132 research outputs found
MAC Based Dynamic VLAN Tagging with OpenFlow for WLAN Access Networks
Many network device vendors are providing a vendor specific VLAN based access solutions for WLAN clients. This applications allows network operators to specify WLAN devices which automatically fall into their department specific networks ans allows them to access their local resources like e.g. printers. The configuration of these VLAN mappings is usually manufacturer specific and depends also on the local VLAN policies. However, the presented OpenFlow approach on the other hand presents a solution to encapsulate this functionality as network application. Thus, an architecture, implementation, and evaluation is presented in order to demonstrate that this particular functionality can be easily realized in an OpenFlow network application
Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients
Federated Learning (FL) is a decentralized machine learning approach where
local models are trained on distributed clients, allowing privacy-preserving
collaboration by sharing model updates instead of raw data. However, the added
communication overhead and increased training time caused by heterogenous data
distributions results in higher energy consumption and carbon emissions for
achieving similar model performance than traditional machine learning. At the
same time, efficient usage of available energy is an important requirement for
battery constrained devices. Because of this, many different approaches on
energy-efficient and carbon-efficient FL scheduling and client selection have
been published in recent years. However, most of this research oversimplifies
power performance characteristics of clients by assuming that they always
require the same amount of energy per processed sample throughout training.
This overlooks real-world effects arising from operating devices under
different power modes or the side effects of running other workloads in
parallel. In this work, we take a first look on the impact of such factors and
discuss how better power-performance estimates can improve energy-efficient and
carbon-efficient FL scheduling.Comment: Machine Learning and Networking Workshop, NetSys 202
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
In most works on deep incremental learning research, it is assumed that novel
samples are pre-identified for neural network retraining. However, practical
deep classifiers often misidentify these samples, leading to erroneous
predictions. Such misclassifications can degrade model performance. Techniques
like open set recognition offer a means to detect these novel samples,
representing a significant area in the machine learning domain.
In this paper, we introduce a deep class-incremental learning framework
integrated with open set recognition. Our approach refines class-incrementally
learned features to adapt them for distance-based open set recognition.
Experimental results validate that our method outperforms state-of-the-art
incremental learning techniques and exhibits superior performance in open set
recognition compared to baseline methods
Predicting Dynamic Memory Requirements for Scientific Workflow Tasks
With the increasing amount of data available to scientists in disciplines as
diverse as bioinformatics, physics, and remote sensing, scientific workflow
systems are becoming increasingly important for composing and executing
scalable data analysis pipelines. When writing such workflows, users need to
specify the resources to be reserved for tasks so that sufficient resources are
allocated on the target cluster infrastructure. Crucially, underestimating a
task's memory requirements can result in task failures. Therefore, users often
resort to overprovisioning, resulting in significant resource wastage and
decreased throughput.
In this paper, we propose a novel online method that uses monitoring time
series data to predict task memory usage in order to reduce the memory wastage
of scientific workflow tasks. Our method predicts a task's runtime, divides it
into k equally-sized segments, and learns the peak memory value for each
segment depending on the total file input size. We evaluate the prototype
implementation of our method using workflows from the publicly available
nf-core repository, showing an average memory wastage reduction of 29.48%
compared to the best state-of-the-art approac
Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing?
Distributed dataflow systems such as Apache Spark or Apache Flink enable
parallel, in-memory data processing on large clusters of commodity hardware.
Consequently, the appropriate amount of memory to allocate to the cluster is a
crucial consideration.
In this paper, we analyze the challenge of efficient resource allocation for
distributed data processing, focusing on memory. We emphasize that in-memory
processing with in-memory data processing frameworks can undermine resource
efficiency. Based on the findings of our trace data analysis, we compile
requirements towards an automated solution for efficient cluster resource
allocation.Comment: 4 pages, 3 Figures; ACM SSDBM 202
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