Survey of End-to-End Mobile Network Measurement Testbeds, Tools, and Services

Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in measuring end-to-end mobile network performance to, among other goals, minimize negative impact on application responsiveness. In this survey we examine current approaches to end-to-end mobile network performance measurement, diagnosis, and application prototyping. We compare available tools and their shortcomings with respect to the needs of researchers, developers, regulators, and the public. We intend for this survey to provide a comprehensive view of currently active efforts and some auspicious directions for future work in mobile network measurement and mobile application performance evaluation.Comment: Submitted to IEEE Communications Surveys and Tutorials. arXiv does not format the URL references correctly. For a correctly formatted version of this paper go to http://www.cs.montana.edu/mwittie/publications/Goel14Survey.pd

Enhancing Mobile Capacity through Generic and Efficient Resource Sharing

Mobile computing devices are becoming indispensable in every aspect of human life, but diverse hardware limits make current mobile devices far from ideal for satisfying the performance requirements of modern mobile applications and being used anytime, anywhere. Mobile Cloud Computing (MCC) could be a viable solution to bypass these limits which enhances the mobile capacity through cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. Traditional schemes either restrict to share a specific type of hardware resource within individual applications, which requires tremendous reprogramming efforts; or disregard the runtime execution pattern and transmit too much unnecessary data, resulting in bandwidth and energy waste.To address the aforementioned challenges, we present three novel designs of resource sharing frameworks which utilize the various system resources from a remote or personal cloud to enhance the mobile capacity in a generic and efficient manner. First, we propose a novel method-level offloading methodology to run the mobile computational workload on the remote cloud CPU. Minimized data transmission is achieved during such offloading by identifying and selectively migrating the memory contexts which are necessary to the method execution. Second, we present a systematic framework to maximize the mobile performance of graphics rendering with the remote cloud GPU, during which the redundant pixels across consecutive frames are reused to reduce the transmitted frame data. Last, we propose to exploit the unified mobile OS services and generically interconnect heterogeneous mobile devices towards a personal mobile cloud, which complement and flexibly share mobile peripherals (e.g., sensors, camera) with each other

Device-to-Device Mobile Gaming

Tänu läbimurretele mobiilsete seadmete ja sotsiaalvõrgustikes vastastikuse mobiilse suhtlemise valdkondades on seadmelt-seadmele (ingl. k. device-to-device) mobiilsed mängud muutunud aktuaalseks trendiks. Selleks, et säästa rakenduste poolt nõutavat energiat ja kiirendada nende reaktsiooniaega, on võimalikuks vahendiks kasutada koodi mahalaadimist pilve vahendusel või seadmelt-seadmele. Teatavasti on andmevahetuses eelistatud madal latentsusaeg, mille tõttu on seadmelt-seadmele mahalaadimine sobilikum. Sellegipoolest ei ole lähedal asuvale seadmele mahalaadimine praktikas otstarbekas, sest kasutaja ei pruugi olla nõus teise seadme poolt edastatud ülesande lahendamises, kuna sellega kaasneb lisanduv energia kadu. Antud töös läheneme probleemile uuest küljest: selle asemel, et lasta teisel seadmel töö ära teha on võimalik kasutada juba lahendatud ülesannete tulemusi. Püstitatud eesmärgi saavutamiseks arendati välja raamistik ja teostati juhtumiuuring. Valideerimise tulemusele põhinedes leidsime, et lähedal asuvate, omavahel ühendatud seadmete puhul on võimalik vähendada rakenduse koormust.Device-to-Device(D2D) mobile gaming is a new trend which is emerging as a result of the increasing advances in mobile devices and social network interaction with mobile peers. As these games are played between players in proximity, it is possible to take advantage of computational offloading to balance the load of these applications. Smartphone games can be instrumentalized with computational offloading mechanisms in order to save energy and increase response time of the applications. In this context, remote cloud and D2D offloading has been proposed. It is well known that low latency is preferable to high latency in the communication when offloading, and as a result, D2D offloading is more suitable than remote cloud. However, the idea of offloading to a nearby device is not feasible in practice, because a user may not be willing to process the task from another device. This can be clearly seen as processing a task from another device does not represent a gain but rather a loss in resources for the device that executes the task. In this thesis, we investigate a new perspective, in which a device is not requested to process a task, but it is alleviated from processing one task that another device has already processed. To achieve this purpose, we develop a framework and a case study. Based on the result of the validation, we found out that it is possible to balance the execution load of an application between nearby interconnected devices

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models