9 research outputs found
Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks
This paper considers computation offloading in fog-radio access networks
(F-RAN), where multiple user equipments (UEs) offload their computation tasks
to the F-RAN through a number of fog nodes. Each UE can choose one of the fog
nodes to offload its task, and each fog node may simultaneously serve multiple
UEs. Depending on the computation burden at the fog nodes, the tasks may be
computed by the fog nodes or further offloaded to the cloud via
capacity-limited fronthaul links. To compute all UEs tasks as fast as possible,
joint optimization of UE-Fog association, radio and computation resources of
F-RAN is proposed to minimize the maximum latency of all UEs. This min-max
problem is formulated as a mixed integer nonlinear program (MINP). We first
show that the MINP can be reformulated as a continuous optimization problem,
and then employ the majorization minimization (MM) approach to finding a
solution for it. The MM approach that we develop herein is unconventional in
that---each MM subproblem is inexactly solved with the same provable
convergence guarantee as the conventional exact MM. In addition, we also
consider a cooperative offloading model, where the fog nodes
compress-and-forward their received signals to the cloud. Under this model, a
similar min-max latency optimization problem is formulated and tackled again by
the inexact MM approach. Simulation results show that the proposed algorithms
outperform some heuristic offloading strategies, and that the cooperative
offloading is generally better than the non-cooperative one.Comment: 11 pages, 8 figure
Application-Aware Consensus Management for Software-Defined Intelligent Blockchain in IoT
Currently, IoT has become an important carrier of blockchains, which not only makes blockchain more ubiquitous but also improves the security of IoT. Consensus is the core component of blockchains with various forms, which raises the following challenges. Dynamic management and configuration of the consensuses in a blockchain are required because IoT applications have high dynamics. Moreover, an IoT node is usually reutilized by various applications in different blockchains, which means the IoT node should be switched frequently to cross consensuses in different blockchains. To address this, a software-defined blockchain architecture is proposed to realized the dynamic configurations for blockchains. Then a consensus function virtualization approach with application-aware work flow is proposed, which can abstract and manage various consensus resources. Next, a transfer-learning-based intelligent scheme is designed to implement the application- layer packet analysis and perform the efficient management of virtualized consensus resources. Experiment results indicate the feasibility of the proposed scheme. This work is significant in enhancing the flexibility and extendibility of blockchains in IoT
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC
Joint Radio and Computational Resource Allocation in IoT Fog Computing
The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.peerReviewe