Ahmadreza Montazerolghaem | University of Isfahan (original) (raw)

Papers by Ahmadreza Montazerolghaem

Journal of Engineering Research, 2024

This article presents a comprehensive exploration of the architecture and various approaches in t... more This article presents a comprehensive exploration of the architecture and various approaches in the domain of cloud computing and software-defined networks. The salient points addressed in this article encompass: Foundational Concepts: An overview of the foundational concepts and technologies of cloud computing, including software-defined cloud computing. Algorithm Evaluation: An introduction and evaluation of various algorithms aimed at enhancing network performance. These algorithms include Intelligent Rule-Based Metaheuristic Task Scheduling (IRMTS), reinforcement learning algorithms, task scheduling algorithms, and Priority-aware Semi-Greedy (PSG). Each of these algorithms contributes uniquely to optimizing Quality of Service (QoS) and data center efficiency. Resource Optimization: An introduction and examination of cloud network resource optimization based on presented results and practical experiments, including a comparison of the performance of different algorithms and approaches. Future Challenges: An investigation and presentation of challenges and future scenarios in the realm of cloud computing and software-defined networks. In conclusion, by introducing and analyzing simulators like Mininet and CloudSim, the article guides the reader in choosing the most suitable simulation tool for their project. Through its comprehensive analysis of the architecture, methodologies, and prevalent algorithms in cloud computing and software-defined networking, this article aids the reader in achieving a deeper understanding of the domain. Additionally, by presenting the findings and results of conducted research, it facilitates the discovery of the most effective and practical solutions for optimizing cloud network resources.

The Internet of Things (IoT) is a collection of different devices that contain different software... more The Internet of Things (IoT) is a collection of different devices that contain different software and hardware technologies to communicate with other devices using unique addressing methods [1,2]. The IoT devices collect data from their surroundings through various sensors and exchange them [2]. As a result, IoT system applied in various fields such as smart homes, smart cities, transportation, e-health care, agriculture, and industries. Cloud Computing (CC) [3] is an emerging computing technology that, due to its capabilities, can provide all the resources needed for the quality of IoT services for IoT. The CC system consists of a large number of Data Centers (DCs), each DC also consists of a large number of Virtual Machines (VMs). But due to the long geographical distance with IoT devices on the network Edge Computing (EC), the CC system is not suitable for delay-sensitive IoT devices such as emergency monitoring, and energy usage measurements from a smart grid, cause long delays that may not be acceptable for some applications in today's world [3,4]. Therefore, to solve this problem, the computing resources should be closer to the network EC devices, and the CC system is very suitable for this and can provide the resources needed to reduce the workload in cloud DC, facilitate task processing, facilitate networking, and facilitate the storage of data generated by IoT sensors, with the lowest amount of communication cost and delay [4-7]. Each server or Fog Computing (FC) node is a virtualized system equipped with a wireless communication unit, simpler processing and computing devices for data, and data storage cards. When FC nodes receive more task requests from IoT devices that exceed their capacity, they can offload some of their load to cloud layer DCs [8-10]. In other words, CC and FC are models of hosting services over the Internet for IoT devices. Fig. 8.1 shows the architecture of IoT-Fog-Cloud system, with CC in the top layer, FC in the middle layer, and IoT devices in the bottom layer. Task Scheduling (TSch) is an effective method for efficient management of virtual resources of the FC and EC environment [11] based on specific constraints and deadlines by different users, which can be used to assign the set of requested tasks by users or existing IoT devices to FC and CC resources in order to execute them [12-16]. According to Fig. 8.1, in the proposed TSch model that is considered for scheduling the task requests of IoT devices in the FC system, Fog Broker (FB) is the main part and is located in the FC layer, which includes three main parts: Task Administrator (TA), Resource Monitoring Service (RMS), and Task Scheduler (TSR). The TA receives all task requests from various IoT devices, and then forwards them to the TSR, maintaining their required resources and attributes. Also, RMS is responsible for collecting information on FC resources and monitoring the status of FC resources. TSR unit is the main core of FB unit, and TSch algorithms are executed in it. According to the characteristics of the sent task requests as well as the capabilities of the available FC resources, the TSR schedules the tasks for execution and processing by assigning the appropriate FC nodes to the task requests. Finally, the processed task requests are sent back to the FB and from there to the respective users or IoT devices [12-16]. In order to allocate FC resources based on the demand of users or IoT devices, fully flexible infrastructure virtualization that uses IoT task Handbook of Whale Optimization Algorithm.

