• Title, Summary, Keyword: Offloading computation

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Adaptive Application Component Mapping for Parallel Computation Offloading in Variable Environments

  • Fan, Wenhao;Liu, Yuan'an;Tang, Bihua
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.11
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    • pp.4347-4366
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    • 2015
  • Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

Snapshot-Based Offloading for Web Applications with HTML5 Canvas (HTML5 캔버스를 활용하는 웹 어플리케이션의 스냅샷 기반 연산 오프로딩)

  • Jeong, InChang;Jeong, Hyuk-Jin;Moon, Soo-Mook
    • Journal of KIISE
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    • v.44 no.9
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    • pp.871-877
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    • 2017
  • A vast amount of research has been carried out for executing compute-intensive applications on resource-constrained mobile devices. Computation offloading is a method in which heavy computations are dynamically migrated from a mobile device to a server, exploiting the powerful hardware of the server to perform complex computations. An important issue for offloading is the complexity of reconciling the execution state of applications between the server and the client. To address this issue, snapshot-based offloading has recently been proposed, which utilizes the snapshot of a web app as the portable description of the execution state. However, for web applications using the HTML5 canvas, snapshot-based offloading does not function correctly, because the snapshot cannot capture the state of the canvas. In this paper, we propose a code generation technique to save the canvas state as part of a snapshot, so that the snapshot-based offloading can be applied to web applications using the canvas.

SorMob: Computation Offloading Framework based on AOP (SorMob: AOP 기반의 연산 오프로딩 프레임워크)

  • Cho, Yeongpil;Cho, Doosan;Paek, Yunheung
    • KIPS Transactions on Computer and Communication Systems
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    • v.2 no.5
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    • pp.203-208
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    • 2013
  • As smartphones are rapidly and widely spread, their applications request gradually larger computation power. Recently, in the personal computer, computing power of hardware has exceeded performance requirement of software sometimes. Computing power of smartphone, however, will not grow at the same pace as demand of applications because of form factor to seek thinner devices and power limitation by relatively slow technical progress of battery. Computation offloading is getting huge attention as one of solution for the problem. It has not commonly used technology in spite of advantages for performance and power consumption since the existing offloading frameworks are difficult for application developer to utilize. This paper presents an application developer-friendly offloading framework, named SorMob. Based on Aspect Oriented Programming model, SorMob provides a convenient environment for application development, and its performance was verified by comparing with the existing offloading framework.

Adaptive Cloud Offloading of Augmented Reality Applications on Smart Devices for Minimum Energy Consumption

  • Chung, Jong-Moon;Park, Yong-Suk;Park, Jong-Hong;Cho, HyoungJun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.8
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    • pp.3090-3102
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    • 2015
  • The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

User Mobility Model Based Computation Offloading Decision for Mobile Cloud

  • Lee, Kilho;Shin, Insik
    • Journal of Computing Science and Engineering
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    • v.9 no.3
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    • pp.155-162
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    • 2015
  • The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are becoming more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as a desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to choose good offloading decisions in mobile environments. In general, users' mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision-making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation results show that the proposed technique can help boost the performance of mobile devices in terms of response time and energy consumption, when users are highly mobile.

A Constrained Multi-objective Computation Offloading Algorithm in the Mobile Cloud Computing Environment

  • Liu, Li;Du, Yuanyuan;Fan, Qi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.9
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    • pp.4329-4348
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    • 2019
  • Mobile cloud computing (MCC) can offload heavy computation from mobile devices onto nearby cloudlets or remote cloud to improve the performance as well as to save energy for these devices. Therefore, it is essential to consider how to achieve efficient computation offloading with constraints for multiple users. However, there are few works that aim at multi-objective problem for multiple users. Most existing works concentrate on only single objective optimization or aim to obtain a tradeoff solution for multiple objectives by simply setting weight values. In this paper, a multi-objective optimization model is built to minimize the average energy consumption, time and cost while satisfying the constraint of bandwidth. Furthermore, an improved multi-objective optimization algorithm called D-NSGA-II-ELS is presented to get Pareto solutions with better convergence and diversity. Compared to other existing works, the simulation results show that the proposed algorithm can achieve better performance in terms of energy consumption, time and cost while satisfying the constraint of the bandwidth.

An Overview of Mobile Edge Computing: Architecture, Technology and Direction

  • Rasheed, Arslan;Chong, Peter Han Joo;Ho, Ivan Wang-Hei;Li, Xue Jun;Liu, William
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.4849-4864
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    • 2019
  • Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

An Offloading Decision Scheme Considering the Scheduling Latency of the Cloud in Real-time Applications (실시간 응용에서 클라우드의 스케줄링 지연 시간을 고려한 오프로딩 결정 기법)

  • Min, Hong;Jung, Jinman;Kim, Bongjae;Heo, Junyoung
    • KIISE Transactions on Computing Practices
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    • v.23 no.6
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    • pp.392-396
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    • 2017
  • Although mobile device-related technologies have developed rapidly, many problems arising from resource constraints have not been solved. Computation offloading that uses resources of cloud servers over the Internet was proposed to overcome physical limitations, and many studies have been conducted in terms of energy saving. However, completing tasks within their deadlines is more important than saving energy in real-time applications. In this paper, we proposed an offloading decision scheme considering the scheduling latency in the cloud to support real-time applications. The proposed scheme can improve the reliability of real-time tasks by comparing the estimated laxity of offloading a task with the estimated laxity of executing a task in a mobile device and selecting a more effective way to satisfy the task's deadline.

Combinatorial Auction-Based Two-Stage Matching Mechanism for Mobile Data Offloading

  • Wang, Gang;Yang, Zhao;Yuan, Cangzhou;Liu, Peizhen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.6
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    • pp.2811-2830
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    • 2017
  • In this paper, we study the problem of mobile data offloading for a network that contains multiple mobile network operators (MNOs), multiple WiFi or femtocell access points (APs) and multiple mobile users (MUs). MNOs offload their subscribed MUs' data traffic by leasing the unused Internet connection bandwidth of third party APs. We propose a combinatorial auction-based two-stage matching mechanism comprised of MU-AP matching and AP-MNO matching. The MU-AP matching is designed to match the MUs to APs in order to maximize the total offloading data traffic and achieve better MU satisfaction. Conversely, for AP-MNO matching, MNOs compete for APs' service using the Nash bargaining solution (NBS) and the Vickrey auction theories and, in turn, APs will receive monetary compensation. We demonstrated that the proposed mechanism converges to a distributed stable matching result. Numerical results demonstrate that the proposed algorithm well capture the tradeoff among the total data traffic, social welfare and the QoS of MUs compared to other schemes. Moreover, the proposed mechanism can considerably offload the total data traffic and improve the network social welfare with less computation complexity and communication overhead.

Mobile Energy Efficiency Study using Cloud Computing in LTE (LTE에서 클라우드 컴퓨팅을 이용한 모바일 에너지 효율 연구)

  • Jo, Bokyun;Suh, Doug Young
    • Journal of Broadcast Engineering
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    • v.19 no.1
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    • pp.24-30
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    • 2014
  • This study investigates computing offloading effect of cloud in real-time video personal broadcast service, whose server is mobile device. Mobile device does not have enough computing resource for encoding video. The computing burden is offloaded to cloud, which has abundant resources in terms of computing, power, and storage compared to mobile device. By reducing computing burden, computation energy can be saved while transmission data amount increases because of decreasing compression efficiency. This study shows that the optimal operation point can be found adaptively to time-varying LTE communication condition result of tradeoff analysis between offloaded computation burden and increase in amount of transmitted data.