Abstract
Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a user-centric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.
Highlights
In the digital era, we are experiencing an explosive increase in the number of mobile devices accessing the wireless network
We propose an adaptive quality of service (QoS)-aware Multipath Service Provisioning (QMSP) approach to maximize the number of requests served by the edge server
QoS-AWARE MULTI-PATH SERVICE PROVISIONING we present our main contribution of this work; a QoS-aware Multi-path Service Provisioning (QMSP) algorithm in Mobile Edge Computing (MEC) environment
Summary
We are experiencing an explosive increase in the number of mobile devices accessing the wireless network. Advancements in cloud computing (CC) and wireless communication technology have been the motivating factor behind such explosive growth. The total number of mobile devices is expected to reach 75 billion by 2020, while the volume of data is expected to exceed 24.3 exabytes/mo [1]. Facial recognition, virtual reality, online immersive gaming, and natural language processing. These applications typically require high availability and are data intensive or computing intensive, requiring high resource and energy consumption. The conflict between the intensive application of compute/data and resource-constrained mobile devices prevents the efficient adaptation of new paradigms [2]
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