arXiv subjects

Sanaa H. Mohamed

Publications and source records attributed to Sanaa H. Mohamed.

18 recordsLinked to original sources

arXiv2024

Resilience in PON-based data centre architectures with two-tier cascaded AWGRs

This paper investigates the performance of a two-tier AWGR-based Passive Optical Network (PON) data centre architecture against an AWGR-based PON data centre architecture by considering various scenarios involving link failures to evaluate the resilience of both designs. To optimize traffic routing under different failure scenarios, a Mixed Integer Linear Programming (MILP) model is developed and the power consumption and delay performance is assessed. The results demonstrate that the two-tier AWGR architecture reduced the power consumption and the delay compared to the AWGR-based architecture by up to 10% and 61%, respectively.

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arXiv2024

Access-Point to Access-Point Connectivity for PON-based OWC Spine and Leaf Data Centre Architecture

In this paper, we propose incorporating Optical Wireless Communication (OWC) and Passive Optical Network (PON) technologies into next generation spine-and-leaf Data Centre Networks (DCNs). In this work, OWC systems are used to connect the Data Centre (DC) racks through Wavelength Division Multiplexing (WDM) Infrared (IR) transceivers. The transceivers are placed on top of the racks and at distributed Access Points (APs) in the ceiling. Each transceiver on a rack is connected to a leaf switch that connects the servers within the rack. We replace the spine switches by Optical Line Terminal (OLT) and Network Interface Cards (NIC) in the APs to achieve the desired connectivity. We benchmark the power consumption of the proposed OWC-PON-based spine-and-leaf DC against traditional spine-and-leaf DC and report 46% reduction in the power consumption when considering eight racks.

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arXiv2023

WDM/TDM over Passive Optical Networks with Cascaded-AWGRs for Data Centers

Data centers based on Passive Optical Networks (PONs) can provide high capacity, low cost, scalability, elasticity and high energy-efficiency. This paper introduces the use of WDM-TDM multiple access in a PON-based data center that offers multipath routing via two-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to improve the utilization of resources. A Mixed Integer Linear Programming (MILP) model is developed to optimize resource allocation while considering multipath routing. The results show that all-to-all connectivity is achieved in the architecture through the use of two different wavelength within different time slots for the communication between racks in the same or different cells, as well as with the OLT switches.

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arXiv2023

Relay Assisted Multiuser OWC Systems under Human Blockage

This paper proposes using cooperative communication based on optoelectronic (O-E-O) amplify-and-forward relay terminals to reduce the influence of the blockage and shadowing resulting from human movement in a beam steering Optical Wireless Communication (OWC) system. The simulation results indicate that on average, the outage probability of the cooperative communication mode with O-E-O relay terminals is two orders of magnitude lower than the outage probability of the system without relay terminals.

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arXiv2023

Multiuser beam steering OWC system based on NOMA

In this paper, we propose applying Non-Orthogonal Multiple Access (NOMA) technology in a multiuser beam steering OWC system. We study the performance of the NOMA-based multiuser beam steering system in terms of the achievable rate and Bit Error Rate (BER). We investigate the impact of the power allocation factor of NOMA and the number of users in the room. The results show that the power allocation factor is a vital parameter in NOMA-based transmission that affects the performance of the network in terms of data rate and BER.

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arXiv2023

Energy Efficient Resource Allocation for Demand Intensive Applications in a VLC Based Fog Architecture

In this paper, we propose an energy efficient passive optical network (PON) architecture for backhaul connectivity in indoor visible light communication (VLC) systems. The proposed network is used to support a fog computing architecture designed to allow users with processing demands to access dedicated fog nodes and idle processing resources in other user devices (UDs) within the same building. The fog resources within a building complement fog nodes at the access and metro networks and the central cloud data center. A mixed integer linear programming (MILP) model is developed to minimize the total power consumption associated with serving demands over the proposed architecture. A scenario that considers applications with intensive demands is examined to evaluate the energy efficiency of the proposed architecture. A comparison is conducted between allocating the demands in the fog nodes and serving the demands in the conventional cloud data center. Additionally, the proposed architecture is compared with an architecture based on state-of-art Spine-and-Leaf (SL) connectivity. Relative to the SL architecture and serving all the demands in the cloud, the adoption of the PON-based architecture achieves 84% and 86% reductions, respectively.

