Rasoul Mohammadi, S. K. Shekofieh, Mahmoud Naghibzadeh, Hamid Noori
{"title":"A dynamic special-purpose scheduler for concurrent kernels on GPU","authors":"Rasoul Mohammadi, S. K. Shekofieh, Mahmoud Naghibzadeh, Hamid Noori","doi":"10.1109/ICCKE.2016.7802143","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802143","url":null,"abstract":"GPUs are widely used as powerful accelerators for data-parallel applications such as financial and scientific applications in industrial and scientific areas. Effective scheduling of kernels can significantly enhance performance and utilization. In shared environments such as cloud, lots of kernels from users are being requested to be launched for execution. An effective kernel scheduling method can improve performance. In special environments such as space agency in which special tasks are processing separate fixed-size input data, special-purpose scheduling methods can be effective. In this paper, a dynamic special-purpose scheduler is proposed for scheduling specific tasks that are processing different fixed-size input data. Previous works mostly are static and can't schedule kernels that are launched in runtime. Experimental results show up to 25 percent improvement in execution time in the best case and 15 percent in average on NVIDIA GTX760.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130412623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hoora Ketabdar, Razieh Rezaee, Abbas Ghaemi-Bafghi, Masoud Khosravi-Farmad
{"title":"Network security risk analysis using attacker's behavioral parameters","authors":"Hoora Ketabdar, Razieh Rezaee, Abbas Ghaemi-Bafghi, Masoud Khosravi-Farmad","doi":"10.1109/ICCKE.2016.7802161","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802161","url":null,"abstract":"Computer networks consist of several assets such as hardware, software, and data sources. These assets have often some vulnerabilities which can be exploited by attackers that violate security policies in the network. Considering the limited budget, the network administrator should analyze and prioritize these vulnerabilities to be able to efficiently protect a network by mitigating the most risky ones. So far, several security parameters are offered to analyze security risks from the network security administrator's perspective. The major drawback of these methods is that they do not consider attacker's motivation. Depending on the motivation of potential attackers, different attack path may be selected for network security compromise. So, attacker's motivation is a key factor in predicting the attacker's behavior. In this paper, the attacker's motivation is considered in the process of security risk analysis, so network administrators are able to analyze security risks more accurately. The proposed method is applied on a network and the results are compared with novel works in this area. The experimental results show that network administrator will be able to precisely predict the behavior of attackers and apply countermeasures more efficiently.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Abbasi-Tadi, M. Khayyambashi, Hadi Khosravi-Farsani
{"title":"Data center task scheduling through Biogeography-Based Optimization model with the aim of reducing makespan","authors":"Ali Abbasi-Tadi, M. Khayyambashi, Hadi Khosravi-Farsani","doi":"10.1109/ICCKE.2016.7802113","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802113","url":null,"abstract":"Due to the rapid growth in the number of cloud users and the increment of data center users as the basis of clouds thereof, an optimal task scheduling problem would emerge as a vital issue in near future. Since, the complexity of optimal task scheduling nature, which is NP-Complete, the evolutionary algorithms render better performance than simple gradient-based algorithms. In the proposed approach, an evolutionary algorithm based on Biogeography-Based Optimization is applied to achieve optimal task scheduling in data centers. Workloads are distributed over virtual machines in a manner that total execution time (makespan) is minimized. An Information Base Repository (IBR) is considered and applied in order to store the online Virtual Machines load status. The IBR and the workloads information submitted to the data center are applied first to draw decisions for choosing which one of the VMs will be the receptive of the submitted workload; next, forwards the workload to the specified VM. The VM available resources of Memory, Bandwidth, storage and VM CPU Million Instruction Per Second are considered to find the optimal dispatching solution. Simulation results indicate that an increase in the number of VMs, would not change the time of getting optimal solution in a drastic manner and the covergence time increases in a slow graduation compared with task scheduling approaches, which is based on Genetic Optimization and Particle Swarm Optimization. So the total workload will be distributed in an optimal manner.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121645025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Model for evaluating trust between users in social networks based on subjective logic","authors":"Mahdi Zohreie, H. Shakeri","doi":"10.1109/ICCKE.2016.7802157","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802157","url":null,"abstract":"In recent years, social networks developed in terms of diversities, capabilities and users. Therefore, researchers in different fields have been attracted to these kind of networks. Trust management between users in these kind of networks is one of the most important research topics. In this article a new model for evaluating trust between users in social networks based on subjective logic is presented. Firstly, trust and confidence are calculated with regard to the criteria and categories have been considered. Then, these calculated opinions are converted to subjective logic model and finally these opinions will be combined to obtain general trust. For accepting or rejecting friend requests a threshold is considered. For evaluating the proposed model, a certain type of Leave-One-Out validation technique is used. The proposed model is evaluated by different kinds of evaluation methods. Precision and Recall metrics that have been used for this model, represent a significant improvement in this method over the other methods.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124100567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An energy efficient Metaheuristic method for Micro Robots Indoor area Coverage problem","authors":"Saeed Saeedvand, H. S. Aghdasi","doi":"10.1109/ICCKE.2016.7802121","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802121","url":null,"abstract":"Recently micro robots have become more popular for realizing many indoor area coverage applications. According to the characteristics and limited energy of micro robots, they usually are used as multi-cooperators at covering indoor areas. Hence, researchers proposed some different algorithms for solving indoor area coverage problem. As far as we are aware the existed algorithms for micro robots usually were the same algorithms provided for normal sized robots. At normal sized robots although researchers taking into account the obstacles at the area, most of them did not provide energy efficiency as much as possible. In this paper we propose an Energy efficient Metaheuristic method for Micro Robots Indoor area Coverage problem (EMMRIC). In the proposed method at first we partition area to the number of micro robots through restricted K-mean algorithm and then micro robots cover assigned subareas by utilizing a modified genetic algorithm. The simulation results of EMMRIC prove the correctness of the proposed method in comparison with the state-of-the-art one.