Mohammad Aldossary - Academia.edu (original) (raw)

Papers by Mohammad Aldossary

Research paper thumbnail of Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Computer Systems Science and Engineering

Research paper thumbnail of Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Computers, Materials & Continua

Research paper thumbnail of Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures

IEEE Access

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-to-end network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Research paper thumbnail of Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Sensors

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining p... more Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be ad...

Research paper thumbnail of Energy Efficient UAV-Based Service Offloading over Cloud-Fog Architectures

Cornell University - arXiv, May 14, 2022

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-toend network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Research paper thumbnail of An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

Computers, materials & continua, 2022

Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and... more Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060, NEOM city has been proposed to be the "Saudi hub" for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global electricity demand by 2030. Thus, reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions. Therefore, this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming (MILP) model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia. This approach considers the Saudi National Fiber Network (SNFN) as the backbone of Saudi Internet infrastructure. First, we compare two different scenarios of data center locations. The first scenario considers a traditional cloud data center located in Jeddah and Riyadh, whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure. Then, we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs. After that, we optimize the energy efficiency of different cloud data centers' locations (in the SNFN) to reduce the associated carbon emissions and energy costs. Simulation results show that the proposed approach can save up to 94% of the carbon emissions and 62% of the energy cost compared to the current cloud physical topology. These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources. Finally, we design a heuristic algorithm to verify the proposed approach, and it gives equivalent results to the MILP model.

Research paper thumbnail of A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

Computers, Materials & Continua, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the most cost-effective decision(s) to handle the service performance variation. The evaluation on a Cloud testbed shows that the proposed approach is capable of predicting the workload and power consumption as well as estimating the live migration and auto-scaling total cost for VMs during service operation, with a high prediction accuracy based on historical workload patterns.

Research paper thumbnail of Energy Consumption-based Pricing Model for Cloud Computing

Pricing mechanisms employed by di erent service providers significantly influence the role of clo... more Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

Research paper thumbnail of A Review of Dynamic Resource Management in Cloud Computing Environments

Computer Systems Science and Engineering, 2021

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used t... more In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.

Research paper thumbnail of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

Future Generation Computer Systems, 2018

This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in clou... more This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments.

Research paper thumbnail of Delay-Optimal Task Offloading for UAV-Enabled Edge-Cloud Computing Systems

IEEE Access

The emergence of delay-sensitive and computationally-intensive mobile applications and services p... more The emergence of delay-sensitive and computationally-intensive mobile applications and services pose a significant challenge for Unmanned Aerial Vehicles (UAVs) devices due to the scarcity in their resources such as computational power and battery lifetime. Mobile cloud computing has been introduced as a promising solution to overcome these limitations through task offloading. However, high-latency and security issues are considered the main challenges of this paradigm. Subsequently, the edge-cloud computing paradigm has been introduced and widely used to help to mitigate these issues. Nevertheless, the current task offloading models permit UAVs to execute their intensive tasks at the connected edge server, which leads to excessive loads due to the large number of UAVs and thereby increases the delay. Therefore, in this paper, we propose a delay-optimal task offloading approach for multi-tier edge-cloud computing in a multiuser environment. The problem is formulated as an optimization model using Integer Linear Programming (ILP) techniques to minimize the total service time of UAVs. Simulation results demonstrate that the proposed approach not only saves the service time by 33.5% and 55% for edge and cloud execution policies respectively, but also scales well for a large number of UAVs. INDEX TERMS Computation offloading, edge-cloud computing, mobile edge computing, optimization, Internet of Things, unmanned aerial vehicles, linear programming. I. INTRODUCTION Nowadays, the Internet of Things (IoT) is fully embraced in virtually every aspect of our lives, owing to the advances made in Unmanned Aerial Vehicles (UAVs), sensors, and communication technologies. Such advancements have aided in the proliferation of complex IoT applications, which can generate and process a massive amount of data [1]. However, UAVs have limited resources onboard such as battery and computational power that restricts the execution of such types of applications on these devices [2], [3]. To alleviate these limitations and achieve the required communication and processing delay, intensive computations can be transmitted and remotely processed at more resourceful devices via the computation offloading concept [4], [5]. Consequently, two types of computation offloading models are proposed namely binary offloading [6] and partial of-floading [7]. In the case of binary offloading, the computation task is either executed locally at the UAV or offloaded and remotely executed at the edge server, whereas, in partial offloading, the computation task is divided into two parts, one for local computing and the other one for remote execution.

