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Robert Birke - IEEE Xplore Author Profile

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Author details

Robert Birke

Also published under: R. Birke

Publications

81

Citations

1,065

Publications by Year

20052024

Co-Authors:

L. AbeniGiuliano AlbaneseMarco AldinucciAndreea S. AnghelShadi Atalla

Show All Co-Authors (168)

Robert Birke

Also published under: R. Birke

Affiliation

Department of Computer Science, University of Turin, Italy

Publication Topics

Machine Learning,
Federated Learning,
Generative Adversarial Networks,
Inference Attacks,
Local Data,
Model Aggregation,
Side-channel,
Ablation Analysis,
Active Entities,
Adult Dataset,
Adversarial Perturbations,
Application Execution

Biography

Robert Birke (Senior Member, IEEE) received the Ph.D. degree in electronics and communications engineering from the Politecnico di Torino, Italy, in 2009. He is a Tenured Assistant Professor with the University of Turin, Italy. He has been a Visiting Researcher with IBM Research Zurich, Switzerland, and a Principal Scientist with ABB Corporate Research, Switzerland. He has published more than 90 papers at venues related to communication, system performance, and machine learning, e.g., SIGCOMM, SIGMETRICS, FAST, INFOCOM, ACML, and JSAC. His research interests are in the broad area of virtual resource management, including network design, workload characterization, and AI and big-data application optimization.(Based on document published on 1 February 2023).

Publications

81

Citations

1,065

Publications by Year

20052024

Co-Authors:

L. Abeni
Giuliano Albanese
Marco Aldinucci
Andreea S. Anghel
Shadi Atalla

Show All Co-Authors (168)

Author's Published Works

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Showing 1-25 of 81 results

Conferences (66)

Journals (13)

Magazines (2)

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Robert Birke(70)
Lydia Y. Chen(41)
R. Birke(11)
Evgenia Smirni(9)
M. Mellia(7)
IBM Research Zurich, Rüschlikon, Switzerland(7)
TU Delft, Delft, Netherlands(6)
Dipartimento di Elettronica, Politecnico di Turino, Italy(5)
School of Computing, Newcastle University, Newcastle upon Tyne, U.K.(4)
Universidad del Rosario, Bogotá, Colombia(4)
IEEE Transactions on Network and Service Management(6)
2008 International Conference on High Performance Switching and Routing(2)
2016 IEEE International Conference on Autonomic Computing (ICAC)(2)
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)(2)
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications(2)
IEEE(81)
Media(2)
Atlanta, GA, USA(7)
Kassel, Germany(3)
San Francisco, CA, USA(3)
Barcelona, Spain(2)
Budapest, Hungary(2)
Virtual Machines(29)
Arrival Rate(15)
Data Center(14)
Load Balancing(12)
Deep Neural Network(10)

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Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza;Valentina Cesare;Marco Edoardo Santimaria;Robert Birke;Alberto Vecchiato;Ugo Becciani;Marco Aldinucci

SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2024 | Conference Paper |

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Applications that analyze data from modern scientific experiments will soon require a computing capacity of ExaFLOPs. The current trend to achieve such performance is to employ GPU-accelerated supercomputers and design applications to optimally exploit this hardware. Since each supercomputer is typically a one-off project, the necessity of having computational languages portable across diverse CPU...Show More

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza;Valentina Cesare;Marco Edoardo Santimaria;Robert Birke;Alberto Vecchiato;Ugo Becciani;Marco Aldinucci

SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2024 | Conference Paper |

CLUES: Collusive Theft of Conditional Generative Adversarial Networks

Simon Queyrut;Valerio Schiavoni;Lydia Y. Chen;Pascal Felber;Robert Birke

2024 43rd International Symposium on Reliable Distributed Systems (SRDS)

Year: 2024 | Conference Paper |

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Conditional Generative Adversarial Networks (cGANs) are increasingly popular web-based synthesis services accessed through a query API, e.g., cGANs generate a cat image based on a “cat” query. However, cGAN-based synthesizers can be stolen via adversaries' queries, i.e., model thieves. The prevailing adversarial assumption is that thieves act independently: they query the deployed cGAN (i.e., the ...Show More

