An Edge Computing Matching Framework with Guaranteed Quality of Service
ABSTARCT :
Edge computing is a new computing paradigm, which aims at enhancing user experience by bringing computing resources closer to where data is produced by Internet of Things (IoT). Edge services are provided by small data centers located at the edge of the network, called cloudlets. However, IoT users often face strict Quality of Service (QoS) constraints for a proper remote execution of their applications on edge. Each user has specific resource requirements and budget limitations for her IoT application, while each cloudlet offers a limited number and types of resources, each with a specific cost. Therefore, a key challenge is how to efficiently match cloudlets to IoT applications and enable a convenient any-time access to edge computing services considering preferences and incentives of users and cloudlets. In this paper, we address this problem by proposing a novel two-sided matching solution for edge services considering QoS requirements in terms of service response time. In addition, we determine dynamic pricing of edge services based on the preferences and incentives of cloudlets, IoT users, and the system. The proposed matching is incentive compatible, individually rational, weakly budget balanced, asymptotically locative efficient, and computationally efficient. We perform a comprehensive assessment through extensive performance analysis experiments to evaluate our proposed matching and pricing solutions.
EXISTING SYSTEM :
Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood.
DISADVANTAGE :
When the cloud resources are closer to the users, the users experience fewer network issues. Moreover, the location of IoT devices creates some problems for cloud providers.
PROPOSED SYSTEM :
This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition.
ADVANTAGE :
The proposed solution aims to develop decentralized smart objects within IoT and take the benefits of cloud computing by managing data processing and storage needs with lower costs.
There are three selection service options offered to the end-users.
pure cloud services (the edge service is a middle layer between the cloud and the end user); edge services (extenders of classical cloud services); and, coordinated fog-to-cloud services (a collaborative model to facilitate matching between cloud and fog services).
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