Relevant bibliographies by topics / Heterogeneous cloud framework

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Heterogeneous cloud framework'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Heterogeneous cloud framework"

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Wang, Chao, Xi Li, Peng Chen, Aili Wang, Xuehai Zhou, and Hong Yu. "Heterogeneous Cloud Framework for Big Data Genome Sequencing." IEEE/ACM Transactions on Computational Biology and Bioinformatics 12, no. 1 (2015): 166–78. http://dx.doi.org/10.1109/tcbb.2014.2351800.

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Dr.S.M., Jagateesan1 V.M.Pavithra2. "A SURVEY ON BIGDATA SCHEDULING ON CLOUD FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 922–27. https://doi.org/10.5281/zenodo.834614.

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Computational science workflows have been successfully run on traditional High Performance Computing (HPC) systems like clusters and Grids for many years. Now a day, users are interested to execute their workflow applications in the Cloud to exploit the economic and technical benefits of this new rising technology. The deployment and management of workflows over the current existing heterogeneous and not yet interoperable Cloud providers, is still a challenging task for the workflow developers. The Pointer Gossip Content Addressable Network Montage Framework allows an automatic selection of the goal clouds, a uniform get entry to to the clouds, and workflow data management with respect to user Service Level Agreement (SLA) requirements. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this comparative review on workflow scheduling algorithm cloud environment is provide solution for the problems. Based on the analysis, the authors also highlight some research directions for future investigation. The previous results offer benefits to users by executing workflows with the expected performance and service quality at lowest cost.
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Balta, Haris, Jasmin Velagic, Halil Beglerovic, Geert De Cubber, and Bruno Siciliano. "3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems." Remote Sensing 12, no. 10 (2020): 1608. http://dx.doi.org/10.3390/rs12101608.

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The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.
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Shih, Chi-Sheng, Joen Chen, Yu-Hsin Wang, and Norman Chang. "Heterogeneous and Elastic Computation Framework for Mobile Cloud Computing." International Journal of Software Engineering and Knowledge Engineering 24, no. 07 (2014): 1013–37. http://dx.doi.org/10.1142/s0218194014400051.

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The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.
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Wu, Lei, and Yuandou Wang. "Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach." International Journal of Web Services Research 15, no. 4 (2018): 82–96. http://dx.doi.org/10.4018/ijwsr.2018100105.

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Cloud computing, with dependable, consistent, pervasive, and inexpensive access to geographically distributed computational capabilities, is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. Scheduling multiple workflows over cloud infrastructures and resources is well recognized to be NP-hard and thus critical to meeting various types of Quality-of-Service (QoS) requirements. In this work, the authors consider a multi-objective scientific workflow scheduling framework based on the dynamic game-theoretic model. It aims at reducing make-spans, cloud cost, while maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). The authors consider randomly-generated scientific workflow templates as test cases and carry out extensive real-world tests based on third-party commercial clouds. Experimental results show that their proposed framework outperforms traditional ones by achieving lower make-spans, lower cost, and better system fairness.
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Edavalath, Sheena, and Manikandasaran S. Sundaram. "Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment." Scientific Temper 14, no. 04 (2023): 1339–44. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.41.

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Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.
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Mr., K. Rajkumar. "ANALYSIS OF MULTI-CLOUD ENVIRONMENT WITH SECURED FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 645–48. https://doi.org/10.5281/zenodo.60110.

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Cloud computing associate rising technology with high cost data storages devices as well as the rapid rate for different cloud services such as Infrastructure as a service, software as a service, Platform as a Services. The cloud storage moves the user’s facts to large data centers which is remotely located. This paper proposes the Multi-cloud computing Architecture allow dynamic, efficient resource sharing among the cloud Service. Mechanisms for collaboration across multiple cloud service must undergo a rigorous, in-depth security analysis to find new threats and concerns resulting from collaboration. They must have the support of creative, systematic, and usable mechanisms that give effective security for data and applications. Without these provider-centric changes, current proposals don't give facilities for client-centric, on-the-fly, and expedient combos of heterogeneous cloud-based services.
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Omoniyi David Olufemi. "Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4098–140. https://doi.org/10.30574/wjarr.2025.26.2.2074.