CLUES: Collusive Theft of Conditional Generative Adversarial Networks

Simon Queyrut;Valerio Schiavoni;Lydia Y. Chen;Pascal Felber;Robert Birke

2024 43rd International Symposium on Reliable Distributed Systems (SRDS)

Year: 2024 | Conference Paper |

A Performance Analysis for Confidential Federated Learning

Bruno Casell;Iacopo Colonnelli;Gianluca Mittone;Robert Birke;Walter Riviera;Antonio Sciarappa;Carlo Cavazzoni;Marco Aldinucci

2024 IEEE Security and Privacy Workshops (SPW)

Year: 2024 | Conference Paper |

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Federated Learning (FL) has emerged as a solution to preserve data privacy by keeping the data locally on each participant’s device. However, FL alone is still vulnerable to attacks that can cause privacy leaks. Therefore, additional security measures, at the cost of increasing runtimes, become necessary. The Trusted Execution Environment (TEE) approach offers the highest degree of security during...Show More

A Performance Analysis for Confidential Federated Learning

Bruno Casell;Iacopo Colonnelli;Gianluca Mittone;Robert Birke;Walter Riviera;Antonio Sciarappa;Carlo Cavazzoni;Marco Aldinucci

2024 IEEE Security and Privacy Workshops (SPW)

Year: 2024 | Conference Paper |

GDTS: GAN-Based Distributed Tabular Synthesizer

Zilong Zhao;Robert Birke;Lydia Y. Chen

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Federated Learning (FL) and Multi-Discriminator (MD) systems has just been demonstrated, it is unknown if tabular GANs can be learned from decentralized data sources. Different from image...Show More

GDTS: GAN-Based Distributed Tabular Synthesizer

Zilong Zhao;Robert Birke;Lydia Y. Chen

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

Year: 2023 | Conference Paper |

A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum

Marco Aldinucci;Robert Birke;Antonio Brogi;Emanuele Carlini;Massimo Coppola;Marco Danelutto;Patrizio Dazzi;Luca Ferrucci;Stefano Forti;Hanna Kavalionak;Gabriele Mencagli;Matteo Mordacchini;Marcelo Pasin;Federica Paganelli;Massimo Torquati

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into auton...Show More

A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum

Marco Aldinucci;Robert Birke;Antonio Brogi;Emanuele Carlini;Massimo Coppola;Marco Danelutto;Patrizio Dazzi;Luca Ferrucci;Stefano Forti;Hanna Kavalionak;Gabriele Mencagli;Matteo Mordacchini;Marcelo Pasin;Federica Paganelli;Massimo Torquati

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

Year: 2023 | Conference Paper |

Permutation-Invariant Tabular Data Synthesis

Yujin Zhu;Zilong Zhao;Robert Birke;Lydia Y. Chen

2022 IEEE International Conference on Big Data (Big Data)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first c onduct a...Show More

Permutation-Invariant Tabular Data Synthesis

Yujin Zhu;Zilong Zhao;Robert Birke;Lydia Y. Chen

2022 IEEE International Conference on Big Data (Big Data)

Year: 2022 | Conference Paper |

Evaluation of Networking Options for Containerized Deployment of Real-Time Applications

Giuliano Albanese;Robert Birke;Georgia Giannopoulou;Sandro Schönborn;Thanikesavan Sivanthi

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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Enterprises in the field of industrial automation experience an increasing demand for providing virtualized software solutions. Inspired by the recent trends in serverless and cloud computing, software virtualization is considered even for safety-critical applications with hard real-time requirements, as a means of avoiding hardware vendor lock-in and reducing volume and maintenance cost of device...Show More

Evaluation of Networking Options for Containerized Deployment of Real-Time Applications

Giuliano Albanese;Robert Birke;Georgia Giannopoulou;Sandro Schönborn;Thanikesavan Sivanthi

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

Year: 2021 | Conference Paper |

Guest Editorial: Special Section on Embracing Artificial Intelligence for Network and Service Management

Hanan Lutfiyya;Robert Birke;Giuliano Casale;Amogh Dhamdhere;Jinho Hwang;Takeru Inoue;Neeraj Kumar;Deepak Puthal;Nur Zincir-Heywood

IEEE Transactions on Network and Service Management

Year: 2021 | Volume: 18, Issue: 4 | Journal Article |

Cited by: Papers (6)

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Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer e...Show More