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131001191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigating and Evaluating Energy-efficient Routing Protocols in Wireless Sensor Networks","authors":"M. Manouchehri","doi":"10.1109/ICCKE.2016.7802162","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802162","url":null,"abstract":"Wireless sensor networks are comprised of a group of nodes, which are randomly distributed in an environment. Since the energy consumptions of nodes are limited in these networks, data should be collected and sent optimally and in an energy-efficient way. The available routing protocols are used to send data in this way. The main responsibility of these protocols is to reduce energy consumption and increase network lifetime in routing. Energy-efficient routing protocols are investigated for such networks in this paper. These protocols are divided into three groups, based on data, network structure and reliability. Regarding data-centric protocols, on-demand and negotiation-based protocols are discussed. Based on the network structure, hierarchical and location-based protocols are introduced. In the reliability-based group, the quality of service and multipath routing protocols are presented. In addition to investigating the protocols of each group and challenges in energy-efficient routing, the obtained parameters are compared in order to introduce the best protocol in each group.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131623091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pooria Taghizadeh Naderi, Hadi Tabatabaee Malazi, M. Ghassemian, H. Haddadi
{"title":"Quality of claim metrics in social sensing systems: A case study on IranDeal","authors":"Pooria Taghizadeh Naderi, Hadi Tabatabaee Malazi, M. Ghassemian, H. Haddadi","doi":"10.1109/ICCKE.2016.7802128","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802128","url":null,"abstract":"There is an ongoing trend in social sensing where people act as sensors and report the events happening in their surroundings. These claims are often reported by smartphones and need to be processed to discover new patterns of events. Since these claims are not generated with consistent quality, the processing and evaluation tasks can become a challenge. In this paper, we address questions on how the quality of each claim can be evaluated, and which factors should be considered to qualify the quality of the claims. To do this, we investigate the sources of low-quality claims an propose a new form of Quality of Claim (QoC) metrics. We categorize the Quality of Claim factors into two classes of Content Measure and Feedback Measure. The study is performed on Two datasets. The main dataset is the #IranDeal extracted from Twitter. To compare the quality metrics, a second dataset is crawled from the Fouresqure social network. The metrics follow the power law pattern and are modeled by a Zipfian distribution function. The results show the power degree varies from 1.75 to 5. A number of factors are discussed as an influencer of the variation, such as the query criteria of the extracted dataset, the characteristics of the QoC metric, and the type of the social network.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114663751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cool elevator: A thermal-aware routing algorithm for partially connected 3D NoCs","authors":"Ebadollah Taheri, A. Patooghy, K. Mohammadi","doi":"10.1109/ICCKE.2016.7802125","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802125","url":null,"abstract":"Despite of high throughput and low fabrication cost of vertically partially connected 3D NoCs, thermal difficulties arise from poor heat dissipation and inappropriate traffic distribution of these kinds of 3D NoCs. This paper proposes an adaptive routing algorithm in order to manage thermal challenges in partially connected 3D NoCs. In the proposed routing algorithm, vertical links declare their availability/unavailability status to their neighbor nodes due to their current temperature. In this way, hot vertical links have a chance to reduce their traffic load and to cool down. In a predefined time periods vertical links update process is done to determine current hot and cool vertical links. In an updating time periods, cool vertical links are announced to the routers of each layer for transmitting packets to other layers. Access Noxim simulator is used to evaluates the routing algorithm in different partially 3D networks. Results show that the proposed routing algorithm decreases the number of overheated nodes by at least 74% and improves the thermal variance by at least 13%. These results are achieved within at most 10% overhead in average latency delay.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards a tracing framework for Model-Driven software systems","authors":"Fazilat Hojaji, B. Zamani, A. Hamou-Lhadj","doi":"10.1109/ICCKE.2016.7802156","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802156","url":null,"abstract":"Understanding software behavior by analyzing its execution traces is an important enabler for many software engineering tasks. In Model-Driven Development (MDD), dynamic analysis methods are often used to analyze executable models to enable the understanding of software behavior in early stages of the development process. An execution trace of a model can provide information to help reason about executable models. However, understanding an execution trace is not an easy task due to the size and complexity of typical traces. In this work, we aim at tackling this problem by proposing a model tracing framework, comprising compaction techniques to simplify the analysis of large traces at a higher level of abstraction, and a model tracing language, to capture run-time behavior of the executed model adequately.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122812487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Danya Karimi, K. Rangzan, G. Akbarizadeh, Mostafa Kabolizadeh
{"title":"A new method to improve classification accuracy of fused RADAR and optical data","authors":"Danya Karimi, K. Rangzan, G. Akbarizadeh, Mostafa Kabolizadeh","doi":"10.1109/ICCKE.2016.7802163","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802163","url":null,"abstract":"In past few decades, feature selection and learning have been considered by many researchers in terms of reducing the dimensionality of feature space and optimal feature selection. In traditional methods, feature selection and learning, are separately done. In this paper, a new method of supervised feature selection and learning, based on sparse regularization, was used to improve the classification accuracy of two pairs of fused radar and optical data for the first time. NMF features extracted from the images and the extracted features were used in two learned and unlearned forms as input to the SVM classifier, which choose as a base classifier. The results showed significant improvement in classification accuracy, resulting from the implementation of the sparse regularization algorithm based on L2, p norm.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124142823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}