170 results found
Stewart I, Ilie D, Zamyatin A, et al., 2018, Committing to Quantum Resistance: A Slow Defence for Bitcoin against a Fast Quantum Computing Attack., IACR Cryptology ePrint Archive, Vol: 2018, Pages: 213-213
Zamyatin A, Stifter N, Judmayer A, et al., 2018, (Short Paper) A Wild Velvet Fork Appears! Inclusive Blockchain Protocol Changes in Practice., Pages: 87-87
Zamyatin A, Stifter N, Schindler P, et al., 2018, Flux: Revisiting Near Blocks for Proof-of-Work Blockchains.
, 2018, Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, ICPE 2018, Berlin, Germany, April 09-13, 2018, Publisher: ACM
Mora SV, Knottenbelt WJ, 2017, Deep Learning for Domain-Specific Action Recognition in Tennis, 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 170-178, ISSN: 2160-7508
Pesu T, Knottenbelt WJ, 2017, Optimising hidden stochastic PERT networks, Pages: 133-136
Copyright © 2016 EAI. This paper introduces a technique for minimising subtask dispersion in hidden stochastic PERT networks. The technique improves on existing research in two ways. Firstly, it enables subtask dispersion reduction in DAG structures, whereas previous techniques have only been applicable to single-layer split-merge or fork-join systems. Secondly, the exact distributions of subtask processing times do not need to be known, so long as there is some means of generating samples. The technique is further extended to use a metric which trades off subtask dispersion and task response time.
Zamyatin A, Wolter K, Werner S, et al., 2017, Swimming with Fishes and Sharks: Beneath the Surface of Queue-based Ethereum Mining Pools, 25th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Publisher: IEEE COMPUTER SOC, Pages: 99-109, ISSN: 1526-7539
Harrison PG, Patel NM, Knottenbelt WJ, 2016, Energy--Performance Trade-Offs via the EP Queue, ACM Transactions on Modeling and Performance Evaluation of Computing Systems, Vol: 1, Pages: 1-31, ISSN: 2376-3639
Haughian G, Osman R, Knottenbelt WJ, 2016, Benchmarking Replication in Cassandra and MongoDB NoSQL Datastores, 27th International Conference on Database and Expert Systems Applications (DEXA), Publisher: SPRINGER INT PUBLISHING AG, Pages: 152-166, ISSN: 0302-9743
Kelly J, Knottenbelt WJ, 2016, Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature., CoRR, Vol: abs/1605.00962
Tsimashenka I, Knottenbelt WJ, Harrison PG, 2016, Controlling variability in split-merge systems and its impact on performance, ANNALS OF OPERATIONS RESEARCH, Vol: 239, Pages: 569-588, ISSN: 0254-5330
Wu H, Knottenbelt WJ, Wolter K, et al., 2016, An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing., Publisher: Springer, Pages: 311-328
Bradley J, Knottenbelt W, Thomas N, 2015, Preface, Electronic Notes in Theoretical Computer Science, Vol: 310, Pages: 1-3, ISSN: 1571-0661
Chen X, Knottenbelt WJ, 2015, A performance tree-based monitoring platform for clouds, Pages: 97-98
Copyright © 2015 ACM. Cloud-based software systems are expected to deliver reli- able performance under dynamic workload while eficiently managing resources. Conventional monitoring frameworks provide limited support for exible and intuitive performance queries. In this paper, we present a prototype monitor- ing and control platform for clouds that is a better fit to the characteristics of cloud computing (e.g. extensible, user- defined, scalable). Service Level Objectives (SLOs) are ex- pressed graphically as Performance Trees, while violated SLOs trigger mitigating control actions.
Chen X, Rupprecht L, Osman R, et al., 2015, CloudScope: Diagnosing and Managing Performance Interference in Multi-Tenant Clouds, 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Publisher: IEEE, Pages: 164-173, ISSN: 1526-7539
Kelly J, Knottenbelt WJ, 2015, Neural NILM: Deep Neural Networks Applied to Energy Disaggregation., CoRR, Vol: abs/1507.06594
Kelly J, Knottenbelt WJ, 2015, Neural NILM: Deep Neural Networks Applied to Energy Disaggregation., Publisher: ACM, Pages: 55-64
Nika M, Wilding T, Fiems D, et al., 2015, Going Multi-viral: Synthedemic Modelling of Internet-based Spreading Phenomena., ICST Trans. Ambient Systems, Vol: 2, Pages: e4-e4
Parson O, Fisher G, Hersey A, et al., 2015, Dataport and NILMTK: A Building Data Set Designed for Non-intrusive Load Monitoring, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Publisher: IEEE, Pages: 210-214
Pesu T, Knottenbelt WJ, 2015, Dynamic Subtask Dispersion Reduction in Heterogeneous Parallel Queueing Systems, ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, Vol: 318, Pages: 129-142, ISSN: 1571-0661
Wu H, Knottenbelt WJ, Wolter K, 2015, Analysis of the Energy-Response Time Tradeoff for Mobile Cloud Offloading Using Combined Metrics., Publisher: IEEE, Pages: 134-142
, 2015, 8th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2014, Bratislava, Slovakia, December 9-11, 2014, Publisher: ICST
, 2015, Computer Performance Engineering - 12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings, Publisher: Springer
, 2015, An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing., CoRR, Vol: abs/1510.07986
Batra N, Kelly J, Parson O, et al., 2014, NILMTK: an open source toolkit for non-intrusive load monitoring., Publisher: ACM, Pages: 265-276
Chen X, Ho CP, Osman R, et al., 2014, Understanding, modelling, and improving the performance of web applications in multicore virtualised environments, Pages: 197-207
As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multi-core virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26%. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
The modern world features a plethora of social, technological and biological epidemic phenomena. These epidemics now spread at unprecedented rates thanks to advances in industrialisation, transport and telecommunications. Effective real-time decision making and management of modern epidemic outbreaks depends on the two factors: the ability to determine epidemic parameters as the epidemic unfolds, and the ability to characterise rigorously the uncertainties inherent in these parameters. This paper presents a generic maximum- likelihoodbased methodology for online epidemic fitting of SIR models from a single trace which yields confidence intervals on parameter values. The method is fully automated and avoids the laborious manual efforts traditionally deployed in the modelling of biological epidemics. We present case studies based on both synthetic and real data. © 2014 Springer International Publishing.
NoSQL databases have emerged as a backend to support Big Data applications. NoSQL databases are characterized by horizontal scalability, schema-free data models, and easy cloud deployment. To avoid overprovisioning, it is essential to be able to identify the correct number of nodes required for a specific system before deployment. This paper benchmarks and compares three of the most common NoSQL databases: Cassandra, MongoDB and HBase. We deploy them on the Amazon EC2 cloud platform using different types of virtual machines and cluster sizes to study the effect of different configurations. We then compare the behavior of these systems to high-level queueing network models. Our results show that the models are able to capture the main performance characteristics of the studied databases and form the basis for a capacity planning tool for service providers and service users. © 2014 Springer International Publishing.
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