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The convergence of quantum computing, artificial intelligence (AI), and federated cloud architecture offers transformative potential for secure, scalable, and privacy-preserving data processing. Yet, trust management and cross-domain observability remain major challenges, particularly in decentralized, heterogeneous cloud environments. This paper introduces Quantum-AI Federated Clouds (QAIFC) a novel trust-aware framework that combines quantum-safe encryption, federated machine learning, and explainable AI to enable secure and observable operations across cloud domains. We present QFedSecure, a protocol suite leveraging lattice-based cryptography, quantum key distribution, and AI-driven anomaly detection to support trust propagation and policy enforcement. The framework features a dynamic trust model, observability protocol, and mechanisms for adversarial resilience. Simulations using Qiskit, TensorFlow Federated, and NS3 show up to 40% improvement in trust calibration and 55% increase in adversarial detection over baseline systems. This work advances the foundation for resilient, decentralized, and quantum-secure AI cloud ecosystems.
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Bauer, Daniela, and Simon Fayer. "Standardizing DIRAC’s Cloud Interfaces." EPJ Web of Conferences 295 (2024): 04039. http://dx.doi.org/10.1051/epjconf/202429504039.

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DIRAC is a widely used framework for distributed computing. It provides a layer between users and computing resources by offering a common interface to a number of heterogeneous resource providers. In these proceedings we describe a new implementation of the DIRAC to Cloud interface.
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Bibal, J. V. Benifa, and D. Dejey. "An Auto-Scaling Framework for Heterogeneous Hadoop Systems." International Journal of Cooperative Information Systems 26, no. 04 (2017): 1750004. http://dx.doi.org/10.1142/s0218843017500046.

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The scalability of the cloud infrastructure is essential to perform large-scale data processing using MapReduce programming model by automatically provisioning and de-provisioning the resources on demand. The existing MapReduce model shows performance degradation while getting adapted to heterogeneous environments since sufficient techniques are not available to scale the resources on demand and the scheduling algorithms would not cooperate as the resources are configured dynamically. An Auto-Scaling Framework (ASF) is presented in this article to configure the resources automatically based on the current system load in a heterogeneous Hadoop environment. The scheduling of data and task is done in a data-local manner that adapts while new resources are configured, or the existing resources are removed. A monitoring module is integrated with the JobTracker to observe the status of physical machines, compute the system load and provide automated provisioning of the resources. Then, Replica Tracker is utilized to track the replica objects for efficient scheduling of the task in the physical machines. The experiments are conducted in a commercial cloud environment using diverse workload characteristics, and the observations show that the proposed framework outperforms the existing scheduling mechanisms by the performance metrics such as average completion time, scheduling time, data locality, resource utilization and throughput.
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Dissertations / Theses on the topic "Heterogeneous cloud framework"

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Sigwele, Tshiamo. "Energy Efficient Cloud Computing Based Radio Access Networks in 5G. Design and evaluation of an energy aware 5G cloud radio access networks framework using base station sleeping, cloud computing based workload consolidation and mobile edge computing." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16062.

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Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.
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Ramharuk, Vikash. "Survivable cloud multi-robotics framework for heterogeneous environments." Diss., 2015. http://hdl.handle.net/10500/19698.