Segmentation is considered as an essential step in image processing. This process divides differe... more Segmentation is considered as an essential step in image processing. This process divides different parts of the image into several categories. Multi-level Thresholding is a method that facilitates this process. The problem is to correctly segment each image to find the best set of thresholds [1]. Thresholding usually uses image processing methods due to its consistency and low Computational Complexity (CC). Two main methods are Otsu's method [2-4] and Kapur's method [5,6]. However, such approaches have high CC for Multi-level Thresholding [7]. Thresholds help each other to separate interesting objects from their background. The higher splitting quality depends on the selected thresholds [8]. Recently, Meta-Heuristic (MH) algorithms like Particle Swarm Optimization (PSO) [9], Whale Optimization Algorithm (WOA) [10], Moth-Flame Optimization (MFO) [11] have been successfully applied for Thresholding problems [3,8,12], and ABC [13,14] and, Harris Hawks Optimizer (HHO) [15] are used in other problems. MH algorithms have attracted the attention of researchers due to their excellent performance in finding threshold vectors in Multi-level Thresholding Image Segmentation (MTIS) systems. MH algorithms are either used separately in these problems, or been used in a combined version to solve the MTIS. Most MH algorithms are population-based and initially find a plausible answer by randomly moving through the search space. Such algorithms also include two phases of exploration and exploitation to search for the desired solution on the search space, through which the two phases search globally and locally, respectively. Therefore, several attempts have been made in the literature to achieve a better balance between exploration and exploitation phases to ensure maximum performance on a given optimization problem. In this chapter, our contribution is the design and implementation of an MTIS system using a combination of WOA, MFO, and the Inverse Otsu (IO) Function. This modification is developed using the operators of the MFO algorithm in an attempt to enhance the exploitation phase of WOA during the process of finding the optimal solution for a given optimization problem. It is used to increase the system's performance so that the combined MFWOA algorithm performs better than WOA and MFO and provides better solutions. Therefore, the optimal exploration and exploitation properties of MFO and WOA are used in the search space to find the best thresholds. The rest of our chapter is organized as follows: Section 45.2 presents an overview of related work. In Section 45.3, we describe the prerequisites used in the proposed method. Section 45.4 offers the proposed method. Section 45.5 describes the performance analysis and test results. Finally, Section 45.6 presents the conclusions. 45.2 Related work The works that have been done so far in the field of MTIS using MH algorithms are single MH and Hybrid MH, which are briefly described in the following. Handbook of Whale Optimization Algorithm.

