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  1. Book ; Online: SplitPlace

    Tuli, Shreshth

    Intelligent Placement of Split Neural Nets in Mobile Edge Environments

    2021  

    Abstract: In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes ... ...

    Abstract In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing.

    Comment: First Place - Gold Medal at the Student Research Competition at ACM SIGMETRICS Conference 2021
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 000
    Publishing date 2021-10-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: SimTune: bridging the simulator reality gap for resource management in edge-cloud computing.

    Tuli, Shreshth / Casale, Giuliano / Jennings, Nicholas R

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 19158

    Abstract: Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous ... ...

    Abstract Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively.
    MeSH term(s) Humans ; Cloud Computing ; Computer Simulation
    Language English
    Publishing date 2022-11-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-23924-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: SimTune

    Shreshth Tuli / Giuliano Casale / Nicholas R. Jennings

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    bridging the simulator reality gap for resource management in edge-cloud computing

    2022  Volume 12

    Abstract: Abstract Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ... ...

    Abstract Abstract Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively.
    Keywords Medicine ; R ; Science ; Q
    Subject code 670
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: CILP

    Tuli, Shreshth / Casale, Giuliano / Jennings, Nicholas R.

    Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments

    2023  

    Abstract: Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload demands ... ...

    Abstract Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload demands to provision VMs proactively. However, existing AI-based solutions tend to not holistically consider all crucial aspects such as provisioning overheads, heterogeneous VM costs and Quality of Service (QoS) of the cloud system. To address this, we propose a novel method, called CILP, that formulates the VM provisioning problem as two sub-problems of prediction and optimization, where the provisioning plan is optimized based on predicted workload demands. CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores. We extend the neural network to also act as an imitation learner that dynamically decides the optimal VM provisioning plan. A transformer based neural model reduces training and inference overheads while our novel two-phase decision making loop facilitates in making informed provisioning decisions. Crucially, we address limitations of prior work by including resource utilization, deployment costs and provisioning overheads to inform the provisioning decisions in our imitation learning framework. Experiments with three public benchmarks demonstrate that CILP gives up to 22% higher resource utilization, 14% higher QoS scores and 44% lower execution costs compared to the current online and offline optimization based state-of-the-art methods.

    Comment: Accepted in IEEE Transactions on Network and Service Management
    Keywords Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning
    Subject code 004 ; 006
    Publishing date 2023-02-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing.

    Tuli, Shreshth / Tuli, Shikhar / Tuli, Rakesh / Gill, Sukhpal Singh

    Internet of things (Amsterdam, Netherlands)

    2020  Volume 11, Page(s) 100222

    Abstract: The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very ... ...

    Abstract The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policies to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This has been deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.
    Language English
    Publishing date 2020-05-12
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2542-6605
    ISSN (online) 2542-6605
    DOI 10.1016/j.iot.2020.100222
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

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  6. Book ; Online: AVAC

    Tuli, Shikhar / Tuli, Shreshth

    A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices

    2020  

    Abstract: Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging ... ...

    Abstract Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

    Comment: Accepted at 2020 IEEE International Symposium on Circuits and Systems (ISCAS)
    Keywords Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning
    Subject code 621
    Publishing date 2020-05-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments

    Tuli, Shreshth / Casale, Giuliano / Jennings, Nicholas R.

    2022  

    Abstract: The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers ... ...

    Abstract The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNN-based schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.

    Comment: Accepted as a poster in SIGMETRICS 2022
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Performance
    Subject code 006
    Publishing date 2022-05-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: RadNet

    Tuli, Shreshth / Wilkinson, Matthew R. / Kettell, Chris

    Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting

    2022  

    Abstract: Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents using spatio-temporal forecasting. We ... ...

    Abstract Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents using spatio-temporal forecasting. We consider the specific use case of road traffic systems where incidents take the form of anomalous events, such as accidents or broken-down vehicles. To tackle this, we develop a neural model, called RadNet, which forecasts system parameters such as average vehicle speeds for a future timestep. As such systems largely follow daily or weekly periodicity, we compare RadNet's predictions against historical averages to label incidents. Unlike prior work, RadNet infers spatial and temporal trends in both permutations, finally combining the dense representations before forecasting. This facilitates informed inference and more accurate incident detection. Experiments with two publicly available and a new road traffic dataset demonstrate that the proposed model gives up to 8% higher prediction F1 scores compared to the state-of-the-art methods.

    Comment: Accepted in IJCAI 2022 - Workshop on AI for Time Series Analysis
    Keywords Computer Science - Machine Learning
    Subject code 380
    Publishing date 2022-06-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing

    Tuli, Shreshth / Tuli, Shikhar / Tuli, Rakesh / Gill, Sukhpal Singh

    Internet of Things

    2020  Volume 11, Page(s) 100222

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ISSN 2542-6605
    DOI 10.1016/j.iot.2020.100222
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: CAROL

    Tuli, Shreshth / Casale, Giuliano / Jennings, Nicholas R.

    Confidence-Aware Resilience Model for Edge Federations

    2022  

    Abstract: In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting both latency- ... ...

    Abstract In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting both latency-sensitive and compute-intensive IoT tasks necessitates service resilience, especially for the broker nodes in typical broker-worker deployment designs. Existing fault-tolerance or resilience schemes often lack robustness and generalization capability in non-stationary workload settings. This is typically due to the expensive periodic fine-tuning of models required to adapt them in dynamic scenarios. To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction. Thus, whenever a broker fails, we quickly recover the system by executing a local-search over the broker-worker topology space and optimize future QoS. The confidence score enables us to keep track of the prediction performance and run parsimonious neural network fine-tuning to avoid excessive overheads, further improving the QoS of the system. Experiments on a Raspberry-Pi based edge testbed with IoT benchmark applications show that CAROL outperforms state-of-the-art resilience schemes by reducing the energy consumption, deadline violation rates and resilience overheads by up to 16, 17 and 36 percent, respectively.

    Comment: Accepted in DSN 2022
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-03-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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