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The emergence of cloud computing has transformed the potential of robotics by enabling multi-robotic teams to fulfil complex tasks in the cloud. This paradigm is known as “cloud robotics” and relieves robots from hardware and software limitations, as large amounts of available resources and parallel computing capabilities are available in the cloud. The introduction of cloud-enabled robots alleviates the need for computationally intensive robots to be built, as many, if not all, of the CPU-intensive tasks can be offloaded into the cloud, resulting in multi-robots that require much less power, energy consumption and on-board processing units. While the benefits of cloud robotics are clearly evident and have resulted in an increase in interest among the scientific community, one of the biggest challenges of cloud robotics is the inherent communication challenges brought about by disconnections between the multi-robotic system and the cloud. The communication delays brought about by the cloud disconnection results in robots not being able to receive and transmit data to the physical cloud. The unavailability of these robotic services in certain instances could prove fatal in a heterogeneous environment that requires multi-robotic teams to assist with the saving of human lives. This niche area is relatively unexplored in the literature. This work serves to assist with the challenge of disconnection in cloud robotics by proposing a survivable cloud multi-robotics (SCMR) framework for heterogeneous environments. The SCMR framework leverages the combination of a virtual ad hoc network formed by the robot-to-robot communication and a physical cloud infrastructure formed by the robot-to-cloud communications. The Quality of Service (QoS) on the SCMR framework is tested and validated by determining the optimal energy utilization and Time of Response (ToR) on drivability analysis with and without cloud connection. The experimental results demonstrate that the proposed framework is feasible for current multi-robotic applications and shows the survivability aspect of the framework in instances of cloud disconnection.<br>School of Computing<br>M.Sc. (Computer Science)
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Chen, Jian-Hao, and 陳建豪. "Design and Implementation of Resource-Aware Computation Framework over Heterogeneous Mobile Cloud Systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/34435734955664645865.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>101<br>As variable applications on mobile devices raised, the computation resource of mobile device is insufficient to meet the requirement of applications. There are two approaches to improve performance of mobile devices: mobile computing and GPGPU technique. This thesis aims to incorporate the two approaches to greatly improve the performance of mobile devices. Open Computing Language (OpenCL), one of the GPGPU techniques, is an open standard framework to write a GPGPU applications. We design and implement an OpenCL runtime to assist mobile device to utility remote GPGPU computation resources. The OpenCL runtime provides a lightweight GPU virtulization layer. Mobile devices can execute GPGPU computation with the OpenCL runtime even if the mobile devices lacks for GPU. Furthermore, we proposed an extended OpenCL framework to serve more mobile devices subject to limited computation resources.
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Hong-WeiLiu and 劉弘偉. "Cloud Attendance and Heterogeneous Access Controller Management System Based on OM2M Framework with Cache Concept." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/58fskp.

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Book chapters on the topic "Heterogeneous cloud framework"

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Kundu, Anirban, Chunlin Ji, and Ruopeng Liu. "Cloud Based Heterogeneous Distributed Framework." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32063-7_50.

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Ahn, Euijai, Kangyoon Lim, and Gerard Jounghyun Kim. "MIDAS: A Software Framework for Accommodating Heterogeneous Interaction Devices for Cloud Applications." In Distributed, Ambient, and Pervasive Interactions. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39351-8_37.

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Zanella, Michele. "Post-cloud Computing: Addressing Resource Management in the Resource Continuum." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_9.

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AbstractThe exponential growth of interconnected IoT devices, highlights the infrastructure limitations of Cloud-based computing approaches. In this context, novel solutions (i.e., Fog and Edge computing) aim to exploit a continuum resource space composed of nearby and mobile devices as a single heterogeneous and distributed system to move part of the computation closer to data sources. In this regard, the heterogeneous nature of these devices (performance, features, capabilities...) requires proper programming models and run-time management layers. This chapter proposes an overview of recent modeling premises and quantitative results in a resource management perspective through the BarMan framework, which combines a task-based programming model, a run-time resource manager, and the BeeR task distribution software to deploy use-case applications-modules across the boards of a real Fog cluster.
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Lee, Myunghee, Gerard J. Kim, and Jeonghyun Baek. "MIDAS-M: A Software Framework for Supporting Multimodal Interaction on Heterogeneous Interaction Devices for Cloud Applications." In Distributed, Ambient and Pervasive Interactions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58697-7_12.

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Repetto, Matteo, and Alessandro Carrega. "Monitoring Network Flows in Containerized Environments." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_2.