IEEE Transactions on Intelligent Transportation Systems, Nov 30, 2023

Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, Io... more Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, IoV networks' servers and switches are facing resource crises. Multimedia vehicles connected to the Internet of Things are increasing; there are millions of vehicles and heavy multimedia traffic in the IoV network. The network's scarcity of resources results in overload, which, in turn, leads to a degradation of both Quality of Service (QoS) and Quality of Experience (QoE). Conversely, when resources are abundant, it leads to unnecessary energy wastage. Managing IoV network resources optimally while considering constraints such as Energy, Load, QoS, and QoE is a complex challenge. To address this, the study proposes a solution by decomposing the problem and designing a modular architecture named ELQ 2. This architecture enables simultaneous control of the mentioned constraints, effectively reducing overall complexity. To achieve this objective, Network Softwarization and Virtualization concepts are employed. This modern architecture allows dynamically adjusting of the scale of the resources on demand, effectively reducing energy usage. Additionally, this architecture provides some other potentials, such as "the distribution of multimedia traffic among servers", "determining the route with high QoS for traffic", and "selecting a media with high QoE". A real test field is provided by Floodlight Controller, Open vSwitch, and Kamailio Server tools to evaluate the performance of ELQ 2. The findings suggest that the utilization of ELQ 2 holds promise in reducing the count of active servers and switches via effective resource management. Additionally, it demonstrates enhancements in various QoS and QoE parameters, encompassing throughput, multimedia delay, R Factor, and MOS, accomplished through load balancing strategies. As an illustration, the deployment of flows has achieved a commendable success rate of 95% owing to the utilization of SDN-based and comprehensive management practices encompassing all network resources.

arXiv (Cornell University), Jul 12, 2013

Electric Power Components and Systems, 2011

ABSTRACT

IEEE Transactions on Network and Service Management, 2016

Concurrency and Computation: Practice and Experience, Jun 20, 2023

arXiv (Cornell University), Jul 25, 2018

International journal of ambient systems and applications, Jun 30, 2013

Internet of Things

In the future, it is anticipated that software-defined networking (SDN) will become the preferred... more In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research.

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)

Journal of Ambient Intelligence and Humanized Computing

Soft Computing, Dec 10, 2018

The SIP protocol was standardized by the IETF at the application layer for initiating, managing, ... more The SIP protocol was standardized by the IETF at the application layer for initiating, managing, and terminating multimedia sessions and has been widely used as the main signaling protocol on both the Internet and VoIP networks. Most challenges in this protocol are overload and lack of proper state distribution. These challenges cause a wide range of next-generation network users to face a sharp decline in service quality. In this article, we define the state distribution problem between several nodes where the state maintenance is accompanied by considerable consumption of resources leading to overload. For the problem solution, the goal is to increase the overall throughput of calls and the availability of servers. First, we provide a framework based on software-defined networking technology, and then we formulate the problem as an optimization problem and implement and evaluate it as a module on the proposed controller. This leads to a more scalable SIP network that dynamically determines the number of SIP requests for which the server is modeled while delegating the state maintenance to its downstream server for the rest of the requests. This is in contrast to existing SIP servers because they are statically configured to be either stateless or stateful, resulting in non-optimal call throughput. Performance evaluation is performed at two levels of infrastructure and control and the results are presented.

IEEE Internet of Things Journal, 2022

2011 IEEE Consumer Communications and Networking Conference (CCNC), 2011

Springer Nature, 2022

Today, the multimedia over IP (MoIP) network has become a cost-effective and efficient alternativ... more Today, the multimedia over IP (MoIP) network has become a cost-effective and efficient alternative to the public switched telephone network (PSTN). Free applications for multimedia transmission over the Internet have become increasingly popular, gaining considerable popularity around the world. This communication consists of two phases, i.e., signaling phase and media exchange phase. The SIP protocol is responsible for the MoIP network signaling to provide services such as VoIP, voice and video conferencing, video over demand (VoD), and instant messaging. This application layer protocol has been standardized by the IETF for initiating, managing, and tearing down multimedia sessions and has been widely used as the main signaling protocol on the Internet. The signaling and media are handled by SIP proxies and network switches, respectively. One of the most critical challenges in MoIP is the overloading of SIP proxies and network switches. Because of these challenges, a wide range of network users experiences a sharp drop in service quality. Overload occurs when there are not enough processing resources and memory to process all the messages due to the lack of proper routing. This study aims to model the routing problem in MoIP by providing a framework based on software-defined networking (SDN) technology and a convex mathematical programming model to prevent overload. The proposed framework is simulated and implemented using various scenarios and network topologies. The results show that throughput, latency, message retransmission rate, and resource consumption have improved using the proposed approach.

Elsevier, 2023