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computation partitioning for Mobile Cloud Computing in a Big Data Environment

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2017年第4期13卷 2009-2018页

作者： Li, Jianqiang Huang, Luxiang Zhou, Yaoming He, Suiqiang Ming, Zhong Shenzhen Univ Coll Comp & Software Engn Shenzhen 518060 Peoples R China Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China

The growth of mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment that are available to mobile clients while addressing the dynamic changes in network bandwidth. Big data can be handled via MCC. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve the performance. First, we constructed a model of stateful data streaming and investigated the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single-frame data flow, task scheduling, and executing efficiency. We also defined the problem for a multiframe data flow calculation segmentation decision that is optimized for dynamic conditions and provided an analysis. Second, we proposed a computation partitioning method for single-frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. We followed the scheduling method to provide the optimal calculation for data frame execution time after computation partitioning and the best computation partitioning method. Third, we explored a calculation segmentation method for single-frame data flow based on multiframe data using multiframe data optimization adjustment and prediction of future changes in network bandwidth. We were able to demonstrate that the calculation method for multiframe data in a changing network bandwidth environment is more efficient than the calculation method with the limitation of calculations for single-frame data. Finally, our research verified the effectiveness of single-frame data in the application of the data stream and analyzed the performance of the method to optimize the adjustment of multiframe data. We used a MCC platform prototype system for face recognition to verify the effectiveness of the method.

关键词： Big data computation partitioning data stream dynamic environment mobile cloud computing (MCC) stateful

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computation partitioning for Mobile Cloud Computing in a Big Data Environment 2

Computation Partitioning for Mobile Cloud Computing in a Big...

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2nd International Conference on Frontiers of Sensors Technologies (ICFST)

作者： Huang, Luxiang Li, Jianqiang Li, Jun Yi, Dongyi Shen Zhen Univ Comp Sci & Software Enginnering Shenzhen Peoples R China

ISBN: (纸本)9781509048601

The growth of Mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment available for mobile clients while working with the dynamic changes of network bandwidth. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve performance. First, we constructed a model of stateful data streaming and studied the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single frame data flow, task scheduling and executing efficiency. Second, we proposed a computation partitioning method for Single frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. Finally, our research verified the effectiveness of single frame data in the application of the data stream. We used a mobile cloud computing platform prototype system for face recognition to verify the effectiveness of the method.

关键词： computation partitioning single frame data flow dynamic environment mobile cloud computing stateful component

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Joint computation partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第5期14卷 1439-1452页

作者： Yang, Lei Liu, Bo Cao, Jiannong Sahni, Yuvraj Wang, Zhenyu South China Univ Technol Sch Software Engn Guangzhou 510630 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China

The proliferation of mobile devices and ubiquitous access of the wireless network enable many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), which is an offline algorithm, to solve the problem. We compare MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency. Moreover, we also design an online method, named by Cooperative Online Scheduling (COS), which can be easily deployed in practical systems. By extensive evaluations, we show that COS outperforms the benchmark methods by 25 percent on average.

关键词： Cloud computing Bandwidth Resource management Mobile handsets computational modeling Servers Benchmark testing computation partitioning latency sensitive applications mobile edge clouds

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Revisiting computation partitioning in Future 5G-Based Edge Computing Environments

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IEEE INTERNET OF THINGS JOURNAL 2019年第2期6卷 2427-2438页

作者： Cao, Jin Yang, Lei Cao, Jiannong South China Univ Technol Sch Software Engn Guangzhou 510640 Guangdong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Edge computing recently attracts the industry and academic attentions due to its advantage of providing low latency services in a much closer place to the end users. This paper studies the problem of computation partitioning in future 5G-based edge computing environments. Although the problem has been studied a lot in (mobile) cloud computing, the problem in this paper is different with previous works. Traditional partitioning approaches in cloud computing aim to achieve an optimal tradeoff between the network transmission cost and the local computation cost, because the data transmission to cloud is very costly. However, in future 5G-based edge computing, the high bandwidth and low latency will overcome the data transmission challenge. Instead the constrained computation capability of the edge will greatly affect the performance of an partitioned execution of the application. As the challenge changes, we propose a new partitioning model, which parallelizes the computations and fully utilizes the computational resources on the edge and end devices. We develop an off-line solution for partitioning and scheduling the computation to the resources. We prove in theory that our off-line solution achieves the optimal performance. Based on the off-line solution, we further develop a set of online algorithms, and conduct extensive simulations to show that our proposed online algorithms significantly outperform the benchmark algorithms.

关键词： computation partitioning edge computing task dispatching task scheduling

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Multi-User computation partitioning for Latency Sensitive Mobile Cloud Applications

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IEEE TRANSACTIONS ON COMPUTERS 2015年第8期64卷 2253-2266页

作者： Yang, Lei Cao, Jiannong Cheng, Hui Ji, Yusheng Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Liverpool John Moores Univ Sch Comp & Math Sci Liverpool L3 5UX Merseyside England Natl Inst Informat Informat Syst Architecture Res Div Tokyo Japan

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users' computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces.

