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1. Modeling and Mitigating Security Threats in Network Functions Virtualization (NFV)

   https://doi.org/10.1007/978-3-030-49669-2_1  

   Alhebaishi, N. ; Wang, L. ; Jajodia, S. ( June 2020 , Proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec 2020))

   Singhal, A. ; Vaidya, J. (Ed.)

   By virtualizing proprietary hardware networking devices, Network Functions Virtualization (NFV) allows agile and cost-effective deployment of diverse network services for multiple tenants on top of the same physical infrastructure. As NFV relies on virtualization, and as an NFV stack typically involves several levels of abstraction and multiple co-resident tenants, this new technology also unavoidably leads to new security threats. In this paper, we take the first step toward modeling and mitigating security threats unique to NFV. Specifically, we model both cross-layer and co-residency attacks on the NFV stack. Additionally, we mitigate such threats through optimizing the virtual machine (VM) placement with respect to given constraints. The simulation results demonstrate the effectiveness of our solution. 

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2. HYPER: A Hybrid High-Performance Framework for Network Function Virtualization

   https://doi.org/10.1109/JSAC.2017.2760438  

   Sun, Chen ; Bi, Jun ; Zheng, Zhilong ; Hu, Hongxin ( October 2017 , IEEE Journal on Selected Areas in Communications)

   Network function virtualization (NFV) offers the potential for both enhancing service delivery flexibility and reducing overall costs by virtualizing network functions that are traditionally implemented in dedicated hardware. However, the flexibility of NFV comes with considerable compromises since virtual machine carried functions could introduce significant performance overhead. In this paper, we present a novel high-performance framework called HYPER, which combines programmable hardware infrastructure and traditional software infrastructure in NFV to achieve both high performance and flexibility for supporting virtualized network functions (VNFs). In HYPER, we design a mediator layer to hide underlying infrastructure heterogeneity from the NFV orchestrator to simplify VNF management. In addition, we design a SLA-aware service chaining algorithm in HYPER to leverage the benefits of the hybrid infrastructure to fulfill both functional and performance requirements from service subscribers (or tenants). To optimize resource utilization efficiency, we also introduce a performance-aware VNF placement algorithm in HYPER, which accommodates both resource and performance requirements in placing VNFs. We implement HYPER in a testbed based on OpenStack and ONetCard. Experimental results show that HYPER reduces the forwarding latency of a service chain by 40% to 67% compared with data plane development kit -based implementation, while maintaining the flexibility of VNF management. 

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3. Experiences on Clustering High-Dimensional Data using pbdR

   https://doi.org/10.1145/3144763.3144768  

   Amreen, Sadika ; Mockus, Audris ( January 2017 , Proceedings of the 1st International Workshop on Software Engineering for High Performance Computing in Computational and Data-enabled Science & Engineering)

   Motivation: Software engineering for High Performace Computing (HPC) environments in general [1] and for big data in particular [5] faces a set of unique challenges including high complexity of middleware and of computing environments. Tools that make it easier for scientists to utilize HPC are, therefore, of paramount importance. We provide an experience report of using one of such highly effective middleware pbdR [9] that allow the scientist to use R programming language without, at least nominally, having to master many layers of HPC infrastructure, such as OpenMPI [4] and ScalaPACK [2]. Objective: to evaluate the extent to which middleware helps improve scientist productivity, we use pbdR to solve a real problem that we, as scientists, are investigating. Our big data comes from the commits on GitHub and other project hosting sites and we are trying to cluster developers based on the text of these commit messages. Context: We need to be able to identify developer for every commit and to identify commits for a single developer. Developer identifiers in the commits, such as login, email, and name are often spelled in multiple ways since that information may come from different version control systems (Git, Mercurial, SVN, ...) and may depend on which computer is used (what is specified in .git/config of the home folder). Method: We train Doc2Vec [7] model where existing credentials are used as a document identifier and then use the resulting 200-dimensional vectors for the 2.3M identifiers to cluster these identifiers so that each cluster represents a specific individual. The distance matrix occupies 32TB and, therefore, is a good target for HPC in general and pbdR in particular. pbdR allows data to be distributed over computing nodes and even has implemented K-means and mixture-model clustering techniques in the package pmclust. Results: We used strategic prototyping [3] to evaluate the capabilities of pbdR and discovered that a) the use of middleware required extensive understanding of its inner workings thus negating many of the expected benefits; b) the implemented algorithms were not suitable for the particular combination of n, p, and k (sample size, data dimension, and the number of clusters); c) the development environment based on batch jobs increases development time substantially. Conclusions: In addition to finding from Basili et al., we find that the quality of the implementation of HPC infrastructure and its development environment has a tremendous effect on development productivity. 

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4. EdgeNet: A Lightweight Scalable Edge Cloud

   Cappos, Justin ; Hemmings, Matthew ; McGeer, Rick ; Rafetseder, Albert ; Ricart, Glenn ( October 2018 , Symposium on Edge Computing)

   This paper describes EdgeNet, a lightweight cloud infrastructure for the edge. We aim to bring as much of the flexibility of open cloud computing as possible to a very lightweight, easily-deployed, software-only edge infrastructure. EdgeNet has been informed by the advances of cloud computing and the successes of such distributed systems as PlanetLab, GENI, G-Lab, SAVI, and V-Node: a large number of small points-of-presence, designed for the deployment of highly distributed experiments and applications. EdgeNet differs from its predecessors in two significant areas: first, it is a software-only infrastructure, where each worker node is designed to run part- or full-time on existing hardware at the local site; and, second, it uses modern, industry-standard software both as the node agent and the control framework. The first innovation permits rapid and unlimited scaling: whereas GENI and PlanetLab required the installation and maintenance of dedicated hardware at each site, EdgeNet requires only a software download, and a node can be added to the EdgeNet infrastructure in 15 minutes. The second offers performance, maintenance, and training benefits; rather than maintaining bespoke kernels and control frameworks, and developing training materials on using the latter, we are able to ride the wave of open-source and industry development, and the plethora of industry and community tutorial materials developed for industry standard control frameworks. The result is a global Kubernetes cluster, where pods of Docker containers form the service instances at each point of presence. 

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5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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