Research paper thumbnail of A Review of Energy-related Cost Issues and Prediction Models in Cloud Computing Environments

Computer Systems Science and Engineering, 2021

Research paper thumbnail of Performance and energy-based cost prediction modelling of virtual machines in cloud computing environments

Cloud Computing has transformed the way in which enterprises and individuals are utilising the In... more Cloud Computing has transformed the way in which enterprises and individuals are utilising the Information Technology (IT) by offering on-demand services such as applications, platforms and infrastructures for their customers with reasonable prices based on their usage (e.g., pay-as-you-go model). However, the wide adoption of Cloud Computing and the growing number of Cloud customers have increased the overall operational costs for Cloud providers, especially with the increasing cost of energy consumed to operate Cloud services. Consequently, Cloud providers consider energy consumption as one of the most important cost factors to be maintained within their infrastructures. In order to achieve energy efficiency and reduce the operational costs for Cloud services, reactive and proactive management mechanisms can be used to efficiently manage Cloud resources and reduce energy-related costs while maintaining service performance requirements. However, these mechanisms need to be supporte...

Research paper thumbnail of Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Intelligent Automation & Soft Computing, 2021

Research paper thumbnail of Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment

IEEE Access

The current agriculture systems compete to take advantage of industry advanced technologies, incl... more The current agriculture systems compete to take advantage of industry advanced technologies, including the internet of things (IoT), cloud/fog/edge computing, artificial intelligence, and agricultural robots to monitor, track, analyze and process various functions and services in real-time. Additionally, these technologies can make the agricultural processes smarter and more cost-efficient by using automated systems and eliminating any human interventions, hence enhancing agricultural production to meet future expectations. Although the current agriculture systems that adopt the traditional cloud-based architecture have provided powerful computing infrastructure to distributed IoT sensors. However, the cost of energy consumption associated with transferring heterogeneous data over the multiple network tiers to process, analyze and store the sensor's information in the cloud has created a huge load on information and communication infrastructure. Besides, the energy consumed by cloud data centers has an environmental impact associated with using non-clean fuels, which usually release carbon emissions (CO2) to produce electricity. Thus, to tackle these issues, we propose a new integrated edge-fog-cloud architectural paradigm that promises to enhance the energy-efficient of smart agriculture systems and corresponding carbon emissions. This architecture allows data collection from several sensors to process and analyze the agriculture data that require real-time operation (e.g., weather temperature, soil moisture, soil acidity, irrigation, etc.) in several layers (edge, fog, and cloud). Thus, the real-time processing could be held by the edge and fog layers to reduce the load on the cloud layer, which will help to enhance the overall energy consumption and process the agriculture applications/services efficiently. Mathematical modeling is conducted using mixed-integer linear programming (MILP) for a smart agriculture environment, where the proposed architecture is implemented, and results are analyzed and compared to the traditional implementation. According to the results of thousands of agriculture sensors, the proposed architecture outperforms the traditional cloud-based architecture in terms of reducing the overall energy consumption by 36% and the carbon emissions by 43%. In addition to these achievements, the results show that our proposed architecture can reduce network traffic by up to 86%, which can reduce network congestion. Finally, we develop a heuristic algorithm to validate and mimic the presented approach, and it shows comparable results to the MILP model. INDEX TERMS Smart agriculture, edge-fog-cloud computing, internet of things, energy-efficiency, carbon emission. Hatem A. Alharbi received the B.Sc. degree in Computer Engineering (Hons.

Research paper thumbnail of Modeling and Analyzing Offloading Strategies of IoT Applications over Edge Computing and Joint Clouds

Symmetry

Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For en... more Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the ...

Research paper thumbnail of Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

Computers, Materials & Continua

Research paper thumbnail of A novel approach for IoT tasks offloading in edge-cloud environments

Journal of Cloud Computing

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased ... more Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are cond...

Research paper thumbnail of A Hybrid Approach for Performance and Energy-based Cost Prediction in Clouds

A Hybrid Approach for Performance and Energy-based Cost Prediction in Clouds, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the mo...

Research paper thumbnail of Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Proceedings of the 8th International Conference on Cloud Computing and Services Science, 2018

Virtual Machines (VMs) live migration is one of the important approaches to improve resource util... more Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

Research paper thumbnail of Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

Computer Systems Science and Engineering

Research paper thumbnail of Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Computers, Materials & Continua

Research paper thumbnail of Energy Efficient UAV-Based Service Offloading Over Cloud-Fog Architectures

IEEE Access

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-to-end network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Research paper thumbnail of Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Sensors

Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining p... more Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be ad...

