Live Prefetching for Mobile Computation Offloading

Abstract

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 prefetcheddata 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.

Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

“…The use of caching to dynamically store the program and/or task data at the MEC system has been recently recognized as a cost-effective method to reduce computation delay, energy consumption, and bandwidth cost [3], [10]. Here, we refer to the techniques to cache the input and/or output of computation tasks at the server/user side as computation content caching (such as in [3]- [7]). On one hand, content caching reduces the data exchange between the edge servers and MUs if the required input data can be found in the cache.…”

Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

“…For the case of offloading (t k,n > 0), let g k denote the channel power gain between mobile k and the BS, which is assumed to be constant during the computation offloading for each mobile. Based on a widely-used empirical model in [4], [6], [31], [32], the transmission power, denoted by p t,n , can be modeled by a monomial function with respect to the achievable transmission rate (in bits/s) r k,n = k,n /t k,n :…”

“…2 gives the normalized signal power per symbol versus the rate, where the monomial order of (m = 3) can fairly approximate the transmission power. 6 Thus, the offloading energy consumption can be modeled by the following monomial function with respect to k,n and t k,n :…”

Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications

“…Allocating more computing resources in the network and supplying user devices with appropriate communications technology to improve the energy efficiency via offloaded computation pose new challenges. These include the need for reliable wireless connectivity and failure-free remote processing of information, which could be resolved with the advent of communication and computing cooperation (3C) techniques [14,15].…”