CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications

Chae, Dongju; Kim, Jihun; Kim, Jangwoo; Kim, Jong; Yang, Su‐Geun; Cho, Yeongpil; Kwon, Yongin; Paek, Yunheung

doi:10.1109/ccgrid.2014.75

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…Figure 8 Complexity of Target App. Cuckoo [7] No (Manual) Static Yes Java Complex Li et al [10] No (Manual) Static No C Simple Roam [2] No (Manual) Dynamic Yes Java Complex MAUI [4] No (Annotation) Dynamic Yes C# Complex ThinkAir [8] No (Annotation) Dynamic Yes Java Complex Wang and Li [14] No (Annotation) Dynamic No C Simple DiET [13] Yes Static Yes Java Simple Chen et al [1] Yes Dynamic Yes Java Simple HELVM [12,15] Yes Dynamic Yes Java Simple OLIE [6,11] Yes Dynamic Yes Java Complex CloneCloud [3] Yes Dynamic Yes Java Complex COMET [5] Yes Dynamic Yes Java Complex CMcloud [9,31] Yes …”

Section: Battery Consumptionmentioning

confidence: 99%

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Architecture-aware automatic computation offload for native applications

Lee

Park

Heo

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

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Although mobile devices have been evolved enough to support complex mobile programs, performance of the mobile devices is lagging behind performance of servers. To bridge the performance gap, computation offloading allows a mobile device to remotely execute heavy tasks at servers. However, due to architectural differences between mobile devices and servers, most existing computation offloading systems rely on virtual machines, so they cannot offload native applications. Some offloading systems can offload native mobile applications, but their applicability is limited to wellanalyzable simple applications. This work presents automatic cross-architecture computation offloading for generalpurpose native applications with a prototype framework that is called Native Offloader. At compile-time, Native Offloader automatically finds heavy tasks without any annotation, and generates offloading-enabled native binaries with memory unification for a mobile device and a server. At run-time, Native Offloader efficiently supports seamless migration between the mobile device and the server with a unified virtual address space and communication optimization. Native Offloader automatically offloads 17 native C applications from SPEC CPU2000 and CPU2006 benchmark suites without a virtual machine, and achieves a geomean program speedup of 6.42× and battery saving of 82.0%.

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Section: Battery Consumptionmentioning

confidence: 99%

“…Narayanan et al [33] and CMcloud [9,31] use logging data and machine learning methods to predict the performance of mobile applications. Wolski el al.…”

Section: Related Workmentioning

confidence: 99%

Architecture-aware automatic computation offload for native applications

Lee

Park

Heo

et al. 2015

Proceedings of the 48th International Symposium on Microarchitecture

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show abstract

“…Building a separate predictor for every device/server an application may potentially use is burdensome as well. Some techniques [57] to predict an application's performance by scaling the prediction from a reference device according to the new target device's specification would help in this matter. Yunheung Paek received the BSc and MSc degrees in computer engineering from Seoul National University, Korea in 1988 and 1990, respectively.…”

mentioning

confidence: 99%

Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices

Kwon

Lee

et al. 2015

IEEE Trans. on Mobile Comput.

Self Cite

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We present Mantis, a framework for predicting the Computational Resource Consumption(CRC) of Android applications on given inputs accurately, and efficiently. A key insight underlying Mantis is that program codes often contain features that correlate with performance and these features can be automatically computed efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program's CRC metric yet can be evaluated efficiently from the program's input. We apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts four CRC metrics of seven Android apps with estimation error in the range of 0-11.1% by executing predictor code spending at most 1.3% of their execution time on Galaxy Nexus.

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“…Papers [6][7][8]10] only consider the energy and delay as optimization objects that ignore the management of cloud resources. Moreover, some works [11,12,14] focus on resource management for task offloading, which do not consider utilization rate of cloud resources. These works rarely consider utilization rate of PM in cloud server and delay caused by arranging the offloaded task to different PMs in the cloud simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…This new paradigm could reduce the costs of employing a cloud platform without affecting the user experience. Chae et al [14] proposed a cost-effective mobile-to-cloud offloading platform, which aimed at minimizing the server costs and the user service fee. Based on ensuring the performance of target applications, the platform offloaded as many applications to the same server as possible.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Wang

Ren

2018

Future Internet

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Abstract:Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

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CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications

Cited by 14 publications

References 20 publications

Architecture-aware automatic computation offload for native applications

Architecture-aware automatic computation offload for native applications

Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

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