Research paper thumbnail of Energy Efficient UAV-Based Service Offloading over Cloud-Fog Architectures

Cornell University - arXiv, May 14, 2022

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future servi... more Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-toend network power consumption (EENPC), processing power consumption (PPC), UAV's total flight distance (UAVTFD), and UAV's total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV's propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV's coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

Research paper thumbnail of An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

Computers, materials & continua, 2022

Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and... more Based on the Saudi Green initiative, which aims to improve the Kingdom's environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060, NEOM city has been proposed to be the "Saudi hub" for green energy, since NEOM is estimated to generate up to 120 Gigawatts (GW) of renewable energy by 2030. Nevertheless, the Information and Communication Technology (ICT) sector is considered a key contributor to global energy consumption and carbon emissions. The data centers are estimated to consume about 13% of the overall global electricity demand by 2030. Thus, reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon emissions. Therefore, this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming (MILP) model to reduce the carbon emissions associated with ICT infrastructure in Saudi Arabia. This approach considers the Saudi National Fiber Network (SNFN) as the backbone of Saudi Internet infrastructure. First, we compare two different scenarios of data center locations. The first scenario considers a traditional cloud data center located in Jeddah and Riyadh, whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy infrastructure. Then, we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy costs. After that, we optimize the energy efficiency of different cloud data centers' locations (in the SNFN) to reduce the associated carbon emissions and energy costs. Simulation results show that the proposed approach can save up to 94% of the carbon emissions and 62% of the energy cost compared to the current cloud physical topology. These savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy sources. Finally, we design a heuristic algorithm to verify the proposed approach, and it gives equivalent results to the MILP model.

Research paper thumbnail of A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

Computers, Materials & Continua, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the most cost-effective decision(s) to handle the service performance variation. The evaluation on a Cloud testbed shows that the proposed approach is capable of predicting the workload and power consumption as well as estimating the live migration and auto-scaling total cost for VMs during service operation, with a high prediction accuracy based on historical workload patterns.

Research paper thumbnail of Energy Consumption-based Pricing Model for Cloud Computing

Pricing mechanisms employed by di erent service providers significantly influence the role of clo... more Pricing mechanisms employed by di erent service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how di erent pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

Research paper thumbnail of A Review of Dynamic Resource Management in Cloud Computing Environments

Computer Systems Science and Engineering, 2021

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used t... more In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.

Research paper thumbnail of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

Future Generation Computer Systems, 2018

This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in clou... more This is a repository copy of Energy-aware cost prediction and pricing of virtual machines in cloud computing environments.

Research paper thumbnail of Delay-Optimal Task Offloading for UAV-Enabled Edge-Cloud Computing Systems

IEEE Access

The emergence of delay-sensitive and computationally-intensive mobile applications and services p... more The emergence of delay-sensitive and computationally-intensive mobile applications and services pose a significant challenge for Unmanned Aerial Vehicles (UAVs) devices due to the scarcity in their resources such as computational power and battery lifetime. Mobile cloud computing has been introduced as a promising solution to overcome these limitations through task offloading. However, high-latency and security issues are considered the main challenges of this paradigm. Subsequently, the edge-cloud computing paradigm has been introduced and widely used to help to mitigate these issues. Nevertheless, the current task offloading models permit UAVs to execute their intensive tasks at the connected edge server, which leads to excessive loads due to the large number of UAVs and thereby increases the delay. Therefore, in this paper, we propose a delay-optimal task offloading approach for multi-tier edge-cloud computing in a multiuser environment. The problem is formulated as an optimization model using Integer Linear Programming (ILP) techniques to minimize the total service time of UAVs. Simulation results demonstrate that the proposed approach not only saves the service time by 33.5% and 55% for edge and cloud execution policies respectively, but also scales well for a large number of UAVs. INDEX TERMS Computation offloading, edge-cloud computing, mobile edge computing, optimization, Internet of Things, unmanned aerial vehicles, linear programming. I. INTRODUCTION Nowadays, the Internet of Things (IoT) is fully embraced in virtually every aspect of our lives, owing to the advances made in Unmanned Aerial Vehicles (UAVs), sensors, and communication technologies. Such advancements have aided in the proliferation of complex IoT applications, which can generate and process a massive amount of data [1]. However, UAVs have limited resources onboard such as battery and computational power that restricts the execution of such types of applications on these devices [2], [3]. To alleviate these limitations and achieve the required communication and processing delay, intensive computations can be transmitted and remotely processed at more resourceful devices via the computation offloading concept [4], [5]. Consequently, two types of computation offloading models are proposed namely binary offloading [6] and partial of-floading [7]. In the case of binary offloading, the computation task is either executed locally at the UAV or offloaded and remotely executed at the edge server, whereas, in partial offloading, the computation task is divided into two parts, one for local computing and the other one for remote execution.

