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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Teymoori, Peyvand Todd, Terence D. Zhao, Dongmei Karakostas, George McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781728182988

This paper considers the problem of algorithmic efficiency in mobile computation offloading with Concurrent Local Execution (CLE). Online energy optimal algorithms can be developed when CLE is used to guarantee hard task deadlines while offloading over Markovian wireless channels. Unfortunately, these algorithms often have a high computational complexity, which prohibits their use in online mobile implementations. Three algorithms are introduced to reduce this complexity: Markovian Compression (MC), Time Compression (TC) and Preemption Using Continuous offloading (Preemption-CO). MC and TC reduce the state space of the offloading Markovian process, by using a novel notion of geometric similarity, or by running an optimal online offloading algorithm in periodic time steps. In Preemption-CO. while a task is offloaded preemptively, the offloading decision at every time-slot is based on non-preemptive calculations. Our simulations show that, by applying these methods, the running limes of the algorithms can be significantly reduced without suffering unreasonable performance degradation compared with the optimal energy performance.

关键词： Cloud computing mobile computation offloading energy efficiency time efficiency hard task deadline constraints concurrent local execution

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Energy Efficient mobile computation offloading through Workload Migration

Energy Efficient Mobile Computation Offloading through Workl...

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IEEE International Conference on Smart City

作者： Chengxi Gao Victor C. S. Lee Department of Computer Science City University of Hong Kong

ISBN: (纸本)9781509018949

The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may suffer from dramatic energy consumption. To solve this problem, we propose to migrate the workload from remote cloud data center to nearby cloudlets, so as to relieve the pressure of cloud data center and save the energy consumption. Specifically, we propose a light-weight and decentralized algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm to construct the migration scheme. Simulations show that our algorithm can fast converge in tens of iterations, and decrease the overall energy consumption.

关键词： Energy efficiency mobile computation offloading Workload migration Decentralized algorithm Cloudlets

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Energy-Efficient Resource Allocation for mobile-Edge computation offloading

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2017年第3期16卷 1397-1411页

作者： You, Changsheng Huang, Kaibin Chae, Hyukjin Kim, Byoung-Hoon Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China LG Elect Seoul South Korea

mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

关键词： mobile-edge computing resource allocation mobile computation offloading energy-efficient computing

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Energy-Efficient mobile-Edge computation offloading over Multiple Fading Blocks

Energy-Efficient Mobile-Edge Computation Offloading over Mul...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Fan, Rongfei Li, Fudong Jin, Song Wang, Gongpu Jiang, Hai Wu, Shaohua Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728109626

By allowing a mobile device to offload computation-intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, to find out how much data should be offloaded such that the mobile device's energy consumption is minimal. Although the formulated optimization problem is non-convex, we prove that the objective function of the problem is piecewise convex, and accordingly develop an optimal solution for the problem. Numerical results verify the correctness of our findings and the effectiveness of our proposed method.

关键词： mobile edge computing fading mobile computation offloading

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LAMCO: A Layered Approach to mobile Application computation offloading 16

LAMCO: A Layered Approach to Mobile Application Computation ...

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16th IEEE International Wireless Communications and mobile Computing Conference (IEEE IWCMC)

作者： Ballan, Yousef Ahmed, Ali Baghaei, Nilufar Univ Liverpool Liverpool L69 3BX Merseyside England Victoria Univ Wellington Wellington New Zealand Massey Univ Auckland New Zealand

ISBN: (纸本)9781728131290

mobile computational offloading offers a solution to extend the computational power of a mobile device allowing it to run heavyweight activities/tasks. This turns low-end mobile devices into a capable machine of running heavyweight applications that require special processing power. Code offloading is achieved through migration. Migrating code from a local machine (i.e. mobile device) to the cloud where the code is actually executed is challenging. Questions such as when and how migration takes place are critical. Quite a few offloading frameworks are proposed in the literature. They are, mostly, mostly not efficient due to the lack of a standard offloading architecture, some did not propose an architecture at all. The abundance of Quality of Service (QoS) component is another concern. This paper introduces a layered approach to mobile computation offloading called LAMCO. LAMCO is implemented in the Android operating system and the run-time performance of the framework is evaluated in this paper. One advantage of LAMCO is that it does not require any modifications in the underlying operating system as the framework is dynamically installed and updated while the operating system is intact.

关键词： mobile computation offloading Cloud computation Big Data code migration

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An Adaptive Approach Towards computation offloading for mobile Cloud Computing

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2019年第2期14卷 52-73页

作者： Kero, Archana Khanna, Abhirup Kumar, Devendra Agarwal, Amit Shri Guru Ram Rai Inst Technol & Sci Dehra Dun Uttar Pradesh India Univ Melbourne Melbourne Vic Australia Coll Engn Roorkee Roorkee Uttar Pradesh India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun Uttar Pradesh India

The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.

关键词： Adaptive Code offloading Data Stream Applications mobile Cloud Computing mobile computation offloading

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mobile Cloud Computing Architecture for computation offloading 2

Mobile Cloud Computing Architecture for Computation Offloadi...

