Energy-aware Dalvik Bytecode List Scheduling Technique for Mobile Applications

Smart connected devices such as smartphones and tablets are battery-operated to facilitate their mobility. Therefore, low power consumption is a critical requirement for mobile hardware and for the software designed for such devices. In addition to efficient power management techniques and new battery technologies based on nanomaterials, cloud computing has emerged as a promising technique for reducing energy consumption as well as augmenting the computational and memory capabilities of mobile devices. In this study, we designed and implemented a framework that allows for the energy-efficient execution of mobile applications by partially offloading the workload of a mobile device onto a resourceful cloud. This framework comprises a development toolkit, which facilitates the development of mobile applications capable of supporting computation offloading, and a runtime infrastructure for deployment in the cloud. Using this framework, we implemented three different mobile applications and demonstrated that considerable energy savings can be achieved compared with local processing for both resource-intensive and lightweight applications, especially when using high-speed networks such as Wi-Fi and Long-Term Evolution.

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ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Sensors ◽

10.3390/s22020660 ◽

2022 ◽

Vol 22 (2) ◽

pp. 660

Author(s):

Marios Avgeris ◽

Dimitrios Spatharakis ◽

Dimitrios Dechouniotis ◽

Aris Leivadeas ◽

Vasileios Karyotis ◽

...

Keyword(s):

Resource Allocation ◽

Mobile Applications ◽

Cloud Infrastructure ◽

Energy Aware ◽

Total Energy Consumption ◽

Optimal Resource ◽

Prediction Mechanism ◽

The Cost ◽

Different Characteristics ◽

Task Offloading

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

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A Framework for Optimization of Power Consumption in Mobile Computing Devices

Mehran University Research Journal of Engineering and Technology ◽

10.22581/muet1982.2003.18 ◽

2020 ◽

Vol 39 (3) ◽

pp. 635-646

Author(s):

Faiza Khadim ◽

Iram Noreen ◽

Abdul Hafeez Muhammad

Keyword(s):

Mobile Applications ◽

Middle Layer ◽

Mobile User ◽

Cellular Phones ◽

End User ◽

Energy Aware ◽

Daily Life Activities ◽

Battery Capacity ◽

Full Capacity ◽

Processing Engine

Battery driven computing devices such as laptops and cellular phones have become a necessity in this era. Mobile applications help us in daily life activities and with the rise of Internet of Things (IoT) new opportunities are open up to automate different task. However, batteries have their own limitations such as weight, cost, and size. Multiple applications and background processes running in parallel easily drain phone’s battery within 24 hours consequently annoying users by limited battery capacity. Repeated charge, recharge cycles steadily diminish the full capacity of batteries resulting in the immense decreased performance of the device. Therefore, mobile devices and mobile applications are in great need of energy-aware modules. In this paper, a survey is performed to identify the needs of the mobile user in the context of energy consumption problem. The results of survey lead authors to propose a middle layer energy aware framework to address this issue. The proposed framework highly relies on the association between the operating system, application, and end user. The main objective of the proposed framework is to maintain an energy-aware capability to facilitate end user and mobile applications. The major components of the proposed framework are processing engine, application classifier, application resource management, system profiling, application modes, power estimator and power policy management. Proposed framework also offers a policy manager algorithm based on research community feedback and survey's results. Proposed framework emphasizes on energy efficient execution of mobile operations for end user and operating systems.

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