List of Articles Resource Allocation

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  1 - 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
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  2 - 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
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  3 - A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
  Abbas Mirzaei Amir Rahimi
  10.7508/jist.2019.01.005
  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided More

  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile
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  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
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  5 - High Level Synthesis of Decimal Arithmetic on Coarse Grain Reconfigurable Architectures
  Samaneh Emami
  The increasing capabilities of integrated circuits and the complexity of applications have led hardware design methods and tools to higher levels of abstraction and high-level synthesis is one of the key steps in increasing the level of abstraction. In recent years, ext More

  The increasing capabilities of integrated circuits and the complexity of applications have led hardware design methods and tools to higher levels of abstraction and high-level synthesis is one of the key steps in increasing the level of abstraction. In recent years, extensive research has been conducted on the design of decimal arithmetic reconfigurable architectures. Since, on the one hand, the effective use of these architectures depends on the existence of appropriate algorithms and tools to implement the design on the hardware, and on the other hand, research on the development of these algorithms has been very limited, this paper will present methods for the automated synthesis of decimal arithmetic circuits on a coarse-grained reconfigurable architecture. The platform chosen to execute the proposed algorithms is the DARA coarse-grained reconfigurable architecture, which is optimized for decimal arithmetic. The algorithms proposed for resource allocation of synthesis include a heuristic method and an ILP algorithm. The results show that, as expected, for the limited architectural dimensions used, the ILP algorithm performs significantly (about 30%) better than the heuristic algorithm. Manuscript profile
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  6 - 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
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  7 - 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
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  8 - 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
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  9 - 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