Md Hasanul Ferdaus, PhD | East West University (original) (raw)

Papers by Md Hasanul Ferdaus, PhD

International Conference on Computation of Artificial Intelligence & Machine Learning, 2024

As digitization continues to proliferate globally, individuals prefer expressing themselves on so... more As digitization continues to proliferate globally, individuals prefer expressing themselves on social media platforms using their native languages. On these platforms, people share their interests, thoughts, and rights, sometimes receiving appreciation for their views and, at times, encountering conflicts of interest leading to mean comments, including hate speech and aggression towards individuals, societies, or groups. Detecting such comments has become crucial to curbing further abuse. This research paper focuses on identifying aggressive and non-aggressive Bengali text in social media posts and comments through the application of machine learning and deep learning algorithms. The dataset was collected from various social media platforms like Facebook, YouTube, and Twitter. Employing diverse machine learning and deep learning algorithms, such as SVM, Random Forest, KNN, Linear Regression, Decision Tree, and CNN, the authors achieved the highest accuracy of 90.46% with Multinomial Naive Bayes (MNB).

Data in Brief, 2024

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it i... more Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Data in Brief, 2024

In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive r... more In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive record of these valuable species is publicly available. Alarmingly, some of these plants are in a precarious state of endangerment. Therefore, we are creating a unique dataset of Bangladeshʼs rare, endangered, and threatened medicinal plants to support conservation efforts. It will help us to track and conserve endangered plant species, ensuring a more organized approach to research and preservation efforts. We conducted on-site visits to the National Botanical Garden and The Government Unani and Ayurvedic Medical College, capturing photographs of these plants in optimal sunlight conditions at various times of the day. This involved fieldwork, detailed image annotations, dataset organization, diversity augmentation, and contribution to the preservation of our natural heritage. We have collected a total of 16 types of rare and endangered medicinal plant leaf photos to create our unique dataset consisting of a total of 3494 images. This dataset will help researchers in biodiversity conservation through building efficient machine learning models and applying advanced machine learning techniques to identify rare and endangered medicinal plants.

International Journal of Computing and Digital Systems, 2024

The concept of borrowing or lending or renting goods or tools from others is commonly based on ce... more The concept of borrowing or lending or renting goods or tools from others is commonly based on centralized distribution, which means that the transaction of items takes place between a lender and a borrower. The popularity of handheld digital devices among mass people and the availability of secured tracking technologies, such as blockchain, bring the opportunity to introduce a new concept of the decentralized virtual lending or rental system. Since the blockchain eases the financial transactions secured and traceable for individuals, a lent item can be tracked by relating the possessor of that item to a financial transaction. In this paper, we proposed a decentralized virtual lending system based on blockchain for lending and borrowing physical items among individuals or companies. Our proposed system incorporates the monetary value of a physical item in blockchain and tracks the current possessor and ensure safety. Moreover, our proposed decentralized virtual lending system incorporated a recommendation mechanism for the users to borrow an item from the list of best alternatives without visiting a traditional rental company or the owner and allows an individual or a company to monetize by lending their goods and tools to others.

Data in Brief, 2024

The utilization of computer vision techniques has significantly enhanced the automation processes... more The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods. However, the adoption of computer vision approaches requires a comprehensive dataset of fabric samples, each with a known cot- ton percentage, to serve as training data for machine learn- ing models. This paper introduces a novel dataset comprising 1300 original images, covering a wide range of cotton percentages across thirteen distinct categories, from 30% to 99%. By employing image augmentation techniques, such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming, this dataset has been expanded to include a total of 27,300 images, thereby enhancing its utility for training and validating computer vision models aimed at accurately determining cotton content in fabrics. Through the extraction of pertinent features from the images of fabrics, this dataset holds the potential to significantly improve the accuracy and efficiency of computer vision-based cotton percentage detection.

Lecture Notes in Computer Science, 2017

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machin... more Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption.

Cluster Computing, Sep 21, 2020

Enterprise cloud data centers consume a tremendous amount of energy due to the large number of ph... more Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach.

arXiv (Cornell University), Jun 20, 2017

Underutilization of computing resources and high power consumption are two primary challenges in ... more Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial
optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration
overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Journal of Network and Computer Applications, Nov 1, 2017

Today's Cloud applications are dominated by composite applications comprising multiple computing ... more Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses networkaware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the stateof-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.

