تخصیص

Open Access Article

1 - Optimal LO Selection in E-Learning Environment Using PSO Algorithm
gholamali montazer

One of the key issues in e-learning is to identify needs, educational behavior and learning speed of the learners and design a suitable curriculum commensurate to their abilities. This goal is achieved by identifying the learners’ different dimension of personality and More

One of the key issues in e-learning is to identify needs, educational behavior and learning speed of the learners and design a suitable curriculum commensurate to their abilities. This goal is achieved by identifying the learners’ different dimension of personality and ability and assigning suitable learning material to them according these features. In this paper, an intelligent tutoring system is proposed which optimizes the LO selection in e-learning environment. In order to evaluate the proposed method, the designed system has been used in a web-based instruction system in different conditions and the results of the "Academically success", "Satisfactory learning achievement" and "Time of the learners’ attendance" have been analyzed. The obtained results show a significant efficiency compared to other applied methods. Manuscript profile

Open Access Article

2 - The impact of technological innovation capabilities on innovation performance in Knowledge-Based Firms (Case Study: Science and Technology Park of Tehran University)
Mahdi Dehghani Soltani

The aim of this study is to evaluate The impact of technological innovation capabilities on innovation performance in Knowledge-Based Firms. The population in this study was manager and employee of Knowledge-Based Firms in Science and Technology Park of Tehran Universi More

The aim of this study is to evaluate The impact of technological innovation capabilities on innovation performance in Knowledge-Based Firms. The population in this study was manager and employee of Knowledge-Based Firms in Science and Technology Park of Tehran University and the sample size was 126 patients that to obtain this sample size random sampling method is used. The data collection tool in this study was a standard questionnaire and for reliability Cronbach's alpha coefficient was used. Present study is an applied research in terms of purpose and in terms of data collecting way it is considered as a descriptive survey. Structural equations modeling was used for examining of considered model. Finding in this paper show strategy planning, R&D, resource allocation, Marketing and learning capabilities can significantly improve the innovation. R&D and resource allocation capabilities can also significantly improve new product introduction. While Manufacturing and Organizing capability is not affect significantly on innovation performance in Knowledge-Based Firms in Science and Technology Park of Tehran University. Manuscript profile

Open Access Article

3 - Using a multi-objective optimization algorithm for tasks allocate in the cloud-based systems to reduce energy consumption
sara tabaghchimilan nima jafari novimipour

Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amount More

Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively. Manuscript profile

Open Access Article

4 - An Investigation into the impact of Inter-university resource allocation mechanisms on educational and research performance of departments in Shahid Chamran University of Ahvaz
Farzane Ghasemi Masoud Khodapanah Mohammad Reza Akhound

The main purpose of this research was to investigate the effect of internal financial resource allocation mechanisms on the educational and research performance of departments. Present study could be classified as a theoretical-applied research in terms of objectiv More

The main purpose of this research was to investigate the effect of internal financial resource allocation mechanisms on the educational and research performance of departments. Present study could be classified as a theoretical-applied research in terms of objectives and descriptive-analytic as well as correlational in terms of methodology. Shahid Chamran University was selected as the statistical population and all the required data concerning faculties and departments was collected. In order to collect the required data in the time domain of 2010-2013, documents and records held by different units of the university were scrutinized and reviewed. For data analysis, descriptive statistics tables including average growth rates, means, standard deviations and correlation coefficients were developed while to determine the relationships between variables and fit regression models of interest, inferential statistics and multilevel modelling methods were used. The results show that: (1) values do not have the same structure and homogeneity and there is a dissonance between time units and departments. The calculated statistics for research performance (ρ=0.68) and academic performance (ρ=0.52) indicates the necessity of using multilevel modeling. 2) Managerial and educational performance of the departments per faculty member during the study period has encountered some fluctuations and the general trend of these indices has been increasing 3) each department has experienced an individual trend. 4) The mechanisms of financial resource allocation significantly affect educational and research performances of the departments. Manuscript profile

Open Access Article

5 - Power Control and Subchannel Allocation in OFDMA Macrocell-Femtocells Networks
H. Davoudi M. Rasti

Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. More

Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. Since macrocell users have priority to femtocell ones, presence of femtocell users should not prevent macrocell users to access minimum quality-of-service. In this paper, a power control and subchannel allocation scheme in downlink transmission an orthogonal frequency division multiple access (OFDMA) based two tier of macrocell and femtocell is proposed, aiming the maximization of femtocell users total data rate, in which the minimum QOS for all macrocell users and femtocell delay-sensitive users is observed. In macrocell tier, two different problems are considered. The first problem aim to maximizing the total threshold of tolerable cross-tier interference for macrocell users and the second problem’s goal is minimizing the macrocell’s total transmission power. For the femtocell tier, maximizing the users total data rate is the objective. Hungrian method, an assignment optimization method, is used for solving the first problem in macrocell tier. Moreover, in order to solve the second problem a heuristic method for subchannel allocation is proposed and dual Lagrange method is used for power control. In addition, in order to solve the problem for femtocell tier, a heuristic method is used for subchannel allocation. Subsequently, a dual Lagrange method which is one of the convex optimization problem solver is used, so that we can control the power. Finally, an extend simulations are performed to validate the performance of the proposed method. Manuscript profile

Open Access Article

6 - An Efficient Approach for Resource Allocation in Fog Computing Considering Request Congestion Conditions
Samira Ansari Moghaddam سميرا نوفرستي مهري رجايي

Cloud data centers often fail to cope with the millions of delay-sensitive storage and computational requests due to their long distance from end users. A delay-sensitive request requires a response before its predefined deadline expires, even when the network has a hig More

Cloud data centers often fail to cope with the millions of delay-sensitive storage and computational requests due to their long distance from end users. A delay-sensitive request requires a response before its predefined deadline expires, even when the network has a high load of requests. Fog computing architecture, which provides computation, storage and communication services at the edge of the network, has been proposed to solve these problems. One of the fog computing challenges is how to allocate cloud and fog nodes resources to user requests in congestion conditions to achieve a higher acceptance rate of user requests and minimize their response time. Fog nodes have limited storage and computational power, and hence their performance is significantly reduced due to high load of user requests. This paper proposes an efficient resource allocation method in fog computing that decides where (fog or cloud) to process the requests considering the available resources of fog nodes and congestion conditions. According to the experimental results, the performance of the proposed method is better compared with existing methods in terms of average response time and percentage of failed requests. Manuscript profile

Open Access Article

7 - Detector Design & Power Allocation of Frequency Diverse Phased Multi Input Multi Output Radar within Nonhomogeneous Environments
Hamid Reza Fotoohi Firouzabad Seyed Mehdi Hosseini Andargoli Hossein Ghanei Yakhdan J. Abouei

In recent years, Phased-Multiple-Input, Multiple-Output radars (PMRs) have attracted great interest. PMR can combine the advantages of both MIMO radar and phased array radar. Here, PMR transmits orthogonal signals from all subarrays to provide both waveform frequency di More

In recent years, Phased-Multiple-Input, Multiple-Output radars (PMRs) have attracted great interest. PMR can combine the advantages of both MIMO radar and phased array radar. Here, PMR transmits orthogonal signals from all subarrays to provide both waveform frequency diversity and high coherent processing gain. In this paper dealt with detector design in the presence of heterogeneous clutter based on the unknown scattering coefficients for PMR. Then, detection probability and false-alarm probability are computed based on the derived optimum detector. At the end, the power allocation problem is investigated analytically. The numerical simulations show that obtained optimal detector is joint spatial-temporal filter, which, the clutters are effectively weakened in PMR. Furthermore, simulation results illustrate that proposed power allocation algorithm improve detection performance of PMR in comparison with PR and equal power PMR. Manuscript profile

Open Access Article

8 - The argument from particularization for God’s existence in Twelver Shīʿism kalām
Hamid Ataei Nazari

Among classical arguments for God’s existence in Islamic theology is The argument from particularization which is also called “the proof of possibility of attributes” and “proof of particularization”. The gist of the argument is that since all bodies share in corporeali More

