Live Prefetching for Mobile Computation Offloading

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio

Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing

Scavenging the idling computation resources at the enormous number of mobile devices can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing. This paper considers a co-computing system where a user offloads computation of input-data to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based on a predicted helper's CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Last, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms.Comment: Submitted to possible journa

eXCloud: Transparent runtime support for scaling mobile applications in cloud

Cloud computing augments applications with ease-of-access to the enormous resources on the Internet. Combined with mobile computing technologies, mobile applications can exploit the Cloud everywhere by statically distributing code segments or dynamically migrating running processes onto cloud services. Existing migration techniques are however too coarse-grained for mobile devices, so the overheads often offset the benefits of migration. To build a truly elastic mobile cloud computing infrastructure, we introduce eXCloud (eXtensible Cloud) - a middleware system with multi-level mobility support, ranging from as coarse as a VM instance to as fine as a runtime stack frame, and allows resources to be integrated and used dynamically. In eXCloud, a stack-on-demand (SOD) approach is used to support computation mobility throughout the mobile cloud environment. The approach is fully adaptive, goal-driven and transparent. By downward task migration, applications running on the cloud nodes can exploit or take control of special resources in mobile devices such as GPS and cameras. With a restorable MPI layer, task migrations of MPI parallel programs can happen between cloud nodes or be initiated from a mobile device. Our evaluation shows that SOD outperforms several existing migration mechanisms in terms of migration overhead and latency. All our techniques result in better resource utilization through task migrations among cloud nodes and mobile nodes.published_or_final_versionThe 2011 International Conference on Cloud and Service Computing (CSC), Hong Kong, China, 12-14 December 2011. In Proceedings of CSC, 2011, p. 103-11

HORST -Home Router Sharing based on Trust

Abstract-Today's Internet services are increasingly accessed from mobile devices, thus being responsible for growing load in mobile networks. At the same time, more and more WiFi routers are deployed such that a dense coverage of WiFi is available. Results from different related works suggest that there is a high potential of reducing load on the mobile networks by offloading data to WiFi networks, thereby improving mobile users' quality of experience (QoE) with Internet services. Additionally, the storage of the router could be used for content caching and delivery close to the end user, which is more energy efficient compared to classical content servers, and saves costs for network operators by reducing traffic between autonomous systems. Going one step beyond, we foresee that merging these approaches and augmenting them with social information from online social networks (OSNs) will result both in even less costs for network operators and increased QoE of end users. Therefore, we propose home router sharing based on trust (HORST) -a socially-aware traffic management solution which targets three popular use cases: data offloading to WiFi, content caching/prefetching, and content delivery

A Decade of Research in Fog computing: Relevance, Challenges, and Future Directions

Recent developments in the Internet of Things (IoT) and real-time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud-centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics etc. However, after a decade of research, we still do not see large-scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small-scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This paper summarizes the technical, non-functional and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The paper also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real-time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.Comment: Accepted for publication at Wiley Software: Practice and Experience journa