Refactoring android Java code for on-demand computation offloading

Zhang, Ying; Huang, Gang; Liu, Xuanzhe; Zhang, Wei; Mei, Hong; Yang, Songchun

doi:10.1145/2384616.2384634

Cited by 81 publications

(39 citation statements)

References 16 publications

(6 reference statements)

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“…Since the queueing delay of C3P is small, this time interval will result in the delay of D C3P n less than or equal to r C3P n E[β n,i ] for calculating r C3P n packets by helper n. On the other hand, the collector stops sending packets to helpers once it receives R + K packets collectively from all helpers. Again, since the queueing delay of C3P is small, i.e., the period of time packets wait in the queue of helper n to be computed is small, at the time that R + K packets are collected at the collector, there might be only small number of packets waiting at the queue of each helper and thus N n=1 r C3P n ≃ R + K. In addition, with small queueing delay, all helpers will finish their assigned tasks approximately at the same time; this results in (17) and (18).…”

Section: Appendix C: Proof Of Theoremmentioning

confidence: 99%

Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge

Keshtkarjahromi

Xing

Seferoğlu

2018

2018 IEEE 26th International Conference on Network Protocols (ICNP)

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Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into subtasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to timevarying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.

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Section: Appendix C: Proof Of Theoremmentioning

confidence: 99%

Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge

Keshtkarjahromi

Xing

Seferoğlu

2018

2018 IEEE 26th International Conference on Network Protocols (ICNP)

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“…The choice of the offloading scheme would be a critical decision, as it is the core functionality, which transforms mobile application to MCA. [3][4][5][6] Assuming that a development team chooses to use an existing scheme, they will need to evaluate and compare between existing offloading schemes. Two offloading schemes have been selected, 1 based on single thresholding 4 -ST for brevity and another based on multilayer perceptron 3 -known as properly offloading mobile applications to clouds (POMAC).…”

Section: Motivating Examplementioning

confidence: 99%

“…research. 3,5 The purpose of using case study applications from the literature is because of their identified taxonomy (for offloadable tasks). Table 3 presents details of the mobile applications used.…”

Section: The Metrics Collector Interfacementioning

confidence: 99%

“…4 Five scenarios are defined by 1 study 4 for evaluating ST; they are Smartphone only, offloading with all objects, offloading with necessary objects, offloading with necessary objects (delta), and offloading with threshold check. 5 Decision making is the check on the environment conditions of the communications, which influence the offloading. Decision making mechanisms can be based on single (static) thresholds 4 or predictive learning.…”

Section: Notesmentioning

confidence: 99%

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BEFTIGRE: Behaviour‐driven full‐tier green evaluation of mobile cloud applications

Chinenyeze

Liu

Al‐Dubai

2017

J Software Evolu Process

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With the resource‐constrained nature of mobile devices and the resource‐abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource intensive tasks from mobile devices to the cloud. Although these schemes address similar questions within the same domain of mobile cloud application (MCA) optimisation, evaluation is tailored to the scheme and also solely mobile focused, thus making it difficult to clearly compare with other existing counterparts. In this work, we first analyse the existing/commonly adopted evaluation technique, then with the aim to fill the above gap, we propose the behaviour‐driven full‐tier green evaluation approach, which adopts the behaviour‐driven concept for evaluating MCA performance and energy usage—ie, green metrics. To automate the evaluation process, we also present and evaluate the effectiveness of a resultant application program interface and tool driven by the behaviour‐driven full‐tier green evaluation approach. The application program interface is based on Android and has been validated with Elastic Compute Cloud instance. Experiments show that Beftigre is capable of providing a more distinctive, comparable, and reliable green test results for MCAs.

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“…Although the bulk of the original research on program partitioning for managed languages took place during the first decade of this century, recently this technique reemerged under the guise of cloud offloading, a technique for increasing the energy efficiency of mobile applications [1,2,4,8,9]. Our vision is that offloading and insourcing transformations should be developed in concert with each other as complementary operations.…”

Section: Related Workmentioning

confidence: 99%

Automated component insourcing for mobile applications

Tilevich

2013

Proceedings of the 2013 ACM Workshop on Mobile Development Lifecycle

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To alleviate the resource constraints of mobile devices, developers of mobile applications commonly apply the cloud offloading optimization-placing an application's energy and performance intensive functionality to execute at a remote cloud server. Because of the high heterogeneity of mobile hardware and the variability of mobile networks, a cloud offloading optimization may become detrimental to energy consumption and performance efficiency. Reverting to the original, non-distributed version of the application may be infeasible, as the client and server parts may since have been maintained and enhanced independently. In that case, the components executed at the server for optimization purposes need to be moved to be executed on the mobile device; the moved components must be integrated with the rest of the application's functionality. To assist software developers with this non-trivial program restructuring, this paper introduces component insourcing, an automated program transformation that moves a remotely accessed component to be invoked locally, within a shared address space. This position paper motivates the need for component insourcing and demonstrates how this transformation can be implemented in the Java realm. The paper also discusses the technical difficulties of preserving the original component behavior and ensuring good performance in the presence of component insourcing.

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Refactoring android Java code for on-demand computation offloading

Cited by 81 publications

References 16 publications

Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge

Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge

BEFTIGRE: Behaviour‐driven full‐tier green evaluation of mobile cloud applications

Automated component insourcing for mobile applications

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