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arXiv2022

Energy Efficient VM Placement in a Heterogeneous Fog Computing Architecture

Recent years have witnessed a remarkable development in communication and computing systems, mainly driven by the increasing demands of data and processing intensive applications such as virtual reality, M2M, connected vehicles, IoT services, to name a few. Massive amounts of data will be collected by various mobile and fixed terminals that will need to be processed in order to extract knowledge from the data. Traditionally, a centralized approach is taken for processing the collected data using large data centers connected to a core network. However, due to the scale of the Internet-connected things, transporting raw data all the way to the core network is costly in terms of the power consumption, delay, and privacy. This has compelled researchers to propose different decentralized computing paradigms such as fog computing to process collected data at the network edge close to the terminals and users. In this paper, we study, in a Passive Optical Network (PON)-based collaborative-fog computing system, the impact of the heterogeneity of the fog units capacity and energy-efficiency on the overall energy-efficiency of the fog system. We optimized the virtual machine (VM) placement in this fog system with three fog cells and formulated the problem as a mixed integer linear programming (MILP) optimization model with the objective of minimizing the networking and processing power consumption of the fog system. The results indicate that in our proposed architecture, the processing power consumption is the crucial element to achieve energy efficient VMs placement.

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arXiv2022

Energy Efficient Placement of ML-Based Services in IoT Networks

The Internet of Things (IoT) is gaining momentum in its quest to bridge the gap between the physical and the digital world. The main goal of the IoT is the creation of smart environments and self-aware things that help to facilitate a variety of services such as smart transport, climate monitoring, e-health, etc. Huge volumes of data are expected to be collected by the connected sensors/things, which in traditional cases are processed centrally by large data centers in the core network that will inevitably lead to excessive transportation power consumption as well as added latency overheads. Instead, fog computing has been proposed by researchers from industry and academia to extend the capability of the cloud right to the point where the data is collected at the sensing layer. This way, primitive tasks that can be hosted in IoT sensors do not need to be sent all the way to the cloud for processing. In this paper we propose energy efficient embedding of machine learning (ML) models over a cloud-fog network using a Mixed Integer Linear Programming (MILP) optimization model. We exploit virtualization in our framework to provide service abstraction of Deep Neural Networks (DNN) layers that can be composed into a set of VMs interconnected by virtual links. We constrain the number of VMs that can be processed at the IoT layer and study the impact on the performance of the cloud fog approach.

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arXiv2022

Energy-Efficient VM Placement in PON-based Data Center Architectures with Cascaded AWGRs

Data centers based on Passive Optical Networks (PONs) can offer scalability, low cost and high energy-efficiency. Application in data centers can use Virtual Machines (VMs) to provide efficient utilization of the physical resources. This paper investigates the impact of VM placement on the energyefficiency in a PON-based data center architecture that utilizes cascaded Arrayed Waveguide Grating Routers (AWGRs). In this paper, we develop a Mixed Integer Linear Programming (MILP) optimization model to optimize the VM placement in the proposed PON-based data center architecture. This optimization aims to minimize the power consumption of the networking and computing by placing the VMs and their demands in the optimum number of resources (i.e., servers and networking devices) in the data center. We first minimize the processing power consumption only and then we minimize the processing and networking power consumption. The results show that a reduction in the networking power consumption by up to 75% is achieved when performing joint minimization of processing and networking power consumption compared to considering the minimization of the processing power consumption only.