Cloud Computing has recently emerged as a highly successful alternative information technology pa... more Cloud Computing has recently emerged as a highly successful alternative information technology paradigm through on-demand resource provisioning and almost perfect reliability. In order to meet the customer demands, Cloud providers are deploying large-scale virtualized data centers consisting of thousands of servers across the world. These data centers require huge amount of electrical energy that incur very high operating cost and as a result, leave large carbon footprints. The reason behind the extremely high energy consumption is not just the amount of computing resources used, but also lies in inefficient use of these resources. Furthermore, with the recent proliferation of communication intensive applications, network resource demands are becoming one of the key areas of performance bottleneck. As a consequence, efficient utilization of data center resources and minimization of energy consumption are emerging as critical factors for the success of Cloud Computing. This thesis addresses the above mentioned resource and energy related issues by tackling through data center-level resource management, in particular, by efficient Virtual Machine (VM) placement and consolidation strategies. The problem of high resource wastage and energy consumption is dealt with an online consolidated VM cluster placement scheme, utilizing the Ant Colony Optimization (ACO) metaheuristic and a vector algebra-based multi-dimensional resource utilization model. In addition, optimization of network resource utilization is addressed by an online network-aware VM cluster placement strategy in order to localize data traffic among communicating VMs and reduce traffic load in data center interconnects that, in turn, reduces communication overhead in the upper layer network switches. Besides the online placement schemes that optimize the VM placement during the initial VM deployment phase, an offline decentralized dynamic VM consolidation framework and an associated algorithm leveraging VM live migration technique are presented to further optimize the run-time resource usage and energy consumption, along with migration overhead minimization. Such migration-aware dynamic VM consolidation strategy uses realistic VM migration parameters to estimate impacts of necessary VM migrations on data center and hosted applications. Simulation-based performance evaluation using representative workloads demonstrates that the proposed VM placement and consolidation strategies are capable of outperforming the state-of-the-art techniques, in the context of large data centers, by reducing energy consumption up to 29%, server resource wastage up to 85%, and network load up to 60%.

Emerging Research in Cloud Distributed Computing Systems, May 18, 2015

With the pragmatic realization of computing as a utility, Cloud Computing is has recently emerged... more With the pragmatic realization of computing as a utility, Cloud Computing is has recently emerged as a highly successful alternative IT paradigm through on-demand resource provisioning and almost perfect reliability. The rapidly growing customer demands for computing and storage resources are responded by the Cloud providers with the deployment of large scale data centers across the globe. Efficiency and scalability of these data centers, as well as the performance of the hosted applications highly depend on the allocations of the physical resource (e.g., CPU, memory, storage, and network bandwidth). Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the related background information and a taxonomy that characterizes and classifies the various components of network-aware VM placement and migration techniques. An elaborate survey and comparative analysis of the state of the art techniques is also put forward. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms recently proposed by the research community, the survey further identifies the limitations of the existing techniques and discusses on the future research directions.

Cloud Computing: Challenges, Limitations and R&D Solutions, 2014

With immense success and rapid growth within the last few years, cloud computing has been establi... more With immense success and rapid growth within the last few years, cloud computing has been established as the dominant paradigm of IT industry. In order to meet the increasing demand of computing and storage resources, infra-structure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infra-structure resource management systems and technologies, and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastage. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described.

Lecture Notes in Computer Science, 2014

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource uti... more In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

Scalability is one of the design goals that Application Layer Multicast (ALM) protocols address. ... more Scalability is one of the design goals that Application Layer Multicast (ALM) protocols address. An efficient way to achieve scalability is to arrange the members of an ALM group in a hierarchical cluster-oriented overlay network (e.g., NICE). So, the security mechanisms for such ALM protocols need to be scalable as well. Moreover, ALM protocols provide possibilities to adapt new ways of security mechanisms, for example self-certifying node identifiers that can be used for node authentication without resorting to a trusted third party. ALM protocols are often used for group communication in overlay networks that are formed spontaneously by members. These overlay networks exhibit high dynamics of nodes joining and leaving the multicast groups. Thus, the security solutions of such ALM protocols need to consider these dynamics.
Security services in group communication systems, for example video conferencing, have different properties than their counter parts in point-to-point communication systems, like client-server. Establishing data confidentiality in group environments requires more complex and costlier operations than two-party systems. Also, since data in ALM may be read by a non-member attacker using a network sniffer, data confidentiality mechanisms in ALM protocols are important and need to be efficient and highly scalable. Node authentication is another essential security requirement in ALM protocols to prevent a malicious node from impersonating other nodes. Furthermore, proof-of-membership in ALM environments is an important security service that prevents illegitimate nodes from joining multicast groups. Moreover, future ALM protocols may provide multiple communication groups for different application contexts within a single control environment, as envisioned for the Multicast/Multipeer-Overlay (MCP-O) service of the Spontaneous Virtual Networks (SpoVNet) research project. Security requirements for such groups have some additional properties and establishing security efficiently for them poses new challenges.

With the rapid usage of portable devices mobility in IP networks becomes more important issue in ... more With the rapid usage of portable devices mobility in IP networks becomes more important issue in the recent years. IETF standardized Mobile IP that works in Network Layer, which involves tunneling of IP packets from HA to Foreign Agent. Mobile IP suffers many problems of Triangular Routing, conflict with private addressing scheme, increase in load in HA, need of permanent home IP address, tunneling itself, and so on. In this paper, we proposed mobility management in Application Layer protocol SIP and show some comparative analysis between Mobile IP and SIP in context of mobility.

First International Conference on Next-Generation Wireless Systems (ICNEWS2006), 2006

Utility bill like electric, mobile bill etc. payment is becoming a routine work to us. This is th... more Utility bill like electric, mobile bill etc. payment is becoming a routine work to us. This is the age of telecommunication. So, utility bill payment using telecom support will be a wonder work. This paper will show the various ways that we can use to pay our utility bills using telecom support. At the same time it will provide the reason behind this automation.

Bachelor Thesis, 2004

Utility services mean the different types of services we use in our day-to-day life from differen... more Utility services mean the different types of services we use in our day-to-day life from different service providers for various utilities such as electricity, water, gas etc. The service providers bill the customers for consumption of these utilities. The consumers pay these bills to some particular banks and the banks notify the service providers about the payment. Automation of utility bill collection system means the computerization of these bill collections and notification process so that the full process can be carried out automatically. This thesis gives a detailed study of currently existing system of bill collection and notification process and suggested a fully automated process for the same. Here we have suggested introduction of bar code in the traditional bill paper from which billing information can be read by bar code scanners. A comparison of different bar code printing techniques is also studied. Two different techniques for the communication of the billing information from the bank to service provider are also suggested. We also suggested necessary changes in the bank-end software and the service provider-end software. We also designed and implemented two programs, one for the bank and one for the service provider. Due to complexity, time limitation and other shortcomings, a completely reliable system could not be implemented at this stage, but the approach was constructive for future research. A guideline for future development of full automation of utility bill collection process has also been presented.

International Conference on Computation of Artificial Intelligence & Machine Learning, 2024

As digitization continues to proliferate globally, individuals prefer expressing themselves on so... more As digitization continues to proliferate globally, individuals prefer expressing themselves on social media platforms using their native languages. On these platforms, people share their interests, thoughts, and rights, sometimes receiving appreciation for their views and, at times, encountering conflicts of interest leading to mean comments, including hate speech and aggression towards individuals, societies, or groups. Detecting such comments has become crucial to curbing further abuse. This research paper focuses on identifying aggressive and non-aggressive Bengali text in social media posts and comments through the application of machine learning and deep learning algorithms. The dataset was collected from various social media platforms like Facebook, YouTube, and Twitter. Employing diverse machine learning and deep learning algorithms, such as SVM, Random Forest, KNN, Linear Regression, Decision Tree, and CNN, the authors achieved the highest accuracy of 90.46% with Multinomial Naive Bayes (MNB).

Data in Brief, 2024

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it i... more Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Data in Brief, 2024

In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive r... more In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive record of these valuable species is publicly available. Alarmingly, some of these plants are in a precarious state of endangerment. Therefore, we are creating a unique dataset of Bangladeshʼs rare, endangered, and threatened medicinal plants to support conservation efforts. It will help us to track and conserve endangered plant species, ensuring a more organized approach to research and preservation efforts. We conducted on-site visits to the National Botanical Garden and The Government Unani and Ayurvedic Medical College, capturing photographs of these plants in optimal sunlight conditions at various times of the day. This involved fieldwork, detailed image annotations, dataset organization, diversity augmentation, and contribution to the preservation of our natural heritage. We have collected a total of 16 types of rare and endangered medicinal plant leaf photos to create our unique dataset consisting of a total of 3494 images. This dataset will help researchers in biodiversity conservation through building efficient machine learning models and applying advanced machine learning techniques to identify rare and endangered medicinal plants.

