2,678 research outputs found
High Performance Web Servers: A Study In Concurrent Programming Models
With the advent of commodity large-scale multi-core computers, the performance of software running on these computers has become a challenge to researchers and enterprise developers. While academic research and industrial products have moved in the direction of writing scalable and highly available services using distributed computing, single machine performance remains an active domain, one which is far from saturated.
This thesis selects an archetypal software example and workload in this domain, and describes software characteristics affecting performance. The example is highly-parallel web-servers processing a static workload. Particularly, this work examines concurrent programming models in the context of high-performance web-servers across different architectures — threaded (Apache, Go and μKnot), event-driven (Nginx, μServer) and staged (WatPipe) — compared with two static workloads in two different domains. The two workloads are a Zipf distribution of file sizes representing a user session pulling an assortment of many small and a few large files, and a 50KB file representing chunked streaming of a large audio or video file. Significant effort is made to fairly compare eight web-servers by carefully tuning each via their adjustment parameters. Tuning plays a significant role in workload-specific performance. The two domains are no disk I/O (in-memory file set) and medium disk I/O. The domains are created by lowering the amount of RAM available to the web-server from 4GB to 2GB, forcing files to be evicted from the file-system cache. Both domains are also restricted to 4 CPUs.
The primary goal of this thesis is to examine fundamental performance differences between threaded and event-driven concurrency models, with particular emphasis on user-level threading models. Additionally, a secondary goal of the work is to examine high-performance software under restricted hardware environments. Over-provisioned hardware environments can mask architectural and implementation shortcomings in software – the hypothesis in this work is that restricting resources stresses the application, bringing out important performance characteristics and properties. Experimental results for the given workload show that memory pressure is one of the most significant factors for the degradation of web-server performance, because it forces both the onset and amount of disk I/O. With an ever increasing need to support more content at faster rates, a web-server relies heavily on in-memory caching of files and related content. In fact, personal and small business web-servers are even run on minimal hardware, like the Raspberry Pi, with only 1GB of RAM and a small SD card for the file system. Therefore, understanding behaviour and performance in restricted contexts should be a normal aspect of testing a web server (and other software systems)
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous
applications owing to their outstanding ability in extracting latent
representation on graph structures. To render GNN-based service for IoT-driven
smart applications, traditional model serving paradigms usually resort to the
cloud by fully uploading geo-distributed input data to remote datacenters.
However, our empirical measurements reveal the significant communication
overhead of such cloud-based serving and highlight the profound potential in
applying the emerging fog computing. To maximize the architectural benefits
brought by fog computing, in this paper, we present Fograph, a novel
distributed real-time GNN inference framework that leverages diverse and
dynamic resources of multiple fog nodes in proximity to IoT data sources. By
introducing heterogeneity-aware execution planning and GNN-specific compression
techniques, Fograph tailors its design to well accommodate the unique
characteristics of GNN serving in fog environments. Prototype-based evaluation
and case study demonstrate that Fograph significantly outperforms the
state-of-the-art cloud serving and fog deployment by up to 5.39x execution
speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin
Smart Building Data Collection and Ventilation System Energy Prediction
Data has the potential to transform our environments for the better if utilized to its full
potential. A highly interesting use case of data is in relation to Smart Buildings, where
IoT technology presents new possibilities. With appropriate collection and structuring
of the available data, many new opportunities present themselves.
In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To
illustrate the potential in the data, one specific problem is researched, namely that of
indoor climate optimization and its effects on energy usage. The problem description
and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model
predictive control and various machine learning methods to predict energy usage.
For a one day simulation, the proposed optimization strategy yields a 174.86% increase
in energy usage. The conducted work indicates that the proposed model identification
technique is unsuitable for the underlying data utilized in this work. The proposed
model predictive control strategy and machine learning methods contain promising results
Smart Building Data Collection and Ventilation System Energy Prediction
Data has the potential to transform our environments for the better if utilized to its full
potential. A highly interesting use case of data is in relation to Smart Buildings, where
IoT technology presents new possibilities. With appropriate collection and structuring
of the available data, many new opportunities present themselves.
In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To
illustrate the potential in the data, one specific problem is researched, namely that of
indoor climate optimization and its effects on energy usage. The problem description
and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model
predictive control and various machine learning methods to predict energy usage.
For a one day simulation, the proposed optimization strategy yields a 174.86% increase
in energy usage. The conducted work indicates that the proposed model identification
technique is unsuitable for the underlying data utilized in this work. The proposed
model predictive control strategy and machine learning methods contain promising results
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