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Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.


Papers
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Journal ArticleDOI
TL;DR: This article designs two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links.
Abstract: To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This article focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.

3 citations

Proceedings ArticleDOI
04 Dec 2022
TL;DR: In this article , a secure data dissemination scheme using AI and blockchain is proposed, where the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices.
Abstract: In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.

3 citations

Journal ArticleDOI
TL;DR: The proposed techniques overcome user misbehavior by masking the impact of the users' pursued private objectives on the overall system performance and improve fairness among users by allocating service to users adaptively by accounting for how much service each user has received in the past.
Abstract: We propose a credit-based resource allocation technique for dynamic spectrum access that is robust against malicious and selfish behaviors and ensures good overall system fairness performance while also allowing spectrum users to achieve high amounts of service. We also propose a new objective function that, when combined with the proposed credit-based technique, leads to further improvements of the system fairness performance. Our proposed techniques overcome user misbehavior by masking the impact of the users' pursued private objectives on the overall system performance. They also improve fairness among users by allocating service to users adaptively by accounting for how much service each user has received in the past. Our simulation results show that our proposed techniques maintain high system performance by allowing users to achieve high amounts of service and by ensuring fair allocation of spectrum resources among users even in the presence of misbehaved users. Using simulations, we also show that these high performances are also achievable under various different network scenarios.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2002

9,314 citations