Binary Task Offloading Model For Mobile Edge Computing using NDN Architecture

Driven by the advantages of Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) in nextgeneration networks we propose an architecture for MEC using Named Data Networking (NDN). NDN is one of the prominent architectures of ICN having features like unique-naming, in-network caching, inherit support for multicasting, and support for mobility. Placing MEC in NDN provides the additional facilities of edge computing like pushing of resource-hungry and time-critical applications of the mobile devices to the edge-computing server. Therefore, one of the research challenges is the decision regarding the task offloading process by the end-users to the edge-computing server. We propose a mathematical model that enables the end-user to take decisions in Yes/No regarding the binary task offloading process

Review on Network Function Virtualization in Information-Centric Networking

Network function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to 'named information' or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures. © 2021 IEEE

Towards green computing in wireless sensor networks: controlled mobility-aided balanced tree approach

Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance

Antenna allocation and pricing in virtualized massive MIMO networks via Stackelberg game

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output (MIMO) system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO’s problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations