1,288 research outputs found
EdgeFaaS: A Function-based Framework for Edge Computing
The rapid growth of data generated from Internet of Things (IoTs) such as
smart phones and smart home devices presents new challenges to cloud computing
in transferring, storing, and processing the data. With increasingly more
powerful edge devices, edge computing, on the other hand, has the potential to
better responsiveness, privacy, and cost efficiency. However, resources across
the cloud and edge are highly distributed and highly diverse. To address these
challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based
computing framework that supports the flexible, convenient, and optimized use
of distributed and heterogeneous resources across IoT, edge, and cloud systems.
EdgeFaaS allows cluster resources and individual devices to be managed under
the same framework and provide computational and storage resources for
functions. It provides virtual function and virtual storage interfaces for
consistent function management and storage management across heterogeneous
compute and storage resources. It automatically optimizes the scheduling of
functions and placement of data according to their performance and privacy
requirements. EdgeFaaS is evaluated based on two edge workflows: video
analytics workflow and federated learning workflow, both of which are
representative edge applications and involve large amounts of input data
generated from edge devices
Elastic admission control for federated cloud services
This paper presents a technique for admission control of a set of horizontally scalable services, and their optimal placement, into a federated Cloud environment. In the proposed model, the focus is on hosting elastic services whose resource requirements may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. The request may also be partially accommodated in federated external providers, if needed or more convenient. In finding the optimum allocation, the presented mechanism uses a probabilistic optimization model, which takes into account eco-efficiency and cost, as well as affinity and anti-affinity rules possibly in place for the components that comprise the services. In addition to modelling and solving the exact optimization problem, we also introduce a heuristic solver that exhibits a reduced complexity and solving time. We show evaluation results for the proposed technique under various scenarios
A Research Perspective on Data Management Techniques for Federated Cloud Environment
Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored
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