Guest Editorial: Special Section on Embracing Artificial Intelligence for Network and Service Management

Hanan Lutfiyya;Robert Birke;Giuliano Casale;Amogh Dhamdhere;Jinho Hwang;Takeru Inoue;Neeraj Kumar;Deepak Puthal;Nur Zincir-Heywood

IEEE Transactions on Network and Service Management

Year: 2021 | Volume: 18, Issue: 4 | Journal Article |

LABELNET: Recovering Noisy Labels

Amirmasoud Ghiassi;Robert Birke;Rui Han;Lydia Y. Chen

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

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Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. Recent studies improve the robustness of deep models against noisy labels without the knowledge of true labels. In this paper, we advocate to derive a ...Show More

LABELNET: Recovering Noisy Labels

Amirmasoud Ghiassi;Robert Birke;Rui Han;Lydia Y. Chen

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Masa: Responsive Multi-DNN Inference on the Edge

Bart Cox;Jeroen Galjaard;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

Year: 2021 | Conference Paper |

Cited by: Papers (13)

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Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted via executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. The response times of multi-DNN highly affect users’ quality of experience and safety...Show More

Masa: Responsive Multi-DNN Inference on the Edge

Bart Cox;Jeroen Galjaard;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

Year: 2021 | Conference Paper |

Artifact: Masa: Responsive Multi-DNN Inference on the Edge

Bart Cox;Jeroen Galjaard;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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This artifact is a guideline how the Edgecaffe framework, presented in [1], can be used. Edgecaffe is an open-source Deep Neural Network framework for efficient multi-network inference on edge devices. This framework enables the layer by layer execution and fine-grained control during inference of Deep Neural Networks. Edgecaffe is created to give more fine grained-control over the execution durin...Show More

Artifact: Masa: Responsive Multi-DNN Inference on the Edge

Bart Cox;Jeroen Galjaard;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

Year: 2021 | Conference Paper |

MemA: Fast Inference of Multiple Deep Models

Jeroen Galjaard;Bart Cox;Amirmasoud Ghiassi;Lydia Y. Chen;Robert Birke

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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The execution of deep neural network (DNN) inference jobs on edge devices has become increasingly popular. Multiple of such inference models can concurrently analyse the on-device data, e.g. images, to extract valuable insights. Prior art focuses on low-power accelerators, compressed neural network architectures, and specialized frameworks to reduce execution time of single inference jobs on edge ...Show More

MemA: Fast Inference of Multiple Deep Models

Jeroen Galjaard;Bart Cox;Amirmasoud Ghiassi;Lydia Y. Chen;Robert Birke

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

Year: 2021 | Conference Paper |

Enhancing Robustness of On-Line Learning Models on Highly Noisy Data

Zilong Zhao;Robert Birke;Rui Han;Bogdan Robu;Sara Bouchenak;Sonia Ben Mokhtar;Lydia Y. Chen

IEEE Transactions on Dependable and Secure Computing

Year: 2021 | Volume: 18, Issue: 5 | Journal Article |

Cited by: Papers (4)

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Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this article...Show More

Enhancing Robustness of On-Line Learning Models on Highly Noisy Data

Zilong Zhao;Robert Birke;Rui Han;Bogdan Robu;Sara Bouchenak;Sonia Ben Mokhtar;Lydia Y. Chen

IEEE Transactions on Dependable and Secure Computing

Year: 2021 | Volume: 18, Issue: 5 | Journal Article |

sPARE: Partial Replication for Multi-Tier Applications in the Cloud

Robert Birke;Juan F. Pérez;Zhan Qiu;Mathias Börkqvist;Lydia Y. Chen

IEEE Transactions on Services Computing

Year: 2021 | Volume: 14, Issue: 2 | Journal Article |

Cited by: Papers (1)

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Offering consistent low latency remains a key challenge for distributed applications, especially when deployed on the cloud where virtual machines (VMs) suffer from capacity variability caused by co-located tenants. Replicating redundant requests was shown to be an effective mechanism to defend application performance from high capacity variability. While the prior art centers on single-tier syste...Show More

sPARE: Partial Replication for Multi-Tier Applications in the Cloud

Robert Birke;Juan F. Pérez;Zhan Qiu;Mathias Börkqvist;Lydia Y. Chen

IEEE Transactions on Services Computing

Year: 2021 | Volume: 14, Issue: 2 | Journal Article |

End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models

Taraneh Younesian;Chi Hong;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number of non-expert workers via crowd-sourcing. Due to the varying background and experience of crowd workers, the obtained labels are highly prone to errors and even detrimental to the learning models. In this paper, we advoc...Show More

