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

Kwon, Yongin; Lee, Sang‐Min; Yi, Hayoon; Kwon, Donghyun; Yang, Su‐Geun; Chun, Byung-Gon; Huang, Ling; Maniatis, Petros; Naik, Mayur; Paek, Yunheung

doi:10.1109/tmc.2014.2374153

Cited by 16 publications

(10 citation statements)

References 48 publications

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“…To compare EMCO against current state-of-the-art, we have developed a generic code offloading framework which (i) makes offloading decisions based on network latency and estimated energy, but (ii) has no support for diagnosing cloud performance and allocates requests to a random server instead (in the experiments these correspond to m3.medium for the chess app and m3.2xlarge for the backtracking app). Our baseline does not directly follow any single system, but incorporates mechanisms commonly found in current offloading frameworks 12 [2], [3], [6], [8].…”

Section: Response Time and Energy Footprintmentioning

confidence: 99%

“…For the chess app (top figure), we can observe that EMCO is capable of adjusting the offloading environment according to characteristics of the computing task and current context, which results in a stable and fast response time throughout. The performance of the baseline suffers during complex intermediate states as the system cannot 12. https://github.com/huberflores/CodeOffloadingAnnotations adapt to the changing computational requirements.…”

Section: Response Time and Energy Footprintmentioning

confidence: 99%

“…For example, the extent of energy savings from reduced CPU usage depend on temperature and the number of applications running on a device [10]. Similarly, network type, device model, and the capacity and current load of the cloud surrogate have a major impact on offloading performance [11], [12]. Quantifying and characterizing the effect of these factors on a single device is clearly infeasible due to the inherent complexity and diversity of these factors.…”

Section: Introductionmentioning

confidence: 99%

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Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

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Section: Response Time and Energy Footprintmentioning

confidence: 99%

Section: Response Time and Energy Footprintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…Zhu and Shen observe the energy disproportionality in multicore devices, where the first running CPU incurs much higher power cost than each additional core does [6]. Kwon et al propose a framework that predicts the computational resource consumption on mobile devices using program analysis and machine learning [29].…”

Section: 4mentioning

confidence: 99%

An Adaptive and Integrated Low-Power Framework for Multicore Mobile Computing

Choi

Jung

Choi

et al. 2017

Mobile Information Systems

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Employing multicore in mobile computing such as smartphone and IoT (Internet of Things) device is a double-edged sword. It provides ample computing capabilities required in recent intelligent mobile services including voice recognition, image processing, big data analysis, and deep learning. However, it requires a great deal of power consumption, which causes creating a thermal hot spot and putting pressure on the energy resource in a mobile device. In this paper, we propose a novel framework that integrates two well-known low-power techniques, DPM (Dynamic Power Management) and DVFS (Dynamic Voltage and Frequency Scaling) for energy efficiency in multicore mobile systems. The key feature of the proposed framework is adaptability. By monitoring the online resource usage such as CPU utilization and power consumption, the framework can orchestrate diverse DPM and DVFS policies according to workload characteristics. Real implementation based experiments using three mobile devices have shown that it can reduce the power consumption ranging from 22% to 79%, while affecting negligibly the performance of workloads.

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“…Because iOS has best architecture and memory management system [29]. Their applications are designed on specific and strict criteria due to security issues.…”

Section: Performance In Iosmentioning

confidence: 99%

Resource management and security issues in mobile phone operating systems: A comparative analysis

Awan

Waqar

Faseeh

et al. 2017

Preprint

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Nowadays Mobile phones are becoming more popular in our daily lives. Mobile technology has a great effect on human life. Our daily tasks are dependent on mobile devices. Memory Management (MM), Security and Performance plays an important role in every handheld device specially in mobile phones, which are very much dependent on their operating system (OS). These embedded operating systems are on the driving seat when we talk about efficient and useful memory management and secure handling. Three popular OS in mobile phones are Android, Windows and iOS (iPhone OS). Each OS has its own way of managing the memory and provide it to certain number of applications. Android is an open software available for the people to modify as per their needs. But Windows and iOS operating systems didn"t allow their software as open source. Researchers have done a large amount of work using different mechanisms and decision makings to develop new ways to manage the memory of these OS"s. This work shows a comparative analysis of different memory management and security related techniques in above three operating systems. In this paper, we present the analysis of memory management and security in mobile phone operating systems with respect to apps, main memory, cache memory and virtual memory. Also, we compare the overall performance of these OS"s in terms of MM, security concerns. This study will help in finding better operating system in terms of efficient memory management and security.

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

Cited by 16 publications

References 48 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

An Adaptive and Integrated Low-Power Framework for Multicore Mobile Computing

Resource management and security issues in mobile phone operating systems: A comparative analysis

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