An Efficient Application Partitioning Algorithm in Mobile Environments

Wu, Huaming; Knottenbelt, William J.; Wolter, Katinka

doi:10.1109/tpds.2019.2891695

Cited by 106 publications

(57 citation statements)

References 51 publications

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“…Based on MEC model, researchers have done much research work. The existing research mainly focuses on computation offloading [18,19], Yang et al [20] proposed to use the user's mobile mode and service access mode to predict the distribution of user requests in order to minimize the problem of user access delay and service provider cost; and then adjust the service layout and load schedule on the network according to the prediction result. Tan et al [21] propose a generic model for minimizing job response time in the edge-cloud, where jobs are generated in any order and time on the mobile device.…”

Section: Related Workmentioning

confidence: 99%

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Wei

Luo

Sun

2020

Sensors

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The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

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Section: Related Workmentioning

confidence: 99%

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Wei

Luo

Sun

2020

Sensors

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“…Current data managing strategy highlights the emergence of new challenges: normalized bandwidth and transmitting capacity. Transparent and seamless data sharing across ecosystems allowed by technological openness and open value exchanges between government and SME enabled by organizational openness call for a better tomorrow big data applications [94].…”

Section: Big Data Applicationsmentioning

confidence: 99%

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Zhang

et al. 2020

J Cloud Comp

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Cyber-Physical Systems(CPS) serves as an interdisciplinary effort that incorporates cyber vector as well as physical vector. The latter can generate exponentially growing amounts of data. How to process CPS big data systematically and efficiently is the key to the breakthrough of offering prospective and personalized services for each individual user and entity involved. In recent years, much research has been proactively conducted on advancing specific scenario or algorithm. However, few surveys value their integration. For good measure, the synthesis remains a fundamental challenge. Subsequently, we fill the gap in the literature by constructing cloud computing, fog computing and edge computing as a whole to inspire on new architectures and cross utilizations. Moreover, bringing the enthusiasm of traditionally solitude entities into play is crucial. In this exploratory study, we examine definitions of CPS as well as the three aforementioned computing paradigms and then shed new light on comprehensively established frameworks. We also survey on the application level of Cloud-Fog-Edge Computing in CPS respectively and dive into diversified algorithms and strategies to embed big data applications into a more intelligent and convenient society with current deficiencies and future research directions followed.

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“…Offloading of app execution to a remote server is an active research area. Many previous works focus on how to partition app execution between the client and server under resource constraints [8,10,44,45] or in a specific application domain [23,43]. The main focus of our study is on how to migrate app execution, so we explain the previous works in the context of state migration.…”

Section: Related Work 61 Dynamic Execution Offloadingmentioning

confidence: 99%

Dynamic Offloading of Web Application Execution Using Snapshot

Jeong

Moon

2020

ACM Trans. Web

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Mobile web platforms are facing new demands for emerging applications, such as machine learning or augmented reality, which require significant computing powers beyond that of current mobile hardware. Computation offloading can accelerate these apps by offloading the computation-intensive parts of an app from a client to a powerful server. Unfortunately, previous studies of offloading in the field of web apps have a limitation for the offloading target code or require complex user annotations, hindering the widespread use of offloading in web apps. This article proposes a novel offloading system for web apps, which can simplify the offloading process by sending and receiving the execution state of a running web app in the form of another web app called the snapshot. Since running the snapshot restores the whole app state and continues the execution from the point where it was saved, we can offload regular web app computations that affect the DOM state as well as the JavaScript state, and we do not have to pre-install the app binary at the server. Moreover, the snapshot does not require any annotations to be captured, making computation offloading more transparent to app developers. We qualitatively compared the proposed system with previous approaches in terms of programming difficulty and the scope of offloadable codes. In addition, we implemented the proposed system based on a WebKit browser and evaluated the offloading performance with five computation-intensive web apps. Our system achieved significant speedup (from 1.7 to approximately 9.0) in all of the apps, compared to local execution, which proves the feasibility of the proposed approach. CCS Concepts: • Software and its engineering → Organizing principles for web applications;

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An Efficient Application Partitioning Algorithm in Mobile Environments

Cited by 106 publications

References 51 publications

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing

Dynamic Offloading of Web Application Execution Using Snapshot

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