Review on Mobile Web Service Architectures and Challenges
01 Jan 2020-pp 95-105
TL;DR: The review of the comprehensive set of mobile web service architecture approaches is revealed and is compared based on the architectural designs and drawbacks and the challenges encompassed in the mobile webservice environment are described in addition to performance measures of obtained situation ofMobile web service (MWS).
Abstract: Web service is the software system which is intended for supporting machine-to-interoperable machine connection of frameworks above a system. The cellular gadget is developing into another computing system and a common aim to process and access the required data. For accomplishing the appropriated processing, the feature of web services and cellular gadget cannot be downplayed. So, integrating the web service on the cellular devices will increase the usability of web services, which makes web service as the simplest approach. This paper reveals the review of the comprehensive set of mobile web service architecture approaches and is compared based on the architectural designs and drawbacks. Moreover, this paper describes the challenges encompassed in the mobile web service environment in addition to performance measures of obtained situation of mobile web service (MWS).
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TL;DR: DeepAdapter as mentioned in this paper is an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput.
Abstract: Deep learning shows great promise in providing more intelligence to the cross-platform web. However, insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web browsers. We propose DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput. DeepAdapter consists of context-aware pruning, service updating, and online scheduling. First, the offline pruning module provides a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit various contexts. Second, the service updating module optimizes branch model cache on the edge for massive mobile users and updates the new model pruning requirements. Third, the online scheduling module matches optimal branch models for mobile users. Also, a two-stage DRL-based online scheduling method named DeepScheduler can handle high concurrent requests between edge centers and remote cloud by designing the reward prediction model. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.
4 citations
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TL;DR: A real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at theedge is envisions.
Abstract: MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.
700 citations
TL;DR: This paper presents the definitions of the MEC given by researchers and discusses the opportunities brought by the M EC and some of the important research challenges highlighted in MEC environment.
273 citations
TL;DR: A novel offloading system to design robust offloading decisions for mobile services is proposed and its approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services.
Abstract: The development of cloud computing and virtualization techniques enables mobile devices to overcome the severity of scarce resource constrained by allowing them to offload computation and migrate several computation parts of an application to powerful cloud servers. A mobile device should judiciously determine whether to offload computation as well as what portion of an application should be offloaded to the cloud. This paper considers a mobile computation offloading problem where multiple mobile services in workflows can be invoked to fulfill their complex requirements and makes decision on whether the services of a workflow should be offloaded. Due to the mobility of portable devices, unstable connectivity of mobile networks can impact the offloading decision. To address this issue, we propose a novel offloading system to design robust offloading decisions for mobile services. Our approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services. To this end, we also introduce a mobility model and a trade-off fault-tolerance mechanism for the offloading system. A genetic algorithm (GA) based offloading method is then designed and implemented after carefully modifying parts of a generic GA to match our special needs for the stated problem. Experimental results are promising and show near-optimal solutions for all of our studied cases with almost linear algorithmic complexity with respect to the problem size.
261 citations
TL;DR: This paper goes through the Alexa.com top 4000 most popular sites to identify precisely 500 websites claiming to provide a REST web service API, and analyzes these 500 APIs for key technical features, degree of compliance with REST architectural principles, and for adherence to best practices.
Abstract: Businesses are increasingly deploying their services on the web, in the form of web applications, SOAP services, message-based services, and, more recently, REST services. Although the movement towards REST is widely recognized, there is not much concrete information regarding the technical features being used in the field, such as typical data formats, how HTTP verbs are being used, or typical URI structures, just to name a few. In this paper, we go through the Alexa.com top 4000 most popular sites to identify precisely 500 websites claiming to provide a REST web service API. We analyze these 500 APIs for key technical features, degree of compliance with REST architectural principles (e.g., resource addressability), and for adherence to best practices (e.g., API versioning). We observed several trends (e.g., widespread JSON support, software-generated documentation), but, at the same time, high diversity in services, including differences in adherence to best practices, with only 0.8 percent of services strictly complying with all REST principles. Our results can help practitioners evolve guidelines and standards for designing higher quality services and also understand deficiencies in currently deployed services. Researchers may also benefit from the identification of key research areas, contributing to the deployment of more reliable services.
87 citations
TL;DR: This paper formally models this problem of mobile service selection for composition in terms of energy consumption and constructs energy consumption computation models that adopts the genetic algorithm to resolve it.
Abstract: Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.
74 citations