Tactics-Based Remote Execution for Mobile Computing

into a computing giant able to run resource-intensive applications such as natural language translation, speech recognition, face recognition, and augmented reality. However, easily partitioning these applications for remote execution while retaining application-specific information has proven to be a difficult challenge. In this paper, we show that automated dynamic repartitioning of mobile applications can be reconciled with the need to exploit application-specific knowledge. We show that the useful knowledge about an application relevant to remote execution can be captured in a compact declarative form called tactics. Tactics capture the full range of meaningful partitions of an application and are very small relative to code size. We present the design of a tactics-based remote execution system, Chroma, that performs comparably to a runtime system that makes perfect partitioning decisions. Furthermore, we show that Chroma can automatically use extra resources in an overprovisioned environment to improve application performance

Mobile, collaborative augmented reality using cloudlets

The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment

Vision: a Lightweight Computing Model for Fine-Grained Cloud Computing

Cloud systems differ fundamentally in how they offer and charge for resources. While some systems provide a generic programming abstraction at coarse granularity, e.g., a virtual machine rented by the hour, others offer specialized abstractions with fine-grained accounting on a per-request basis. In this paper, we explore Tasklets, an abstraction for instances of short-duration, generic computations that migrate from a host requiring computation to hosts that are willing to provide computation. Tasklets enable fine-grained accounting of resource usage, enabling us to build infrastructure that supports trading computing resources according to various economic models. This computation model is especially attractive in settings where mobile devices can utilize resources in the cloud to mitigate local resource constraints

Leveraging cloudlets for immersive collaborative applications

To enable immersive applications on mobile devices, the authors propose a component-based cyber foraging framework that optimizes application-specific metrics by not only offloading but also configuring application components at runtime. It also enables collaborative scenarios by sharing components between multiple devices

A Low-Energy Fast Cyber Foraging Mechanism for Mobile Devices

The ever increasing demands for using resource-constrained mobile devices for running more resource intensive applications nowadays has initiated the development of cyber foraging solutions that offload parts or whole computational intensive tasks to more powerful surrogate stationary computers and run them on behalf of mobile devices as required. The choice of proper mix of mobile devices and surrogates has remained an unresolved challenge though. In this paper, we propose a new decision-making mechanism for cyber foraging systems to select the best locations to run an application, based on context metrics such as the specifications of surrogates, the specifications of mobile devices, application specification, and communication network specification. Experimental results show faster response time and lower energy consumption of benched applications compared to when applications run wholly on mobile devices and when applications are offloaded to surrogates blindly for execution.Comment: 12 pages, 7 figures, International Journal of Wireless & Mobile Networks (IJWMN

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