关键词： Mobile cloud computing offloading computation partitioning job scheduling

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Network Aware Mobile Edge computation partitioning in Multi-User Environments

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2021年第5期14卷 1478-1491页

作者： Yang, Lei Cao, Jiannong Wang, Zhenyu Wu, Weigang South China Univ Technol Sch Software Engn Guangzhou 510630 Guangdong Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Peoples R China

Mobile edge computation partitioning is an effective technique to improve the applications performance on the mobile devices by selectively offloading some computations from the devices to the nearby edge cloud. Most previous works focus on the computation partitioning for a single user. Recent works begin to study the computation partitioning in a multiple user environment in which a number of users compete for the constrained computation resources on the edge cloud. However, these works neglect the fact that the users normally also share the network resources to access the edge cloud, and thus the allocation of bandwidth to the users significantly affects the overall performance of the users. In this paper, we study network aware mobile edge computation partitioning in multi-user environments, i.e., to decide for each user which parts of the application should be offloaded onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is new in that we consider the competition among users for the network bandwidth as well as the computation resources in a multi-user environment. With a set of novel models and formulations, we transform the problem into the classic Multi-class Multi-dimensional Knapsack Problem, and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. We further consider the user mobility and design effective online algorithms that could be easily deployed in practical systems. Comprehensive trace driven simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.

关键词： Bandwidth Cloud computing Throughput Mobile handsets Heuristic algorithms computational modeling partitioning algorithms Mobile edge computing computation partitioning resource allocation

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Joint computation partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds 10

Joint Computation Partitioning and Resource Allocation for L...

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10th IEEE International Conference on Cloud Computing (CLOUD)

作者： Yang, Lei Liu, Bo Cao, Jiannong Sahni, Yuvraj Wang, Zhenyu South China Univ Technol Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538619933

The proliferation of mobile devices and ubiquitous access of the wireless network enables many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby mobile edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), to solve the problem. We compares MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency.

关键词： computation partitioning latency sensitive applications mobile edge cloud

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Network aware Multi-user computation partitioning in Mobile Edge Clouds 46

Network aware Multi-user Computation Partitioning in Mobile ...

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46th International Conference on Parallel Processing Workshops (ICPPW)

作者： Yang, Lei Cao, Jiannong Wang, Zhenyu Wu, Weigang South China Univ Technol Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538610428

Mobile edge cloud has been increasingly concerned by researchers due to its closer distance to mobile users than the traditional cloud on Internet. Offloading computations from mobile devices to the nearby edge cloud is an effective technique to accelerate the applications and/or save energy on the mobile devices. However, the mobile edge cloud usually has limited computation resources and constrained access bandwidth shared by multiple users in its proximity. Thus, allocation of resources and bandwidth among the users is significant to the overall application performance. In this paper, we study network aware multi-user computation partitioning problem in mobile edge clouds, i.e., to decide for each user which parts of the application should be offload onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is novel in that we consider the competition among users for both computing resources and bandwidth, and jointly optimizes the partitioning decisions with the allocation of resources and bandwidths among users, while most existing works either focus on the single user computation partitioning or study the multiple user computation partitioning without regard of the constrained network bandwidth. We first formulate the problem, and then transform it into the classic Multi-class Multi-dimensional Knapsack Problem and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. Comprehensive simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.

关键词： mobile edge cloud computation partitioning bandwidth allocation

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Delay-Sensitive computation partitioning for Mobile Augmented Reality Applications 28

Delay-Sensitive Computation Partitioning for Mobile Augmente...

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IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)

作者： Zhang, Chaokun Zheng, Rong Cui, Yong Li, Chenhe Wu, Jianping Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China McMaster Univ Dept Comp & Software Hamilton ON Canada Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728168876

Good user experiences in Mobile Augmented Reality (MAR) applications require timely processing and rendering of virtual objects on user devices. Today's wearable AR devices are limited in computation, storage, and battery lifetime. Edge computing, where edge devices are employed to offload part or all computation tasks, allows an acceleration of computation without incurring excessive network latency. In this paper, we use acyclic data flow graphs to model the computation and data flow in MAR applications and aim to minimize the makespan of processing input frames. Due to task dependencies and variable resource availability, makespan minimization is proven to be NP-hard in general. We design DPA, a polynomial-time heuristic algorithm for this problem. For special data flow graphs including chain or star, the algorithm can provide optimal solutions or solutions with a constant approximation ratio. The effectiveness of DPA has been evaluated using extensive simulations with realistic workloads and resource availability measured from a prototype implementation.

关键词： Edge computing Mobile augmented reality computation partitioning Precedence constraint

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partitioning Stateful Data Stream Applications in Dynamic Edge Cloud Environments

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第4期15卷 2368-2381页

作者： Ding, Shaoshuai Yang, Lei Cao, Jiannong Cai, Wei Tan, Mingkui Wang, Zhenyu South China Univ Technol Sch Software Engn Guangzhou 510641 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Guangdong Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly. The frequent updating of partitioning leads to high state migration cost between the mobile side and edge cloud. However, existing works don't take the state migration overhead into consideration. Consequently, the partitioning decisions may cause significant network congestion and increase overall completion time tremendously. In this article, with considering the state migration overhead, we propose a set of novel algorithms to update the partitioning based on the changing network bandwidth. To the best of our knowledge, this is the first work on computation partitioning for stateful data stream applications in dynamic environments. The algorithms aim to alleviate the network congestion and minimize the make-span through selectively migrating state in dynamic edge cloud environments. Extensive simulations show our solution not only could selectively migrate state but also outperforms other classical benchmark algorithms in terms of make-span. The proposed model and algorithms will enrich the scheduling theory for stateful tasks, which has not been explored before.

关键词： Cloud computing Mobile handsets partitioning algorithms Heuristic algorithms Object tracking Data models Servers Edge cloud computation partitioning stateful data stream applications

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