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AbstractWith the progressive implementation of digital services over virtualized infrastructures and smart devices, the inspection of network traffic becomes more challenging than ever, because of the difficulty to run legacy cybersecurity tools in novel cloud models and computing paradigms. The main issues concern i) the portability of the service across heterogeneous public and private infrastructures, that usually lack hardware and software acceleration for efficient packet processing, and ii) the difficulty to integrate monolithic appliances in modular and agile containerized environments.In this Chapter, we investigate the usage of the extended Berkeley Packet Filter (eBPF) for effective and efficient packet inspection in virtualized environments. Our preliminary implementation demonstrates that we can achieve the same performance as well-known packet inspection tools, but with far less resource consumption. This motivates further research work to extend the capability of our framework and to integrate it in Kubernetes.
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Guevara, Ivan, Hafiz Ahmad Awais Chaudhary, and Tiziana Margaria. "Model-Driven Edge Analytics: Practical Use Cases in Smart Manufacturing." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19762-8_29.

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AbstractIn the Internet of Things (IoT) era, devices and systems generate enormous amounts of real-time data, and demand real-time analytics in an uninterrupted manner. The typical solution, a cloud-centred architecture providing an analytics service, cannot guarantee real-time responsiveness because of unpredictable workloads and network congestion. Recently, edge computing has been proposed as a solution to reduce latency in critical systems. For computation processing and analytics on edge, the challenges include handling the heterogeneity of devices and data, and achieving processing on the edge in order to reduce the amount of data transmitted over the network.In this paper, we show how low-code, model-driven approaches benefit a Digital Platform for Edge analytics. The first solution uses EdgeX, an IIoT framework for supporting heterogeneous architectures with the eKuiper rule-based engine. The engine schedules fully automatically tasks that retrieve data from the Edge, as the infrastructure near the data is generated, allowing us to create a continuous flow of information. The second solution uses FiWARE, an IIoT framework used in industry, using IoT agents to accomplish a pipeline for edge analytics. In our architecture, based on the DIME LC/NC Integrated Modelling Environment, both integrations of EdgeX/eKuyper and FiWARE happen by adding an External Native DSL to this Digital Platform. The DSL comprises a family of reusable Service-Independent Building blocks (SIBs), which are the essential modelling entities and (service) execution capabilities in the architecture’s modelling layer. They provide users with capabilities to connect, control and organise devices and components, and develop custom workflows in a simple drag and drop manner.
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Ahmed, Waseem, Mohsin Khan, Adeel Ahmed Khan, et al. "A Framework for Faster Porting of Scientific Applications Between Heterogeneous Clouds." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94180-6_5.

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Kermarrec, Gaël, Vibeke Skytt, and Tor Dokken. "LR B-Splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids." In Optimal Surface Fitting of Point Clouds Using Local Refinement. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16954-0_5.

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AbstractPerforming surface approximation of geospatial point clouds with locally refined (LR) B-splines comes with several challenges: (i) Point clouds have varying data density, (ii) outliers should be eliminated without deleting features, (iii) voids, also called holes, or data gaps should be treated specifically to avoid the drop of the approximated surface in domains without points. These factors tend to be even more challenging when point clouds acquired from different sensors having different noise characteristics are fused together. The data set becomes non-uniform and the fusing process itself involves a risk of an increased noise level. In this chapter, we provide some tools to answer those specific challenges. We will use terrain and seabed data and show didactically how to perform adaptive surface approximation with local refinement and to select customized parameters. We will further address the problem of choosing an appropriate tolerance for performing an adaptive fitting, and discuss the refinement strategies within the context of LR B-splines. The latter is shown to provide a promising framework for surface fitting of heterogeneous point clouds from various sources.
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Nagaraj, Ambika. "Cloud with AI." In The Role of AI in Enhancing IoT-Cloud Applications. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165708123010007.