Research paper thumbnail of A Review of Energy-related Cost Issues and Prediction Models in Cloud Computing Environments

Computer Systems Science and Engineering, 2021

Research paper thumbnail of Performance and energy-based cost prediction modelling of virtual machines in cloud computing environments

Cloud Computing has transformed the way in which enterprises and individuals are utilising the In... more Cloud Computing has transformed the way in which enterprises and individuals are utilising the Information Technology (IT) by offering on-demand services such as applications, platforms and infrastructures for their customers with reasonable prices based on their usage (e.g., pay-as-you-go model). However, the wide adoption of Cloud Computing and the growing number of Cloud customers have increased the overall operational costs for Cloud providers, especially with the increasing cost of energy consumed to operate Cloud services. Consequently, Cloud providers consider energy consumption as one of the most important cost factors to be maintained within their infrastructures. In order to achieve energy efficiency and reduce the operational costs for Cloud services, reactive and proactive management mechanisms can be used to efficiently manage Cloud resources and reduce energy-related costs while maintaining service performance requirements. However, these mechanisms need to be supporte...

Research paper thumbnail of Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Intelligent Automation & Soft Computing, 2021

Research paper thumbnail of Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment

IEEE Access

The current agriculture systems compete to take advantage of industry advanced technologies, incl... more The current agriculture systems compete to take advantage of industry advanced technologies, including the internet of things (IoT), cloud/fog/edge computing, artificial intelligence, and agricultural robots to monitor, track, analyze and process various functions and services in real-time. Additionally, these technologies can make the agricultural processes smarter and more cost-efficient by using automated systems and eliminating any human interventions, hence enhancing agricultural production to meet future expectations. Although the current agriculture systems that adopt the traditional cloud-based architecture have provided powerful computing infrastructure to distributed IoT sensors. However, the cost of energy consumption associated with transferring heterogeneous data over the multiple network tiers to process, analyze and store the sensor's information in the cloud has created a huge load on information and communication infrastructure. Besides, the energy consumed by cloud data centers has an environmental impact associated with using non-clean fuels, which usually release carbon emissions (CO2) to produce electricity. Thus, to tackle these issues, we propose a new integrated edge-fog-cloud architectural paradigm that promises to enhance the energy-efficient of smart agriculture systems and corresponding carbon emissions. This architecture allows data collection from several sensors to process and analyze the agriculture data that require real-time operation (e.g., weather temperature, soil moisture, soil acidity, irrigation, etc.) in several layers (edge, fog, and cloud). Thus, the real-time processing could be held by the edge and fog layers to reduce the load on the cloud layer, which will help to enhance the overall energy consumption and process the agriculture applications/services efficiently. Mathematical modeling is conducted using mixed-integer linear programming (MILP) for a smart agriculture environment, where the proposed architecture is implemented, and results are analyzed and compared to the traditional implementation. According to the results of thousands of agriculture sensors, the proposed architecture outperforms the traditional cloud-based architecture in terms of reducing the overall energy consumption by 36% and the carbon emissions by 43%. In addition to these achievements, the results show that our proposed architecture can reduce network traffic by up to 86%, which can reduce network congestion. Finally, we develop a heuristic algorithm to validate and mimic the presented approach, and it shows comparable results to the MILP model. INDEX TERMS Smart agriculture, edge-fog-cloud computing, internet of things, energy-efficiency, carbon emission. Hatem A. Alharbi received the B.Sc. degree in Computer Engineering (Hons.

Research paper thumbnail of Modeling and Analyzing Offloading Strategies of IoT Applications over Edge Computing and Joint Clouds

Symmetry

Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For en... more Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the ...

Research paper thumbnail of Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

Computers, Materials & Continua

Research paper thumbnail of A novel approach for IoT tasks offloading in edge-cloud environments

Journal of Cloud Computing

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased ... more Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are cond...

Research paper thumbnail of A Hybrid Approach for Performance and Energy-based Cost Prediction in Clouds

A Hybrid Approach for Performance and Energy-based Cost Prediction in Clouds, 2021

With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of ... more With the wide adoption of Cloud Computing, Cloud providers consider energy consumption as one of the biggest cost factors to be maintained within their infrastructures. Consequently, various reactive and proactive management techniques are used to efficiently manage the cloud resources in order to reduce the energy consumption and the operational costs. However, these techniques need to be supported with performance and energy awareness not only at the Physical Machine (PM) level but also at Virtual Machine (VM) level in order to make enhanced cost decisions. Therefore, this paper introduces a new hybrid approach for performance and energy-based cost prediction that aims to integrate auto-scaling with live migration in order to estimate the total cost of heterogeneous VMs by considering their resource usage and power consumption, while maintaining the expected level of service performance. This approach works by detecting the underloaded and overloaded PMs in order to perform the mo...

Research paper thumbnail of Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Proceedings of the 8th International Conference on Cloud Computing and Services Science, 2018

Virtual Machines (VMs) live migration is one of the important approaches to improve resource util... more Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energybased Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.