Computing and Information Systems - Research Publications

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    ChainsFormer: A Chain Latency-Aware Resource Provisioning Approach for Microservices Cluster
    Song, C ; Xu, M ; Ye, K ; Wu, H ; Gill, SS ; Buyya, R ; Xu, C (Springer Nature Switzerland, 2023-01-01)
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    STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management
    Ghosh, S ; Mukherjee, A ; Ghosh, SK ; Buyya, R (WILEY, 2022-12)
    Abstract Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.
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    CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds
    Zakarya, M ; Gillam, L ; Salah, K ; Rana, O ; Tirunagari, S ; Buyya, R (IEEE COMPUTER SOC, 2023-03-01)
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    Market-inspired framework for securing assets in cloud computing environments
    Tziakouris, G ; Mera-Gomez, C ; Ramirez, F ; Bahsoon, R ; Buyya, R (WILEY, 2022-09)
    Abstract Self‐adaptive security methods have been extensively leveraged for securing software systems and users from runtime threats in online and elastic environments, such as the cloud. The existing solutions treat security as an aggregated quality by enforcing “one service for all” without considering the explicit security requirements of each asset or the costs associated with security. Dealing with the security of assets in ultra‐large environments calls for rethinking the way we select and compose services—considering not only the services but the underlying supporting computational resources in the process. We motivate the need for an asset‐centric, self‐adaptive security framework that selects and allocates services and underlying resources in the cloud. The solution leverages learning algorithms and market‐inspired approaches to dynamically manage changes in the runtime security goals/requirements of assets with the provision of suitable services and resources, while catering for monetary and computational constraints. The proposed framework aims to inform the self‐adaptive security efforts of security researchers and practitioners operating in dynamic large‐scale environments, such as the Cloud. To illustrate the utility of the proposed framework it is evaluated using simulation on an application based scenario, involving cloud‐based storage and security services.
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    RESCUE: Enabling green healthcare services using integrated IoT-edge-fog-cloud computing environments
    Das, J ; Ghosh, S ; Mukherjee, A ; Ghosh, SK ; Buyya, R (WILEY, 2022-07)
    Abstract Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework namedRESCUE(enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption.
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    IoT-Pi: A machine learning-based lightweight framework for cost-effective distributed computing using IoT
    Shao, T ; Chowdhury, D ; Gill, SS ; Buyya, R (JOHN WILEY & SONS LTD, 2022-05)
    It is possible to develop intelligent and self‐adaptive application on the edge nodes with rapid increase in computational capability of Internet of Things (IoT) devices. With the rapid growth of cloud technologies, the demand for hybrid architecture with cloud and IoT has also been boosted as well. To satisfy the critical and comprehensive requirements in the architecture evolution, we proposed a lightweight framework called IoT‐Pi to provide a 3‐phase (sample, learn, adapt) life cycle management of cloud resources with machine learning prediction working on IoT edge nodes using Raspberry Pi device. Compared to the traditional interference by human beings in the field of system administration, the accuracy rate of machine learning prediction in the proposed technique for some algorithms reached over 70%, which demonstrates the feasibility and effectiveness of running cloud resource management on an IoT devices such as Raspberry Pi.
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    Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions
    Gupta, I ; Singh, AK ; Lee, C-N ; Buyya, R (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022)
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    Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions
    Ismail, L ; Buyya, R (MDPI, 2022-08)
    The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations' development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications' systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.
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    Systematic scalability analysis for microservices granularity adaptation design decisions
    Hassan, S ; Bahsoon, R ; Buyya, R (WILEY, 2022-06)
    Abstract Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomous, scalable and more reliable computing. A critical problem related to microservices is reasoning about the suitable granularity level of a microservice (i.e., when and how to merge or decompose microservices). Although scalability is pronounced as one of the major factors for adoption of microservices, there is a general gap of approaches that systematically analyse the dimensions and metrics, which are important for scalability‐aware granularity adaptation decisions. To the best of our knowledge, the state‐of‐art in reasoning about microservice granularity adaptation is neither: (1) driven by microservice‐specific scalability dimensions and metrics nor (2) follow systematic scalability analysis to make scalability‐aware adaptation decisions. In this article, we address the aforementioned problems using a two‐fold contribution. Firstly, we contribute to a working catalogue of microservice‐specific scalability dimensions and metrics. Secondly, we describe a novel application of scalability goal‐obstacle analysis for the context of reasoning about microservice granularity adaptation. We analyse both contributions by comparing their usage on a hypothetical microservice architecture against ad‐hoc scalability assessment for the same architecture. This analysis shows how both contributions can aid making scalability‐aware granularity adaptation decisions.