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Distributed computing is essential in our present-day lives as it empowers a scope of utilizations from framework to virtual entertainment. Such framework should adapt to changing burdens and developing use mirroring social orders' communication and reliance on robotized figuring frameworks while fulfilling the nature of administration requirements. Empowering these frameworks is a companion of practical innovations orchestrated to satisfy the need to develop registering applications. There is a need to distinguish fundamental advances in licensing future applications. Cloud suppliers, for example, Facebook, Google and Amazon, use an enormous scope of Cloud Server farms to arrange heterogeneous nature administration requirements. Cloud registering stages can give a bound-together connection point over heterogeneous assets found in the Web of Things-based applications, which work on the dependability of cloud administrations. This chapter discusses cloud-AI architecture, applications, challenges and future directions.
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Delgado, José C. "Distributed Interoperability in Heterogeneous Cloud Systems." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8213-9.ch001.

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Cloud platforms constitute distributed and heterogeneous systems. Interacting applications, possibly in different clouds, face relevant interoperability challenges. This chapter details the interoperability problem and presents an interoperability framework, which provides a systematization of aspects such as coupling, compatibility, and the various levels at which interoperability must occur. After discussing the main limitations of current interoperability technologies, such as Web Services and RESTful applications, the chapter proposes an alternative technology. This entails a new distributed programming language, capable of describing both data and code in a platform-agnostic fashion. The underlying model is based on structured resources, each offering its own service. Service-oriented interfaces can be combined with the structured resources and hypermedia that characterize RESTful applications, instead of having to choose one style or the other. Coupling is reduced by checking interoperability structurally, based on the concepts of compliance and conformance. There is native support for binary data and full-duplex protocols.
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Conference papers on the topic "Heterogeneous cloud framework"

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Yanagawa, Takumi, Vikas Agarwal, Yuji Watanabe, Lou Degenaro, and Anca Sailer. "A Secure Framework for Continuous Compliance across Heterogeneous Policy Validation Points." In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00029.

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Zhang, Shuli, Xizi Peng, Shumei Lei, et al. "A Hierarchical Privacy-Preserving Framework for Heterogeneous Data Utilization in Satellite Internet." In 2025 10th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2025. https://doi.org/10.1109/icccbda64898.2025.11030413.

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Rattihalli, Gourav, Ninad Hogade, Aditya Dhakal, et al. "Fine-Grained Heterogeneous Execution Framework with Energy Aware Scheduling." In 2023 IEEE 16th International Conference on Cloud Computing (CLOUD). IEEE, 2023. http://dx.doi.org/10.1109/cloud60044.2023.00014.

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Noor, Ayman, Devki Nandan Jha, Karan Mitra, et al. "A Framework for Monitoring Microservice-Oriented Cloud Applications in Heterogeneous Virtualization Environments." In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00035.

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Alamri, Atif. "Cloud-Based E-Health Multimedia Framework for Heterogeneous Network." In 2012 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2012. http://dx.doi.org/10.1109/icmew.2012.84.

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Pardesi, Vikas, Aditya Khamparia, and Narendra Kr Bagde. "A secure framework in brokerage of heterogeneous cloud environment for multiple cloud providers." In 2014 5th International Conference- Confluence The Next Generation Information Technology Summit. IEEE, 2014. http://dx.doi.org/10.1109/confluence.2014.6949331.

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Abolfazli, Saeid, Abdullah Gani, and Min Chen. "HMCC: A Hybrid Mobile Cloud Computing Framework Exploiting Heterogeneous Resources." In 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud). IEEE, 2015. http://dx.doi.org/10.1109/mobilecloud.2015.28.

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Sidiropoulos, Harry, George Chatzikonstantis, Dimitrios Soudris, and Christos Strydis. "The VINEYARD Framework for Heterogeneous Cloud Applications: The BrainFrame Case." In 2018 Conference on Design and Architectures for Signal and Image Processing (DASIP). IEEE, 2018. http://dx.doi.org/10.1109/dasip.2018.8597119.

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Hong, Yelin. "A Resource-Oriented Middleware Framework for Heterogeneous Internet of Things." In 2012 International Conference on Cloud and Service Computing (CSC). IEEE, 2012. http://dx.doi.org/10.1109/csc.2012.10.

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"An Open-Source Framework for Integrating Heterogeneous Resources in Private Clouds." In 4th International Conference on Cloud Computing and Services Science. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004936601290134.

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