92 research outputs found
Towards Performance Related Decision Support for Model Driven Engineering of Enterprise SOA Applications
While architecture description languages (ADLs) have gained wide acceptance in the research community as a
means of describing system designs, the uptake within the service-oriented architecture (SOA) domain has been slower
than might have been expected. A contributory cause may be the perceived lack of flexibility and, as yet, the limited tool
support. This chapter describes ALI, a new ADL that aims to address these deficiencies by providing a rich, extensible
and flexible syntax for describing component and service interface types and the use of patterns and meta-information.
These enhanced capabilities are intended to encourage more widespread ADL usage
FairGV: Fair and Fast GPU Virtualization
Increasingly high-performance computing (HPC) application developers are opting to use cloud resources due to higher availability. Virtualized GPUs would be an obvious and attractive option for HPC application developers using cloud hosting services. Unfortunately, existing GPU virtualization software is not ready to address fairness, utilization, and performance limitations associated with consolidating mixed HPC workloads. This paper presents FairGV, a radically redesigned GPU virtualization system that achieves system-wide weighted fair sharing and strong performance isolation in mixed workloads that use GPUs with variable degrees of intensity. To achieve its objectives, FairGV introduces a trap-less GPU processing architecture, a new fair queuing method integrated with work-conserving and GPU-centric co-scheduling polices, and a collaborative scheduling method for non-preemptive GPUs. Our prototype implementation achieves near ideal fairness (? 0.97 Min-Max Ratio) with little performance degradation (? 1.02 aggregated overhead) in a range of mixed HPC workloads that leverage GPUs
CONTINUER : maintaining distributed DNN services during edge failures
Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may result in cascading failures that will adversely impact the delivery of the service and will result in failure to meet specific objectives. The impact of these failures needs to be minimised at runtime. Three techniques are explored in this paper, namely repartitioning, early-exit and skip-connection. When an edge node fails, the repartitioning technique will repartition and redeploy the DNN thus avoiding the failed nodes. The early exit technique makes provision for a request to exit (early)before the failed node. The skip connection technique dynamically routes the request by skipping the failed nodes. This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives(accuracy, latency and downtime thresholds) when an edge node fails. To this end, CONTINUER is developed. Two key activities of the framework are estimating the accuracy and latency when using the techniques for distributed DNNs and selecting the best technique. It is demonstrated on a lab-based experimental testbed that CONTINUER estimates accuracy and latency when using the techniques with no more than an average error of 0.28% and13.06%, respectively and selects the suitable technique with a low overhead of no more than 16.82 milliseconds and an accuracy of up to 99.86%.Postprin
Towards an Automated Evaluation Process for Software Architectures
Optimizing and editing enterprise software systems, after
the implementation process has started, is widely
recognized to be an expensive process. This has led to
increasing emphasis on locating mistakes within software
systems at the design stage, to help minimize
development costs. There is increasing interest in the field
of architecture evaluation techniques that can identify
problems at the design stage, either within complete, or
partially complete architectures. Most current techniques
rely on manual review-based evaluation methods that
require advanced skills from architects and evaluators.
We are currently considering what a formal Architecture
Description Language (ADL) can contribute to the
process of architecture eval uation and validation. Our
investigation is considering the inter-relationships
between the activities performed during the architecture
evaluation process, the characteristics an ADL should
possess to support these activities, and the tools needed to
provide convenient access to, and presentation of
architectural information
The contribution of architecture description languages to the evaluation of software architectures
Identifying limitations and mistakes within software
architectures at the design stage is often cost-
efficient and reduces the overall systemâs
development and marketing time. A number of
techniques have emerged over recent years, for
assessing both single-systems, and product-line
architectures. These techniques do not assume any
particular format or language for the description of
the architecture. Often however, they do require the
ability to extract a range of information from the
architecture description. In this research, we looked
at the relationships between the features that might
be provided by a formal architecture description
language (ADL), and the information required for
architecture assessment purposes. We also designed
a set of visual tools for use within the architecture
development and assessment process in order to
alleviate and aid the human part of the process
ADLARS: An Architecture Description Language for Software Product Lines
Software Product Line (SPL) Engineering has
emerged to become a mature domain for maximizing
reuse within the context of a family of related software
products. Within the process of SPL, the variability
and commonality among the different products within
the scope of a family is captured and modeled into a
systemâs âfeature modelâ. Currently, there are no
Architecture Description Languages (ADLs) that
support the relationship between the feature model
domain and the system architecture domain, leaving a
gap which significantly increases the complexity of
analyzing the systemâs architecture and insuring that it
complies with its set feature model and variability
requirements. In this paper we present ADLARS, an
Architecture Description Language that supports the
relationship between the systemâs feature model and
the architectural structures in an attempt to alleviate
the aforementioned problem. The link between the two
spaces also allows the automatic generation of product
architectures from the family reference architecture
DNNShifter : an efficient DNN pruning system for edge computing
Funding: This research is funded by Rakuten Mobile, Japan .Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which has a significant resource footprint. This presents a challenge for resources operating at the extreme edge of the network, such as mobile and embedded devices that have limited computational and memory resources. To address this, models are pruned to create lightweight, more suitable variants for these devices. Existing pruning methods are unable to provide similar quality models compared to their unpruned counterparts without significant time costs and overheads or are limited to offline use cases. Our work rapidly derives suitable model variants while maintaining the accuracy of the original model. The model variants can be swapped quickly when system and network conditions change to match workload demand. This paper presents DNNShifter  , an end-to-end DNN training, spatial pruning, and model switching system that addresses the challenges mentioned above. At the heart of DNNShifter  is a novel methodology that prunes sparse models using structured pruning - combining the accuracy-preserving benefits of unstructured pruning with runtime performance improvements of structured pruning. The pruned model variants generated by DNNShifter  are smaller in size and thus faster than dense and sparse model predecessors, making them suitable for inference at the edge while retaining near similar accuracy as of the original dense model. DNNShifter  generates a portfolio of model variants that can be swiftly interchanged depending on operational conditions. DNNShifter  produces pruned model variants up to 93x faster than conventional training methods. Compared to sparse models, the pruned model variants are up to 5.14x smaller and have a 1.67x inference latency speedup, with no compromise to sparse model accuracy. In addition, DNNShifter  has up to 11.9x lower overhead for switching models and up to 3.8x lower memory utilisation than existing approaches. DNNShifter  is available for public use from https://github.com/blessonvar/DNNShifter.Publisher PDFPeer reviewe
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