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arXiv2022

OWC-enabled Spine and Leaf Architecture Towards Energy Efficient Data Center Networks

Due to the emergence of new paradigms and services such as 5G/6G, IoT, and more, current deployed wired Data Center Networks (DCNs) are not meeting the required performance metrics due to their limited reconfigurability, scalability, and throughput. To that end, wireless DCNs using technologies such as Optical Wireless Communication (OWC) have become viable and costeffective solutions as they offer higher capacity, better energy efficiency, and better scalability. This paper proposes an OWC-based spine and leaf DCNs where the leaf switches are enabled with OWC transceivers, and the spine switches are replaced by Access Points (APs) in the ceiling connected to a backbone network. The APs are interconnected through a Passive Optical Network (PON) that also connects the architecture with upper network layers. An Infrared (IR) OWC system that employs Wavelength Division Multiplexing (WDM) is proposed to enhance the DCN downlink communication. The simulation (i.e., channel modeling) results show that our proposed data center links achieve good data rates in the data center up to 15 Gbps. For the PON, Arrayed Waveguide Grating Routers (AWGRs) that enable WDM are proposed to connect the APs. We evaluate the performance of the considered architecture in term of its power efficiency compared to traditional spine and leaf data centers. The results show that the OWC-enabled DCN reduces the power consumption by 42% compared to traditional the spine and leaf architecture.

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arXiv2022

Energy Efficient PON Backhaul Network for a VLC Based Fog Architecture

In this paper, an energy efficient passive optical network (PON) architecture is proposed for backhaul connectivity in indoor visible light communication (VLC) systems. The proposed network is used to support a fog computing architecture allowing users with processing demands to access dedicated fog nodes and idle processing resources in other user devices within a building. The fog resources within a building complement fog nodes at higher layers of the access and metro networks and the central cloud data center. A mixed integer linear programming (MILP) model is developed to minimize the total power consumption associated with serving demands over the proposed architecture. The results show that the PON backhaul network improves the energy efficiency of fog computing by 66% for networking and 12% for processing compared to an architecture based on state-of-the-art Spine-and-Leaf connectivity.

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arXiv2021

Optimized Passive Optical Networks with Cascaded-AWGRs for Data Centers

The use of Passive Optical Networks (PONs) in modern and future data centers can provide energy efficiency, high capacity, low cost, scalability, and elasticity. This paper introduces a passive optical network design with 2-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to connect groups of racks (i.e. cells) within a data center. This design employs a Software-Defined Networking (SDN) controller to manage the routing and assignment of the networking resource while introducing multiple paths between any two cells to improve routing, load balancing and resilience. We provide benchmarking results for the power consumption to compare the energy efficiency of this design to state-of-the-art data centers. The results indicate that the cascaded AWGRs architecture can achieve up to 43% saving in the networking power consumption compared to Fat-Tree data center architecture.

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arXiv2021

Angle Diversity Trasmitter For High Speed Data Center Uplink Communications

This paper proposes an uplink optical wireless communication (OWC) link design that can be used by data centers to support communication in spine and leaf architectures between the top of rack leaf switches and large spine switches whose access points are mounted in the ceiling. The use of optical wireless links reduces cabling and allows easy reconfigurability for example when data centres expand. We consider three racks in a data center where each rack contains an Angle Diversity Transmitter (ADT) positioned on the top of the rack to realize the uplink function of a top-of-the-rack (ToR) or a leaf switch. Four receivers are considered to be installed on the ceiling where each is connected to a spine switch. Two types of optical receivers are studied which are a Wide Field-of-View Receiver (WFOVR) and an Angle Diversity Receiver (ADR). The performance of the proposed system is evaluated when the links run at data rates higher than 19 Gbps. The results indicate that the proposed approach achieves excellent performance using simple On-Off Keying (OOK)

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arXiv2021

Energy Efficient Resource Allocation in Federated Fog Computing Networks

There is a continuous growth in demand for time sensitive applications which has shifted the cloud paradigm from a centralized computing architecture towards distributed heterogeneous computing platforms where resources located at the edge of the network are used to provide cloud-like services. This paradigm is widely known as fog computing. Virtual machines (VMs) have been widely utilized in both paradigms to enhance the network scalability, improve resource utilization, and energy efficiency. Moreover, Passive Optical Networks (PONs) are a technology suited to handling the enormous volumes of data generated in the access network due to their energy efficiency and large bandwidth. In this paper, we utilize a PON to provide the connectivity between multiple distributed fog units to achieve federated (i.e. cooperative) computing units in the access network to serve intensive demands. We propose a mixed integer linear program (MILP) to optimize the VM placement in the federated fog computing units with the objective of minimizing the total power consumption while considering inter-VM traffic. The results show a significant power saving as a result of the proposed optimization model by up to 52%, in the VM-allocation compared to a baseline approach that allocates the VM requests while neglecting the power consumption and inter-VMs traffic in the optimization framework.

