Benchmark applications used in mobile cloud computing research: a systematic mapping study

Silva, Francisco Airton; Zaicaner, Germano; Quesado, Eder; Dornelas, Matheus; Silva, Bruno; Maciel, Paulo

doi:10.1007/s11227-016-1674-2

Cited by 28 publications

(16 citation statements)

References 59 publications

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“…An instance follows a utility computing model, whereby consumers pay on the basis of their usage and type preferences. The cost of the instance is proportional to its type, which in turn determines the level of acceleration at which a task is executed and energy required in the communication [28]. Moreover, the type of the instance also defines its capacity to handle multiple offloading requests at once.…”

Section: Augmented Resource Allocationmentioning

confidence: 99%

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: Augmented Resource Allocationmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…Applications in computation offloading have various types and QoE demand [11], and we classify these applications into two categories: latency-sensitive applications and latencytolerant applications. Then, we use the applications listed in the paper [12] as benchmarks, which are type of cognitive assistance applications [13].…”

Section: A Benchmarksmentioning

confidence: 99%

Distributed and Application-Aware Task Scheduling in Edge-Clouds

Xiong

Lin

2018

2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

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Edge computing is an emerging technology which places computing at the edge of the network to provide an ultralow latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading have a wide variety of types, to guarantee the user quality of experience (QoE) in terms of task types is challenging. In this paper, we present Petrel, a distributed and application-aware task scheduling framework for edge-clouds. Petrel implements a sample-based load balancing technology and further adopts adaptive scheduling policies according to task types. This application-aware scheduling not only provides QoE guarantee but also improves the overall scheduling performance. Trace-driven simulations show that Petrel achieves a significant improvement over existing scheduling strategies.

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“…The cost of the instance is proportional to its type. The type of the instance is important as it determines the acceleration in which a task is executed, which affects the overall response time of the offloading request [9]. Moreover, the type of the instance also defines its capacity to handle multiple offloading requests at once.…”

Section: B Computational Provisioningmentioning

confidence: 99%

Large-scale offloading in the Internet of Things

Flores

Kostakos

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Large-scale deployments of IoT devices are subject to energy and performance issues. Fortunately, offloading is a promising technique to enhance those aspects. However, several problems still remain open regarding cloud deployment and provisioning. In this paper, we address the problem of provisioning offloading as a service in large-scale IoT deployments. We design and develop an AutoScaler, an essential component for our offloading architecture to handle offloading workload. In addition, we also develop an offloading simulator to generate dynamic offloading workload of multiple devices. With this toolkit, we study the effect of task acceleration in different cloud servers and analyze the capacity of several cloud servers to handle multiple concurrent requests. We conduct multiple experiments in a real testbed to evaluate the system and present our experiences and lessons learned. From the results, we find that the AutoScaler component introduces a very small overhead of ≈150 milliseconds in the total response time of a request, which is a fair price to pay to empower the offloading architectures with multi-tenancy ability and dynamic horizontal scaling for IoT scenarios.

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Benchmark applications used in mobile cloud computing research: a systematic mapping study

Cited by 28 publications

References 59 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

Distributed and Application-Aware Task Scheduling in Edge-Clouds

Large-scale offloading in the Internet of Things

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