Amjad Mahmood

Bio: Amjad Mahmood is an academic researcher from University of Bahrain. The author has contributed to research in topics: Scheduling (computing) & Web server. The author has an hindex of 10, co-authored 30 publications receiving 264 citations. Previous affiliations of Amjad Mahmood include College of Information Technology & Information Technology University.

A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design

Abstract: The topological design of a computer communication network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple design objectives and numerous design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological design of distributed local area networks (DLANs). Five design objectives are considered herein, namely, network reliability, network availability, average link utilization, monetary cost, and network delay. Goal programming (GP) is incorporated to aggregate the multiple design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Abstract: In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

Abstract: Minimizing power consumption to prolong battery life has become an important design issue for portable battery-operated devices such as smartphones and personal digital assistants (PDAs) On a Dynamic Voltage Scaling (DVS) enabled processor, power consumption can be reduced by scaling down the operating frequency of the processor whenever the full processing speed is not required Real-time task scheduling is a complex and challenging problem for DVS-enabled multiprocessor systems This paper first formulates the real-time task scheduling for DVS-enabled multiprocessor systems as a combinatorial optimization problem It then proposes a genetic algorithm that is hybridized with the stochastic evolution algorithm to allocate and schedule real-time tasks with precedence constraints It presents specialized crossover and perturb operations as well as a topology preserving algorithm to generate the initial population A comprehensive simulation study has been done using synthetic and real benchmark data to evaluate the performance of the proposed Hybrid Genetic Algorithm (HGA) in terms of solution quality and efficiency The performance of the proposed HGA has been compared with the genetic algorithm, particle swarm optimization, cuckoo search, and ant colony optimization The simulation results show that HGA outperforms the other algorithms in terms of solution quality

Examination of factors influencing students and faculty behavior towards m-learning acceptance: An empirical study

Abstract: Purpose Mobile learning is a unique form of learning which uses the distinct features of mobile devices. The purpose of this paper is to investigate the present state of student and faculty perception towards m-learning at open and distance educational institutes in Pakistan. Design/methodology/approach The paper presents a conceptual model based on TAM, which explains factors influencing student and faculty perception towards m-learning acceptance. M-learning acceptance mainly depends on personal attitude, so this study focusses on individual context. Primary data from students and faculty including tutors (n=612, students =448, faculty/tutors=162) was collected through a properly designed questionnaire by using purposive convenient sampling technique during Autumn 2015 semester. Structural equation modelling was used to analyse the collected data. Findings The results indicate that student and faculty skill readiness and self-efficacy influence perceived ease of use and perceived usefulness, where these two factors along with prior experience positively influence behavioural intension (BI) to accept mobile learning. Furthermore study results specifically provide factors which positively influence BI either directly or indirectly. Research limitations/implications The study was limited to AIOU. Originality/value The study specifically provides factors which influence BI either directly or indirectly.

Replicating web contents using a hybrid particle swarm optimization

Abstract: We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0-1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search.

技術解説 IEEE Internet Computing

Abstract: A complex infrastructure is usually a reality in a virtual enterprise. For these enterprises to operate well they would need notions of workflows, global and local business processes, Service Level Agreements, and business transactions. Web Service Standards such as SOAP, WSDL, and BPEL4WS make virtual enterprise increasingly practical, speeding up the flow of business and reducing costs. These web services have to be interfaced with the internal business processes. The interchange of service results in the interaction of existing business processes and results in new distributed processes.

Technology Acceptance Model in M-learning context: A systematic review

Abstract: Various review studies were conducted to provide valuable insights into the current research trend of the Technology Acceptance Model (TAM). Nevertheless, this issue still needs to be investigated from further directions. It has been noticed that research overlooks the investigation of TAM with regard to Mobile learning (M-learning) studies from the standpoint of different perspectives. The present study systematically reviews and synthesizes the TAM studies related to M-learning aiming to provide a comprehensive analysis of 87 research articles from 2006 to 2018. The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models. In addition, the main research problem that was frequently tackled among all the analyzed studies was to examine the acceptance of M-learning among students. Moreover, questionnaire surveys were the primarily relied research methods for data collection. Additionally, most of the analyzed studies were undertaken in Taiwan, this is followed by Spain, China, and Malaysia, respectively among the other countries. Besides, most of the analyzed studies were frequently conducted in humanities and educational context, followed by IT and computer science context, respectively among the other contexts. Most of the analyzed studies were carried out in the higher educational settings. To that end, the findings of this review study provide an insight into the current trend of TAM research involving M-learning studies and form an essential reference for scholars in the M-learning context.

Computing System Reliability: Models and Analysis

Abstract: Computing systems are of growing importance because of their wide use in many areas including those in safety-critical systems. This book describes the basic models and approaches to the reliability analysis of such systems. An extensive review is provided and models are categorized into different types. Some Markov models are extended to the analysis of some specific computing systems such as combined software and hardware, imperfect debugging processes, failure correlation, multi-state systems, heterogeneous subsystems, etc. One of the aims of the presentation is that based on the sound analysis and simplicity of the approaches, the use of Markov models can be better implemented in the computing system reliability.

A Modified Artificial Bee Colony Algorithm

Abstract: To overcome the shortcoming of poor diversity of Artificial Bee Colony(ABC)algorithm,this paper presents a modified Artificial Bee Colony algorithm.Unlike ABC in which onlooker bees choose employed bees from the colony according to proportional fitness choosing strategy,in MABC,in order to decrease the selection pressure and further improve the diversity,it does not set onlooker bees,as a result of no selection pressure on employed bees(solutions).The simulation results show that the presented algorithm can ensure the colony diversity and improve the performances of ABC.