Browsing by Author "Canberk B."

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    GA-based Energy Aware Path Planning Framework for Aerial Network Assistance
    (European Alliance for Innovation, 2021) Özçevik' Y.; Bozkaya E.; Akkoç M.; Erol M.R.; Canberk B.
    Aerial networks have enormous potential to assist terrestrial communications under heavy traffic requests for a predictable duration. However, such potential for improving both the performance and the coverage through the use of drones can face a major challenge in terms of power limitation. Hence, we consider the energy consumption characteristic of the components in such networks to provide energy aware flight path planning. For this purpose, a flight path planning scheme is proposed on an underlying topology graph that models the energy consumption of path traversals in the aerial network. In the proposed model, we offer to seek for the minimum energy consumption on a global problem domain during the entire operational time. Thus, we provide a concrete problem formulation and implement a flight path planning with Genetic Algorithms (GA) approach. Moreover, a novel end-system initiated handover procedure is illustrated to preserve connectivity of terrestrial users in the network architecture. In the end, the evaluation of the proposed model is conducted under three different scales of social event scenarios. A comparison with a dummy path planning scheme without energy awareness concerns is presented according to a set of parameters. The evaluation outcomes show that the proposed model is able to save 20% energy consumption, provides 15% less number of terrestrial replenishment, and 18% more average endurance for the topology. Besides, another energy aware path planning scheme in the literature offering a deployment with Bellman Ford algorithm is also included in the evaluation to evaluate the feasibility of the proposed framework for the enhanced problem domain. © 2021 OZCEVIK et al., licensed to EAI. All Rights Reserved.
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    OFaaS: OpenFlow Switch as a Service for Multi Tenant Slicing in SD-CDN
    (Institute of Electrical and Electronics Engineers Inc., 2021) Erel-Özçevik M.; Canberk B.
    In Software Defined-Content Delivery Networks (SD-CDN), the policies of tenants such as Youtube, Netflix, Office 365, etc. are not the same due to having different 5G traffic requirements for such contents as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency (URLLC) and massive machine-type communication (mMTC). This leads SD-CDN multi-tenant slicing to provide different services with limited network resources, where each tenant can functionally manage their own virtual slice of a physical component according to service level agreements (SLAs). However, they are not permitted to dynamically configure their own components. Therefore, the physical end-to-end configuration of all edge devices causes extra hardware and bandwidth costs. Although software as a service (SaaS) is more preferred to handle cost-efficiency on a switch configuration that increases forwarding throughput (Mbps) with a less number of physical components in SD-CDN, the edge devices can be only served as infrastructure as a service (IaaS) currently. This motivation leads us to serve the switch as a service that includes both IaaS and SaaS characteristics. Therefore, we propose an OpenFlow as a service (OFaaS) design where each tenant has flow management and switch configuration permissions on their own virtual slice. In flow management, we define a novel Service Oriented Architecture (SOA) to orchestrate OFaaS driven topology by isolating each tenant from physical complexity. Here, each tenant can dynamically alter QoS on a flow and load balance between a sub-set of contents via OpenFlow protocol. In switch configuration; a novel OFaaS Management Algorithm for a multi-tenant slicing increases the number of tenants served per OpenFlow switch thanks to OFaaS design. It enables an end-to-end configuration via the NETCONF protocol with a novel YANG model of OFaaS. According to performance evaluation; OFaaS has the same forwarding throughput as conventional IaaS based OpenFlow switch for a homogenous content, whereas it has 71% more forwarding throughput (Mbps) and it has 40% more cost-efficient than conventional one for a heterogeneous content with $17 savings per tenant. © 2021 IEEE.
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    Energy-aware mobility for aerial networks: A reinforcement learning approach
    (John Wiley and Sons Ltd, 2022) Bozkaya E.; Özçevik Y.; Akkoç M.; Erol M.R.; Canberk B.
    With recent advancements in aerial networks, aerial base stations (ABSs) have become a promising mobile network technology to enhance the coverage and capacity of the cellular networks. ABS deployment can assist cellular networks to support network infrastructure or minimize the disruptions caused by unexpected and temporary situations. However, with 3D ABS placement, the continuity of the service has increased the challenge of providing satisfactory Quality of Service (QoS). The limited battery capacity of ABSs and continuous movement of users result in frequent interruptions. Although aerial networks provide quick and effective coverage, ABS deployment is challenging due to the user mobility, increased interference, handover delay, and handover failure. In addition, once an ABS is deployed, an intelligent management must be applied. In this paper, we model user mobility pattern and formulate energy-aware ABS deployment problem with a goal of minimizing energy consumption and handover delay. To this end, the contributions of this paper are threefold: (i) analysis of reinforcement learning (RL)-based state action reward state action (SARSA) algorithm to deploy ABSs with an energy consumption model, (ii) predicting the user next-place with a hidden Markov model (HMM), and (iii) managing the dynamic movement of ABSs with a handover procedure. Our model is validated by comprehensive simulation, and the results indicate superiority of the proposed model on deploying multiple ABSs to provide the communication coverage. © 2021 John Wiley & Sons, Ltd.