End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models

Taraneh Younesian;Chi Hong;Amirmasoud Ghiassi;Robert Birke;Lydia Y. Chen

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2020 | Conference Paper |

Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Shuyu Lin;Ronald Clark;Robert Birke;Sandro Schönborn;Niki Trigoni;Stephen Roberts

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

Cited by: Papers (139)

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In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. As a result, our detection algorithm is capable of identifying anomal...Show More

Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

Shuyu Lin;Ronald Clark;Robert Birke;Sandro Schönborn;Niki Trigoni;Stephen Roberts

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2020 | Conference Paper |

Making Neighbors Quiet: An Approach to Detect Virtual Resource Contention

Joel Vallone;Robert Birke;Lydia Y. Chen

IEEE Transactions on Services Computing

Year: 2020 | Volume: 13, Issue: 5 | Journal Article |

Cited by: Papers (4)

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It is imperative for public cloud providers to guarantee performance targets for tenants' virtual machines (VMs) while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, e.g., latency, and a...Show More

Making Neighbors Quiet: An Approach to Detect Virtual Resource Contention

Joel Vallone;Robert Birke;Lydia Y. Chen

IEEE Transactions on Services Computing

Year: 2020 | Volume: 13, Issue: 5 | Journal Article |

Robust (Deep) Learning Framework Against Dirty Labels and Beyond

Amirmasoud Ghiassi;Taraneh Younesian;Zhilong Zhao;Robert Birke;Valerio Schiavoni;Lydia Y. Chen

2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

Year: 2019 | Conference Paper |

Cited by: Papers (8)

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Data is generated with unprecedented speed, due to the flourishing of social media and open platforms. However, due to the lack of scrutinizing, both clean and dirty data are widely spreaded. For instance, there is a significant portion of images tagged with corrupted dirty class labels. Such dirty data sets are not only detrimental to the learning outcomes, e.g., misclassified images into the wro...Show More

Robust (Deep) Learning Framework Against Dirty Labels and Beyond

Amirmasoud Ghiassi;Taraneh Younesian;Zhilong Zhao;Robert Birke;Valerio Schiavoni;Lydia Y. Chen

2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

Year: 2019 | Conference Paper |

Exploring Deep Reinforcement Learning for Autonomous Powerline Tracking

Panin Pienroj;Sandro Schönborn;Robert Birke

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Year: 2019 | Conference Paper |

Cited by: Papers (5)

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Deep reinforcement learning (DRL) has recently received increasing attention due to unprecedented ability achieved in playing games. Since then DRL has been successfully applied across different domains. Here we explore the potential of DRL in autonomous power transmission lines inspection. Regular inspection is essential to ensure an optimal efficiency void of hot spots or corona discharges, but ...Show More

Exploring Deep Reinforcement Learning for Autonomous Powerline Tracking

Panin Pienroj;Sandro Schönborn;Robert Birke

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Year: 2019 | Conference Paper |

Chisel: Reshaping Queries to Trim Latency in Key-Value Stores

Robert Birke;Juan F. Pérez;Sonia Ben Mokhtar;Navaneeth Rameshan;Lydia Y. Chen

2019 IEEE International Conference on Autonomic Computing (ICAC)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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It is challenging for key-value data stores to trim user (tail) latency of requests as the workloads are observed to have skewed number of key-value pairs and commonly retrieved via multiget operation, i.e., all keys at the same time. In this paper we present Chisel, a novel client side solution to efficiently reshape the query size at the data store by adaptively splitting big requests into chunk...Show More

Chisel: Reshaping Queries to Trim Latency in Key-Value Stores

Robert Birke;Juan F. Pérez;Sonia Ben Mokhtar;Navaneeth Rameshan;Lydia Y. Chen

2019 IEEE International Conference on Autonomic Computing (ICAC)

Year: 2019 | Conference Paper |

Robust Anomaly Detection on Unreliable Data

Zilong Zhao;Sophie Cerf;Robert Birke;Bogdan Robu;Sara Bouchenak;Sonia Ben Mokhtar;Lydia Y Chen

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

Year: 2019 | Conference Paper |

Cited by: Papers (24)

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Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT and cloud, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-...Show More

Robust Anomaly Detection on Unreliable Data

Zilong Zhao;Sophie Cerf;Robert Birke;Bogdan Robu;Sara Bouchenak;Sonia Ben Mokhtar;Lydia Y Chen

2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

Year: 2019 | Conference Paper |

SmallTail: Scaling Cores and Probabilistic Cloning Requests for Web Systems

Ewnetu Bayuh Lakew;Robert Birke;Juan F. Perez;Erik Elmroth;Lydia Y. Chen

2018 IEEE International Conference on Autonomic Computing (ICAC)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundan...Show More

SmallTail: Scaling Cores and Probabilistic Cloning Requests for Web Systems

Ewnetu Bayuh Lakew;Robert Birke;Juan F. Perez;Erik Elmroth;Lydia Y. Chen

2018 IEEE International Conference on Autonomic Computing (ICAC)

Year: 2018 | Conference Paper |

Spatial–Temporal Prediction Models for Active Ticket Managing in Data Centers

Ji Xue;Robert Birke;Lydia Y. Chen;Evgenia Smirni

IEEE Transactions on Network and Service Management

Year: 2018 | Volume: 15, Issue: 1 | Journal Article |

Cited by: Papers (18)

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Performance ticket handling is an expensive operation in data centers, where physical boxes host multiple virtual machines (VMs). A large body of tickets arise from resource usage warnings, e.g., CPU and RAM usages that exceed predefined thresholds. The transient nature of CPU and RAM usage as well as their strong correlation across time among co-located VMs within boxes drastically increase the c...Show More

Spatial–Temporal Prediction Models for Active Ticket Managing in Data Centers

Ji Xue;Robert Birke;Lydia Y. Chen;Evgenia Smirni

IEEE Transactions on Network and Service Management

Year: 2018 | Volume: 15, Issue: 1 | Journal Article |

Optimizing for Tail Sojourn Times of Cloud Clusters

Mathias Björkqvist;Natarajan Gautam;Robert Birke;Lydia Y. Chen;Walter Binder

IEEE Transactions on Cloud Computing

Year: 2018 | Volume: 6, Issue: 1 | Journal Article |

Cited by: Papers (5)

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A common pitfall when hosting applications in today's cloud environments is that virtual servers often experience varying execution speeds due to the interference from co-located virtual servers degrading the tail sojourn times specified in service level agreements. Motivated by the significance of tail sojourn times for cloud clusters, we develop a model of N parallel virtual server queues, each ...Show More

Optimizing for Tail Sojourn Times of Cloud Clusters

Mathias Björkqvist;Natarajan Gautam;Robert Birke;Lydia Y. Chen;Walter Binder

IEEE Transactions on Cloud Computing

Year: 2018 | Volume: 6, Issue: 1 | Journal Article |

Bringing 5G into Rural and Low-Income Areas: Is It Feasible?

Luca Chiaraviglio;Nicola Blefari-Melazzi;William Liu;Jairo A. Gutierrez;Jaap van de Beek;Robert Birke;Lydia Chen;Filip Idzikowski;Daniel Kilper;Paolo Monti;Antoine Bagula;Jinsong Wu

IEEE Communications Standards Magazine

Year: 2017 | Volume: 1, Issue: 3 | Magazine Article |

Cited by: Papers (98)

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Nowadays, at least two billion people are experiencing a complete lack of wireless cellular network coverage. These users live in rural areas and low-income regions, where network operators are not keen to invest, mainly due to high capital expenditure and operational expenditure costs, as well as the scarcity of electricity from the grid. We tackle this challenge by proposing a 5G network explici...Show More

Bringing 5G into Rural and Low-Income Areas: Is It Feasible?

Luca Chiaraviglio;Nicola Blefari-Melazzi;William Liu;Jairo A. Gutierrez;Jaap van de Beek;Robert Birke;Lydia Chen;Filip Idzikowski;Daniel Kilper;Paolo Monti;Antoine Bagula;Jinsong Wu

IEEE Communications Standards Magazine

Year: 2017 | Volume: 1, Issue: 3 | Magazine Article |

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