run these macros at full compiled
speed over large data sets, using on-the-fly compilation, or by creating a
stand-alone batch program. Finally, if processing farms are available, the user
can reduce the execution time of intrinsically parallel tasks - e.g. data
mining in HEP - by using PROOF, which will take care of optimally distributing
the work over the available resources in a transparent way
Efficient Hash-routing and Domain Clustering Techniques for Information-Centric Networks
Hash-routing is a well-known technique used in server-cluster environments to direct content requests to the responsible servers hosting the requested content. In this work, we look at hash-routing from a different angle and apply the technique to Information-Centric Networking (ICN) environments, where in-network content caches serve as temporary storage for content. In particular, edge-domain routers re-direct requests to in-network caches, more often than not off the shortest path, according to the hash-assignment function. Although the benefits of this off-path in-network caching scheme are significant (e.g., high cache hit rate with minimal co-ordination overhead), the basic scheme comes with disadvantages. That is, in case of very large domains the off-path detour of requests might increase latency to prohibitive levels. In order to deal with extensive detour delays, we investigate nodal/domain clustering techniques, according to which large domains are split in clusters, which in turn apply hash-routing in the subset of nodes of each cluster. We model and evaluate the behaviour of nodal clustering and report significant improvement in delivery latency, which comes at the cost of a slight decrease in cache hit rates (i.e., up to 50% improvement in delivery latency for less than 10% decrease in cache hit rate compared to the original hash-routing scheme applied in the whole domain)
Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining
Numerous stream mining applications, such as visual detection, online patient monitoring, and video search and retrieval, are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness (i.e., delay) constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware presents new opportunities for energy saving as well as challenges. For example, employing low-performance processing nodes can save energy but may violate delay requirements, whereas employing high-performance processing nodes can deliver a fast response but may unnecessarily waste energy. Existing scheduling algorithms balance energy versus delay assuming constant processing and power requirements throughout the execution of a stream mining task and without exploiting hardware heterogeneity. In this paper, we propose a novel framework for dynamic scheduling for energy minimization (DSE) that leverages this emerging hardware heterogeneity. By optimally determining the processing speeds for hardware executing classifiers, DSE minimizes the average energy consumption while satisfying an average delay constraint. To assess the performance of DSE, we build a face detection application based on the Viola-Jones classifier chain and conduct experimental studies via heterogeneous processor system emulation. The results show that, under the same delay requirement, DSE reduces the average energy consumption by up to 50% in comparison to conventional scheduling that does not exploit hardware heterogeneity. We also demonstrate that DSE is robust against processing node switching overhead and model inaccuracy
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