Among classical arguments for God’s existence in Islamic theology is The argument from particularization which is also called “the proof of possibility of attributes” and “proof of particularization”. The gist of the argument is that since all bodies share in corporeality yet differ in various attributes, there must be some cause for the particularization of the attributes of bodies. the cause must be an eternal agent who is not a body. Different versions of the argument presented by Twelver Shīʿī theologians in various schools caused important and considerable evolution in the history of the argument. Such evolution in Twelver Shīʿism kalām has not been the subject of researches so far. In the present paper, having discussed different versions of the argument in Twelver Shīʿism kalām from the 6th/12th to 11th/17th centuries, illustrated changes and the evolution of the argument in the period. It shows that there are four different versions of The argument from particularization for God’s existence in the Twelver Shīʿism kalām, which are rather distinct in the premises and conclusion. The argument from particularization has not been received much attention from Twelver Shīʿī theologians, probably due to some fundamental flaws in it, including its disability to prove God as a necessary existent (wājib al-wujūd). Manuscript profile

Open Access Article

9 - A Multi-Objective Differential Evolutionary Algorithm-based Approach for Resource Allocation in Cloud Computing Environment
Saeed Bakhtiari Mahan Khosroshahi

In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effectiv More

In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effective use of system resources. Currently, common methods for scheduling in the cloud computing environment are performed using traditional methods such as Min-Min and meta-heuristic methods such as ant colony optimization algorithm (ACO). The above methods focused on optimizing one goal and do not estimate multiple goals at the same time. The main purpose of this research is to consider several objectives (total execution time, service level agreement and energy consumption) in cloud data centers with scheduling and optimal allocation of tasks. In this research, multi-objective differential evolution algorithm (DEA) is used due to its simple structure features and less adjustable parameters. In the proposed method, a new approach based on DEA to solve the problem of allocation in cloud space is presented which we try to be effective in improving resource efficiency and considering goals such as time, migration and energy by defining a multi-objective function and considering mutation and crossover vectors. The proposed method has been evaluated through a CloudSim simulator by testing the workload of more than a thousand virtual machines on Planet Lab. The results of simulation show that the proposed method in comparison with IqrMc, LrMmt and FA algorithms, in energy consumption by an average of 23%, number of migrations by an average of 29%, total execution time by an average of 29% and service level agreement violation (SLAV) by an average of 1% has been improved. In this case, use of the proposed approach in cloud centers will lead to better and appropriate services to customers of these centers in various fields such as education, engineering, manufacturing, services, etc. Manuscript profile

Open Access Article

10 - Improving resource allocation in mobile edge computing using gray wolf and particle swarm optimization algorithms
seyed ebrahim dashti saeid shabooei

Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigate More

Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation when offloading tasks based on mobile devices to edge servers in computing systems was investigated. Some tasks are processed locally and some are offloaded to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, it was introduced using the hybrid algorithm of particle swarm and gray wolf to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile

Open Access Article

11 - Machine Learning-Based Security Resource Allocation for Defending against Attacks in the Internet of Things
Nasim Navaei Vesal Hakami

Nowadays, the Internet of Things (IoT) has become the focus of security attacks due to the limitation of processing resources, heterogeneity, energy limitation in objects, and the lack of a single standard for implementing security mechanisms. In this article, a solutio More