International Journal of Computing and Digital Systems, 2024

The concept of borrowing or lending or renting goods or tools from others is commonly based on ce... more The concept of borrowing or lending or renting goods or tools from others is commonly based on centralized distribution, which means that the transaction of items takes place between a lender and a borrower. The popularity of handheld digital devices among mass people and the availability of secured tracking technologies, such as blockchain, bring the opportunity to introduce a new concept of the decentralized virtual lending or rental system. Since the blockchain eases the financial transactions secured and traceable for individuals, a lent item can be tracked by relating the possessor of that item to a financial transaction. In this paper, we proposed a decentralized virtual lending system based on blockchain for lending and borrowing physical items among individuals or companies. Our proposed system incorporates the monetary value of a physical item in blockchain and tracks the current possessor and ensure safety. Moreover, our proposed decentralized virtual lending system incorporated a recommendation mechanism for the users to borrow an item from the list of best alternatives without visiting a traditional rental company or the owner and allows an individual or a company to monetize by lending their goods and tools to others.

Data in Brief, 2024

The utilization of computer vision techniques has significantly enhanced the automation processes... more The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods. However, the adoption of computer vision approaches requires a comprehensive dataset of fabric samples, each with a known cot- ton percentage, to serve as training data for machine learn- ing models. This paper introduces a novel dataset comprising 1300 original images, covering a wide range of cotton percentages across thirteen distinct categories, from 30% to 99%. By employing image augmentation techniques, such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming, this dataset has been expanded to include a total of 27,300 images, thereby enhancing its utility for training and validating computer vision models aimed at accurately determining cotton content in fabrics. Through the extraction of pertinent features from the images of fabrics, this dataset holds the potential to significantly improve the accuracy and efficiency of computer vision-based cotton percentage detection.

Lecture Notes in Computer Science, 2017

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machin... more Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption.

Cluster Computing, Sep 21, 2020

Enterprise cloud data centers consume a tremendous amount of energy due to the large number of ph... more Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach.

arXiv (Cornell University), Jun 20, 2017

Underutilization of computing resources and high power consumption are two primary challenges in ... more Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial
optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration
overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Journal of Network and Computer Applications, Nov 1, 2017

Today's Cloud applications are dominated by composite applications comprising multiple computing ... more Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses networkaware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the stateof-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.

Cloud Computing has recently emerged as a highly successful alternative information technology pa... more Cloud Computing has recently emerged as a highly successful alternative information technology paradigm through on-demand resource provisioning and almost perfect reliability. In order to meet the customer demands, Cloud providers are deploying large-scale virtualized data centers consisting of thousands of servers across the world. These data centers require huge amount of electrical energy that incur very high operating cost and as a result, leave large carbon footprints. The reason behind the extremely high energy consumption is not just the amount of computing resources used, but also lies in inefficient use of these resources. Furthermore, with the recent proliferation of communication intensive applications, network resource demands are becoming one of the key areas of performance bottleneck. As a consequence, efficient utilization of data center resources and minimization of energy consumption are emerging as critical factors for the success of Cloud Computing. This thesis addresses the above mentioned resource and energy related issues by tackling through data center-level resource management, in particular, by efficient Virtual Machine (VM) placement and consolidation strategies. The problem of high resource wastage and energy consumption is dealt with an online consolidated VM cluster placement scheme, utilizing the Ant Colony Optimization (ACO) metaheuristic and a vector algebra-based multi-dimensional resource utilization model. In addition, optimization of network resource utilization is addressed by an online network-aware VM cluster placement strategy in order to localize data traffic among communicating VMs and reduce traffic load in data center interconnects that, in turn, reduces communication overhead in the upper layer network switches. Besides the online placement schemes that optimize the VM placement during the initial VM deployment phase, an offline decentralized dynamic VM consolidation framework and an associated algorithm leveraging VM live migration technique are presented to further optimize the run-time resource usage and energy consumption, along with migration overhead minimization. Such migration-aware dynamic VM consolidation strategy uses realistic VM migration parameters to estimate impacts of necessary VM migrations on data center and hosted applications. Simulation-based performance evaluation using representative workloads demonstrates that the proposed VM placement and consolidation strategies are capable of outperforming the state-of-the-art techniques, in the context of large data centers, by reducing energy consumption up to 29%, server resource wastage up to 85%, and network load up to 60%.

Emerging Research in Cloud Distributed Computing Systems, May 18, 2015

With the pragmatic realization of computing as a utility, Cloud Computing is has recently emerged... more With the pragmatic realization of computing as a utility, Cloud Computing is has recently emerged as a highly successful alternative IT paradigm through on-demand resource provisioning and almost perfect reliability. The rapidly growing customer demands for computing and storage resources are responded by the Cloud providers with the deployment of large scale data centers across the globe. Efficiency and scalability of these data centers, as well as the performance of the hosted applications highly depend on the allocations of the physical resource (e.g., CPU, memory, storage, and network bandwidth). Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the related background information and a taxonomy that characterizes and classifies the various components of network-aware VM placement and migration techniques. An elaborate survey and comparative analysis of the state of the art techniques is also put forward. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms recently proposed by the research community, the survey further identifies the limitations of the existing techniques and discusses on the future research directions.

Cloud Computing: Challenges, Limitations and R&D Solutions, 2014

With immense success and rapid growth within the last few years, cloud computing has been establi... more With immense success and rapid growth within the last few years, cloud computing has been established as the dominant paradigm of IT industry. In order to meet the increasing demand of computing and storage resources, infra-structure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infra-structure resource management systems and technologies, and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastage. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described.

Lecture Notes in Computer Science, 2014

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource uti... more In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

Scalability is one of the design goals that Application Layer Multicast (ALM) protocols address. ... more Scalability is one of the design goals that Application Layer Multicast (ALM) protocols address. An efficient way to achieve scalability is to arrange the members of an ALM group in a hierarchical cluster-oriented overlay network (e.g., NICE). So, the security mechanisms for such ALM protocols need to be scalable as well. Moreover, ALM protocols provide possibilities to adapt new ways of security mechanisms, for example self-certifying node identifiers that can be used for node authentication without resorting to a trusted third party. ALM protocols are often used for group communication in overlay networks that are formed spontaneously by members. These overlay networks exhibit high dynamics of nodes joining and leaving the multicast groups. Thus, the security solutions of such ALM protocols need to consider these dynamics.
Security services in group communication systems, for example video conferencing, have different properties than their counter parts in point-to-point communication systems, like client-server. Establishing data confidentiality in group environments requires more complex and costlier operations than two-party systems. Also, since data in ALM may be read by a non-member attacker using a network sniffer, data confidentiality mechanisms in ALM protocols are important and need to be efficient and highly scalable. Node authentication is another essential security requirement in ALM protocols to prevent a malicious node from impersonating other nodes. Furthermore, proof-of-membership in ALM environments is an important security service that prevents illegitimate nodes from joining multicast groups. Moreover, future ALM protocols may provide multiple communication groups for different application contexts within a single control environment, as envisioned for the Multicast/Multipeer-Overlay (MCP-O) service of the Spontaneous Virtual Networks (SpoVNet) research project. Security requirements for such groups have some additional properties and establishing security efficiently for them poses new challenges.

With the rapid usage of portable devices mobility in IP networks becomes more important issue in ... more With the rapid usage of portable devices mobility in IP networks becomes more important issue in the recent years. IETF standardized Mobile IP that works in Network Layer, which involves tunneling of IP packets from HA to Foreign Agent. Mobile IP suffers many problems of Triangular Routing, conflict with private addressing scheme, increase in load in HA, need of permanent home IP address, tunneling itself, and so on. In this paper, we proposed mobility management in Application Layer protocol SIP and show some comparative analysis between Mobile IP and SIP in context of mobility.

First International Conference on Next-Generation Wireless Systems (ICNEWS2006), 2006

Utility bill like electric, mobile bill etc. payment is becoming a routine work to us. This is th... more Utility bill like electric, mobile bill etc. payment is becoming a routine work to us. This is the age of telecommunication. So, utility bill payment using telecom support will be a wonder work. This paper will show the various ways that we can use to pay our utility bills using telecom support. At the same time it will provide the reason behind this automation.

Bachelor Thesis, 2004

Utility services mean the different types of services we use in our day-to-day life from differen... more Utility services mean the different types of services we use in our day-to-day life from different service providers for various utilities such as electricity, water, gas etc. The service providers bill the customers for consumption of these utilities. The consumers pay these bills to some particular banks and the banks notify the service providers about the payment. Automation of utility bill collection system means the computerization of these bill collections and notification process so that the full process can be carried out automatically. This thesis gives a detailed study of currently existing system of bill collection and notification process and suggested a fully automated process for the same. Here we have suggested introduction of bar code in the traditional bill paper from which billing information can be read by bar code scanners. A comparison of different bar code printing techniques is also studied. Two different techniques for the communication of the billing information from the bank to service provider are also suggested. We also suggested necessary changes in the bank-end software and the service provider-end software. We also designed and implemented two programs, one for the bank and one for the service provider. Due to complexity, time limitation and other shortcomings, a completely reliable system could not be implemented at this stage, but the approach was constructive for future research. A guideline for future development of full automation of utility bill collection process has also been presented.