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2nd IEEE International Conference on Next Generation Computing Technologies (NGCT)

作者： Khanna, Abhirup Kero, Archana Kumar, Devendra Univ Petr & Energy Studies Dehra Dun Uttar Pradesh India Shri Guru Ram Rai Inst Technol & Sci Dehra Dun Uttar Pradesh India Coll Engn Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9781509032570

In recent years the Smartphone has undergone significant technological advancements but still remains a low computational entity. mobile Cloud Computing addresses this problem and provides a solution in form of mobile computation offloading (MCO). computation offloading is a concept in which certain parts (tasks) of an application are executed on cloud whereas the rest of them on the mobile device itself. MCO turns out to be a great help with respect to resource constrained mobile derives as it allows resource intensive tasks to be executed remotely. Though such procedures save mobile resources but incur communication cost between the mobile device and cloud. Thus, it becomes extremely essential for offloading models to take steps (offloading decisions) in order to augment the capabilities of mobile devices along with reduced execution and communication costs. In this paper, we present an application offloading model which offloads an application based upon the nature and execution pattern of its tasks. We also propose an algorithm that depicts the work flow of our computation model. The proposed model is simulated using the CloudSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.

关键词： mobile Cloud Computing mobile computation offloading Cloud computing CloudSim

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DEEP REINFORCEMENT LEARNING FOR computation offloading AND RESOURCE ALLOCATION IN BLOCKCHAIN-BASED MULTI-UAV-ENABLED mobile EDGE COMPUTING 17

DEEP REINFORCEMENT LEARNING FOR COMPUTATION OFFLOADING AND R...

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17th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Mohammed, Abegaz Nahom, Hayla Tewodros, Ayall Habtamu, Yasin Hayelow, Gebrye Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Dilla Univ Sch Informat & Comp Dilla Ethiopia

ISBN: (纸本)9780738142593

In the current fifth-generation (5G) and Beyond 5G (B5G) era, the Unmanned Aerial Vehicles (UAVs) have been playing a vital role and attracting interest in different application areas in the military, and civil applications such as communications, disaster management, search and rescue, security, control, agriculture, Internet of things (IoT), etc. In these networks, ultra-heterogeneous IoT devices generate time-sensitive traffic. However, those devices have limited resources to compute tasks. Recently, mobile Edge computation offloading (MECO) has been considered as an encouraging model to enable the computation tasks of IoT devices to be performed by MEC servers and support ultra-low latency IoT applications to ensure Quality of services (QoS). However, terrestrial network failure due to natural and human-made disasters has been increasing, and difficult to provide reliable computation offloading and resource allocation services to IoT networks. Nowadays, UAVs have been promising technology to quickly deploy and recover the system to provide efficient services to edge nodes. The offloading and resource allocation problems in current network technology are complex, and offloading task to edge server is vulnerable to security risks. Hence, we utilize a deep reinforcement learning method to handle a complex problem for computation offloading and resource allocations in a dynamic environment. And also, we explore a blockchain-based multi-UAV-assisted MEC architecture in securing and optimizing the offloading problems.

关键词： Blockchain Deep Reinforcement Learning Massive IoT mobile computation offloading Multi-UAV

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Joint computation offloading and Radio Resource Allocations in Small-Cell Wireless Cellular Networks

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2020年第3期4卷 745-758页

作者： Chen, Hong Zhao, Dongmei Chen, Qianbin Chai, Rong McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Chongqing Univ Posts & Telecommun Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China

mobile computation offloading (MCO) is a technique that can help reduce energy consumption of mobile devices (MDs) by offloading their tasks to more powerful devices for execution. In MCO, the offloading decision for a task depends on availability of both communication and computation resource. In small cell cellular networks, cloudlet servers are usually co-located with the small base stations (SBSs). As a result, offloading decisions of the MDs are coupled with SBS associations, while strong overlapping coverage between the SBSs can result in complicated interference conditions in wireless transmissions that affect the offloading performance. In this paper, offloading decisions and SBS associations are jointly optimized with transmission power and channel assignments in a small cell cellular network. The objective is to minimize the total energy consumption of all MDs, subject to task's latency constraints. The problem is first formulated as a mixed binary nonlinear programming problem, then transformed and solved using the general bender decomposition (GBD). A heuristic solution is proposed that recursively allows more MDs to make offloading decisions based on the current transmission conditions. Compared to using GBD, this solution results in much lower worst-case complexity, while achieving good energy performance for a wide range of system parameters.

关键词： Small cell cellular networks cloudlets mobile computation offloading radio resource allocations

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Heterogeneity in mobile Cloud Computing: Taxonomy and Open Challenges

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2014年第1期16卷 369-392页

作者： Sanaei, Zohreh Abolfazli, Saeid Gani, Abdullah Buyya, Rajkumar Univ Malaya Dept Comp Syst & Technol Kuala Lumpur Malaysia Univ Melbourne Dept Comp & Informat Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic 3010 Australia

The unabated flurry of research activities to augment various mobile devices by leveraging heterogeneous cloud resources has created a new research domain called mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in mobile computing and cloud computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent computing (i.e. mobile computing and cloud computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.

关键词： mobile Cloud Computing Vertical and Horizontal heterogeneity mobile computation offloading Interoperability Portability Seamless communication

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