537 research outputs found
Mobile cloud computing and network function virtualization for 5g systems
The recent growth of the number of smart mobile devices and the emergence of complex multimedia mobile applications have brought new challenges to the design of wireless mobile networks. The envisioned Fifth-Generation (5G) systems are equipped with different technical solutions that can accommodate the increasing demands for high date rate, latency-limited, energy-efficient and reliable mobile communication networks.
Mobile Cloud Computing (MCC) is a key technology in 5G systems that enables the offloading of computationally heavy applications, such as for augmented or virtual reality, object recognition, or gaming from mobile devices to cloudlet or cloud servers, which are connected to wireless access points, either directly or through finite-capacity backhaul links. Given the battery-limited nature of mobile devices, mobile cloud computing is deemed to be an important enabler for the provision of such advanced applications. However, computational tasks offloading, and due to the variability of the communication network through which the cloud or cloudlet is accessed, may incur unpredictable energy expenditure or intolerable delay for the communications between mobile devices and the cloud or cloudlet servers. Therefore, the design of a mobile cloud computing system is investigated by jointly optimizing the allocation of radio, computational resources and backhaul resources in both uplink and downlink directions. Moreover, the users selected for cloud offloading need to have an energy consumption that is smaller than the amount required for local computing, which is achieved by means of user scheduling.
Motivated by the application-centric drift of 5G systems and the advances in smart devices manufacturing technologies, new brand of mobile applications are developed that are immersive, ubiquitous and highly-collaborative in nature. For example, Augmented Reality (AR) mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the cloud, and data delivery in the downlink. Therefore, the optimization of the shared computing and communication resources in MCC not only benefit from the joint allocation of both resources, but also can be more efficiently enhanced by sharing the offloaded data and computations among multiple users. As a result, a resource allocation approach whereby transmitted, received and processed data are shared partially among the users leads to more efficient utilization of the communication and computational resources.
As a suggested architecture in 5G systems, MCC decouples the computing functionality from the platform location through the use of software virtualization to allow flexible provisioning of the provided services. Another virtualization-based technology in 5G systems is Network Function Virtualization (NFV) which prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. For that reason, the development of fault-tolerant virtualization strategies for MCC and NFV is necessary to ensure reliability of the provided services
Ruin Theory for User Association and Energy Optimization in Multi-access Edge Computing
In this letter, a novel framework is proposed for analyzing data offloading
in a multi-access edge computing system. Specifically, a two-phase algorithm,
is proposed, including two key phases: \emph{1) user association phase} and
\emph{2) task offloading phase}. In the first phase, a ruin theory-based
approach is developed to obtain the users association considering the users'
transmission reliability. Meanwhile, in the second phase, an optimization-based
algorithm is used to optimize the data offloading process. In particular, ruin
theory is used to manage the user association phase, and a ruin
probability-based preference profile is considered to control the priority of
proposing users. Here, ruin probability is derived by the surplus buffer space
of each edge node at each time slot. Giving the association results, an
optimization problem is formulated to optimize the amount of offloaded data
aiming at minimizing the energy consumption of users. Simulation results show
that the developed solutions guarantee system reliability under a tolerable
value of surplus buffer size and minimize the total energy consumption of all
users.Comment: This paper has been submitted to IEEE Wireless Communications Letter
Socially Trusted Collaborative Edge Computing in Ultra Dense Networks
Small cell base stations (SBSs) endowed with cloud-like computing
capabilities are considered as a key enabler of edge computing (EC), which
provides ultra-low latency and location-awareness for a variety of emerging
mobile applications and the Internet of Things. However, due to the limited
computation resources of an individual SBS, providing computation services of
high quality to its users faces significant challenges when it is overloaded
with an excessive amount of computation workload. In this paper, we propose
collaborative edge computing among SBSs by forming SBS coalitions to share
computation resources with each other, thereby accommodating more computation
workload in the edge system and reducing reliance on the remote cloud. A novel
SBS coalition formation algorithm is developed based on the coalitional game
theory to cope with various new challenges in small-cell-based edge systems,
including the co-provisioning of radio access and computing services,
cooperation incentives, and potential security risks. To address these
challenges, the proposed method (1) allows collaboration at both the user-SBS
association stage and the SBS peer offloading stage by exploiting the ultra
dense deployment of SBSs, (2) develops a payment-based incentive mechanism that
implements proportionally fair utility division to form stable SBS coalitions,
and (3) builds a social trust network for managing security risks among SBSs
due to collaboration. Systematic simulations in practical scenarios are carried
out to evaluate the efficacy and performance of the proposed method, which
shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author
Energy Efficient Resource Allocation for Mobile-Edge Computation Networks with NOMA
This paper investigates an uplink non-orthogonal multiple access (NOMA)-based
mobile-edge computing (MEC) network. Our objective is to minimize the total
energy consumption of all users including transmission energy and local
computation energy subject to computation latency and cloud computation
capacity constraints. We first prove that the total energy minimization problem
is a convex problem, and it is optimal to transmit with maximal time. Then, we
accordingly proposed an iterative algorithm with low complexity, where
closed-form solutions are obtained in each step. The proposed algorithm is
successfully shown to be globally optimal. Numerical results show that the
proposed algorithm achieves better performance than the conventional methods.Comment: 7 pages 5 figures. arXiv admin note: text overlap with
arXiv:1807.1184
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