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arXiv2021

Energy Minimized Federated Fog Computing over Passive Optical Networks

The rapid growth of time-sensitive applications and services has driven enhancements to computing infrastructures. The main challenge that needs addressing for these applications is the optimal placement of the end-users demands to reduce the total power consumption and delay. One of the widely adopted paradigms to address such a challenge is fog computing. Placing fog units close to end-users at the edge of the network can help mitigate some of the latency and energy efficiency issues. Compared to the traditional hyperscale cloud data centres, fog computing units are constrained by computational power, hence, the capacity of fog units plays a critical role in meeting the stringent demands of the end-users due to intensive processing workloads. In this paper, we aim to optimize the placement of virtual machines (VMs) demands originating from end-users in a fog computing setting by formulating a Mixed Integer Linear Programming (MILP) model to minimize the total power consumption through the use of a federated architecture made up of multiple distributed fog cells. The obtained results show an increase in processing capacity in the fog layer and a reduction in the power consumption by up to 26% compared to the Non-Federated fogs network.

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arXiv2021

Energy-Efficient Distributed Machine Learning in Cloud Fog Networks

Massive amounts of data are expected to be generated by the billions of objects that form the Internet of Things (IoT). A variety of automated services such as monitoring will largely depend on the use of different Machine Learning (ML) algorithms. Traditionally, ML models are processed by centralized cloud data centers, where IoT readings are offloaded to the cloud via multiple networking hops in the access, metro, and core layers. This approach will inevitably lead to excessive networking power consumptions as well as Quality-of-Service (QoS) degradation such as increased latency. Instead, in this paper, we propose a distributed ML approach where the processing can take place in intermediary devices such as IoT nodes and fog servers in addition to the cloud. We abstract the ML models into Virtual Service Requests (VSRs) to represent multiple interconnected layers of a Deep Neural Network (DNN). Using Mixed Integer Linear Programming (MILP), we design an optimization model that allocates the layers of a DNN in a Cloud/Fog Network (CFN) in an energy efficient way. We evaluate the impact of DNN input distribution on the performance of the CFN and compare the energy efficiency of this approach to the baseline where all layers of DNNs are processed in the centralized Cloud Data Center (CDC).

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arXiv2021

Energy-Efficient AI over a Virtualized Cloud Fog Network

Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deployment on the edge devices alone. Thus, most AI models are offloaded to distant cloud data centers (CDCs), which tend to consolidate large amounts of computing and storage resources into one or more CDCs. Deploying services in the CDC will inevitably lead to excessive latencies and overall increase in power consumption. Instead, fog computing allows for cloud services to be extended to the edge of the network, which allows for data processing to be performed closer to the end-user device. However, different from cloud data centers, fog nodes have limited computational power and are highly distributed in the network. In this paper, using Mixed Integer Linear Programming (MILP), we formulate the placement of DNN inference models, which is abstracted as a network embedding problem in a Cloud Fog Network (CFN) architecture, where power savings are introduced through trade-offs between processing and networking. We study the performance of the CFN architecture by comparing the energy savings when compared to the baseline approach which is the CDC.

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arXiv2020

PON-based connectivity for fog computing

Fog computing plays a crucial role in satisfying the requirements of delay-sensitive applications such as connected vehicles, smart grids, and actuator networks by moving data processing close to end users. Passive optical networks (PONs) are widely used in access networks to reduce the power consumption while providing high bandwidth to end users under flexible designs. Typically, distributed fog computing units in access networks have limited processing and storage capacities that can be under or over utilized depending on instantaneous demands. To extend the available capacity in access network, this paper proposes a fog computing architecture based on SDN-enabled PONs to achieve full connectivity among distributed fog computing servers. The power consumption results show that this architecture can achieve up to about 80% power savings in comparison to legacy fog computing based on spine and leaf data centers with the same number of servers.

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