From Mobiles to Clouds: Developing Energy-Aware Offloading Strategies for Workflows

Gao, Bo; He, Ligang; Liu, Limin; Li, Kenli; Jarvis, Stephen A.

doi:10.1109/grid.2012.20

Cited by 32 publications

(14 citation statements)

References 21 publications

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“…An example is the offloading to nearby stationary machine in wireless local area network approach that was proposed by [8] for a cloudlet-based computational offloading application. The primary objective of their work is to reduce energy consumption of mobile devices when the devices are participating in a workflow process initiated by a remote computer via the Internet.…”

Section: Remote Central Server As a Workflow Manager (Rcwfm)mentioning

confidence: 99%

“…Based on (8), the E of ta can be generated, which is the minimum resource requirement of ta. The number of ta each h l can handle depends on the resource availability given by the h l .…”

Section: Parallel Task Delegationmentioning

confidence: 99%

“…Workflow Management Systems (WfMS) have been applied in numerous Mobile Cloud Computing (MCC) applications [1,3,4,8,17] to provide efficient management of complex composite services. Workflows in MCC can be classified into two basic types:…”

Section: Mobile Cloud Workflow Systemsmentioning

confidence: 99%

See 2 more Smart Citations

SPiCa

Chang

Srirama

Ling

2014

Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia

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Mobile devices are capable of acting as smart assistances not only to serve their users but also to collaborate with each other remotely via wireless Internet to accomplish common goals. The latter is achieved by establishing a Social Private Cloud (SPC). SPC is a cluster formed by a scalable group of social network participants using their mobile devices that are capable of providing their resources to accomplish computational tasks. In this paper, we propose a workflow-based SPiCa framework that enables task delegation in SPC. In order to support adaptive task scheduling based on resource availabilities, a resource-aware task scheduling scheme has been proposed and implemented as a proof of concept. The evaluation demonstrates that the framework is capable of dynamically reacting to runtime changes in order to adjust the task delegation process.

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Section: Remote Central Server As a Workflow Manager (Rcwfm)mentioning

confidence: 99%

“…Based on (8), the E of ta can be generated, which is the minimum resource requirement of ta. The number of ta each h l can handle depends on the resource availability given by the h l .…”

Section: Parallel Task Delegationmentioning

confidence: 99%

See 1 more Smart Citation

SPiCa

Chang

Srirama

Ling

2014

Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia

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“…Energy-aware computational offloading techniques have been studied in many recent works [4,7,8,10,[14][15][16] for augmenting resource-poor mobile devices leveraging cloud resources. For example, in [8], the authors proposed a system called MAUI that supports fine-grained code offload to maximize energy conservation without imposing heavy burden on programmers for code partitioning.…”

Section: Related Workmentioning

confidence: 99%

EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing

Xiang

Chen

2014

Wireless Netw

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Mobile cloud computing (MC2) is emerging as a new computing paradigm that seeks to augment resourceconstrained mobile devices for executing computing-and/ or data-intensive mobile applications. Nonetheless, the energy-poverty nature of mobile devices has become a stumbling block that greatly impedes the practical application of MC2. Fortunately, for delay-tolerant mobile applications, energy conservation is achievable via two means: (1) dynamic selection of energy-efficient links (e.g., WiFi interface); and (2) deferring data transmission in bad connectivity. In this paper, we study the problem of energy-efficient downlink and uplink data transmission between mobile devices and clouds. In the presence of unpredictable data arrival, network availability and link quality, our objective is to minimize the time average energy consumption of a mobile device while ensuring the stability of both device-end and cloud-end queues. To achieve this goal, we propose an online control framework named EcoPlan under which mobile users can make flexible link selection and data transmission scheduling decisions to achieve arbitrary energy-delay tradeoffs. Realworld trace-driven simulations demonstrate the effectiveness of EcoPlan, along with its superior energy-efficiency over alternative WiFi-prioritized, minimum-delay and SALSA schemes.

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“…Thirdly, the offloading is used for the load balancing between servers. Fourthly, the offloading technique helped the limited devices, for instance, smartphones to save energy according to Stephen, et al, [4]. The offloading technique has not only one benefit but as well might provide more than one value at the same time.…”

Section: Introductionmentioning

confidence: 99%

New cloud offloading algorithm for better energy consumption and process time

Aldmour

Yousef

Yaghi

et al. 2016

Int J Syst Assur Eng Manag

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Abstract. Offloading in cloud computing is a way to execute big files in short times due to the available processing resources on core computers. However in some cases it is vital to execute the file locally on the node if the file size is less than a threshold size. There is a trade off in this issue due to the limited power of the node, therefore, in this paper a novel algorithm is proposed where the file size in each case is measured and then a decision is taken to either execute the file on the node or to send the file to be processed in the core cloud. The main reason is to save time of the execution of the file. However, the second and important reason, is to save the limited node energy in some large file, where the power consumption of the node will be very high. The measurement of the file size and the execution time and the power consumption for the local node and the core cloud is measured to represent an input to the execution decision

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From Mobiles to Clouds: Developing Energy-Aware Offloading Strategies for Workflows

Cited by 32 publications

References 21 publications

SPiCa

SPiCa

EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing

New cloud offloading algorithm for better energy consumption and process time

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