Nowadays, the Internet of Things (IoT) has become the focus of security attacks due to the limitation of processing resources, heterogeneity, energy limitation in objects, and the lack of a single standard for implementing security mechanisms. In this article, a solution will be presented for the problem of security resources allocating to deal with attacks in the Internet of Things. Security Resource Allocation (SRA) problem in the IoT networks refers to the placement of the security resources in the IoT infrastructure. To solve this problem, it is mandatory to consider the dynamic nature of the communication environments and the uncertainty of the attackers' actions. In the traditional approaches for solving the SRA, the attacker works over based on his assumptions about the system conditions. Meanwhile, the defender collects the system's information with prior knowledge of the attacker's behavior and the targeted nodes. Unlike the mentioned traditional approaches, this research has adopted a realistic approach for the Dynamic Security Resources Allocation in the IoT to battle attackers with unknown behavior. In the stated problem, since there is a need to decide on deploying several security resources during the learning periods, the state space of the strategies is expressed in the combinatorial form. Also, the SRAIoT problem is defined as a combinatorial-adversarial multi-armed bandit problem. Since switching in the security resources has a high cost, in real scenarios, this cost is included in the utility function of the problem. Thus, the proposed framework considers the switching cost and the earned reward. The simulation results show a faster convergence of the weak regret criterion of the proposed algorithms than the basic combinatorial algorithm. In addition, in order to simulate the IoT network in a realistic context, the attack scenario has been simulated using the Cooja simulator. Manuscript profile

Open Access Article

12 - An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
Mahboubeh Zandieh Sepideh Adabi Samaneh Yazdani

Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goa More

Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goals: 1) low computation complexity, 2) high accuracy, and 3) high implementation simplicity in real cloud environments. CMM (Cloud Market Maker) is one of the most popular dynamic pricing models that has two advantages of computation accuracy and the possibility to implement in the real cloud environments. This model calculates the bid price based on a linear function. In designing this linear function, the parameters: buyer’s urgency, number of competitors and number of opponents are considered. Despite the advantages of this pricing function, the importance ratio of the constructor parameters of it is considered the same in various market conditions. Ignoring this issue reduces both system flexibility and computation accuracy in tangible changes in the cloud market. Therefore, the authors of this paper focus on designing a new cloud market-aware intelligent pricing system (which developed in customer side of the market) to tackle the mentioned problem. At the same time, high implementation simplicity of the proposed system should be guaranteed. For this purpose, an agent-based intelligent pricing system by combining support vector machine (SVM) and hierarchical analysis process (AHP) techniques is proposed. Simulation results show the better performance of the proposed solution which is named as DPMA in comparison to CMM. Manuscript profile

Open Access Article

13 - Improving IoT resource management using fog calculations and ant lion optimization algorithm
payam shams Seyedeh Leili Mirtaheri reza shahbazian ehsan arianyan

In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (proc More

In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (processing power, storage memory, and bandwidth) are first extracted. This component determines the requested traffic status of the application in terms of real-time. If the application is not real-time and is somewhat resistant to latency, the request will be referred to the cloud environment, but if the application needs to respond promptly and is sensitive to latency, it will be dealt with as a fog calculation. It will be written to one of the Cloudletes. In this step, in order to select the best solution in allocating resources to serve the users of the IoT environment, the ant milk optimization algorithm was used. The proposed method is simulated in MATLAB software environment and to evaluate its performance, five indicators of fog cells energy consumption, response time, fog cell imbalance, latency and bandwidth have been used. The results show that the proposed method reduces the energy consumption, latency rate in fog cells, bandwidth consumption rate, load balance rate and response time compared to the base design (ROUTER) 22, 18, 12, 22 and 47, respectively. Percentage has improved. Manuscript profile

Open Access Article

14 - Improving Resource Allocation in Mobile Edge Computing Using Particle Swarm and Gray Wolf Optimization Algorithms
seyed ebrahim dashti saeid shabooei

Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigat More

Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigated. Some tasks are uploaded and processed locally and some to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, a multi-objective hybrid algorithm of particle swarm and gray wolf was introduced to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. Local search in the particle swarm algorithm has good results in the problem, but it will cause the loss of global optima, so in this problem, in order to improve the model, the gray wolf algorithm was used as the main basis of the proposed algorithm, in the wolf algorithm Gray, due to the graphical approach to the problem, the set of global searches will reach the optimal solution, so by combining these functions, we tried to improve the operational conditions of the two algorithms for the desired goals of the problem. In order to create a network in this research, the network creation parameters in the basic article were used and the LCG data set was used in the simulation. The simulation environment in this research is the sim cloud environment. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%. Manuscript profile

List of Articles تخصیص

Rimag

Links

Related Centers

Technical Support

Official pages