International Journal of Modern Education and Computer Science, 2015
In real information systems, there are few static documents. On the other hand, there are too man... more In real information systems, there are few static documents. On the other hand, there are too many documents that their content change during the time that could be considered as signals to improve the quality of information retrieval. Unfortunately, considering all these changes could be time-consuming. In this paper, a method has been proposed that the time of analyzing these changes could be reduced significantly. The main idea of this method is choosing a special part of changes that do not make effective changes in the quality of information retrieval; but it could be possible to reduce the analyzing time. To evaluate the proposed method, three different datasets selected from Wikipedia. Different factors have been assessed in term weighting and the effect of the proposed method investigated on these factors. The results of empirical experiments showed that the proposed method could keep the quality of retrieved information in an acceptable rate and reduce the documents' analysis time as a result.
Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities
An important problem in improving mobility services consists in analyzing the transportation offe... more An important problem in improving mobility services consists in analyzing the transportation offer with respect to the demand of mobility. The purpose is always the assessment of the service for its improvements. This activity can be approached having all the historical data, while in most cases is not realistic due to the expensive process of data collection and lack of details about the movements of travelers at the bus stops in terms of pickup and drop-off for each bus line. To deal with these issues, in this paper, a model is provided to support mobility analysis in public transport networks. Our model operates first by analyzing the service offer, provided by mobility operators, and the service demands. Then, the model allows to evaluate the number of people who are picked-up and dropped-off at a stop. The performance of the model has been validated by comparing the observed values obtained from a field observation. The research and tool have been developed in the context of MOSAiC research project partially funded by Tuscany Region, with DISIT lab, ALSTOM, Municipia/Engineering, TAGES and CNIT research centers.
A main key success for public transportation networks is their tuning by the analysis of mobility... more A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key ...
The Internet of things has produced several heterogeneous devices and data models for sensors/act... more The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to...
Pay-as-you-go" is one of the basic properties of Cloud computing. It means that people pay for th... more Pay-as-you-go" is one of the basic properties of Cloud computing. It means that people pay for the resources or services that they use. Moreover, the concept of load balancing has been a controversial issue in recent years. It is a method that is used to split a task to some smaller tasks and allocate them fairly to different resources resulting in a better performance. Considering these two concepts, the idea of "Elasticity" comes to attention. An Elastic system is one which adds or releases the resources based on the changes of the system variables. In this thesis, we extended a distributed storage called Voldemort by adding a controller to provide elasticity. Control theory was used to design this controller. In addition, we used Yahoo! Cloud Service Benchmark (YCSB) which is an open source framework that can be used to provide several load scenarios, as well as evaluating the controller. Automatic control is accomplished by adding or removing nodes in Voldemort by considering changes in the system such as the average service time in our case. We will show that when the service time increases due to increasing the load, as generated by YCSB tool, the controller senses this change and adds appropriate number of nodes to the storage. The number of nodes added is based on the controller parameters to decrease the service time and meet Service Level Objectives (SLO). Similarly, when the average service time decreases, the controller removes some nodes to reduce the cost of using the resources and meet "pay-as-you-go" property.
Selecting the right plan is a key issue when moving to the cloud. When multiple applications need... more Selecting the right plan is a key issue when moving to the cloud. When multiple applications need to be outsourced at the same time, the problem becomes more complicated, and different challenges might need to be solved. In this paper, we address this problem, characterize different outsourcing scenarios, and propose a brokerage-based approach that, depending on the specific scenario, is able to compute one (or more) ranking(s) over a set of candidate plans, based on how they satisfy the specific application requirements. Our approach is able to combine application requirements to determine a plan that is suitable for all applications, possibly taking their importance into consideration (differentiating the impact that their requirements should have in the selection process), and allows different stakeholders to express such importance through linguistic variables, hence simplifying their definition and capturing the imprecision of human judgment. KEYWORDS application importance, application requirements, cloud plan, cloud plan selection, fuzzy numbers 1 INTRODUCTION The cloud is more and more becoming the reference paradigm for companies, public organizations, and private individuals for deploying their applications, which can thus be made available to clients over the Internet, hence reducing the economic costs and management burden as compared to a traditional in-house deployment. The cloud market features a multitude of different plans, differing in their characteristics and offered services. When an application needs to be outsourced to the cloud, selecting the right plan among those made available by providers is a key issue, and one plan is typically considered more suitable than another if its characteristics better match the specific requirements of the application itself. For instance, a mission-critical application would most likely need a highly performant cloud plan, while a secure plan would be the best choice for an application managing sensitive data. It is then important to have models that capture and express application requirements, and techniques that assess how much a candidate cloud plan can satisfy them. 1-7 Whenever moving to the cloud involves different applications, depending on the specific scenario, different challenges that require appropriate solutions can arise. For instance, different applications can have different (and possibly contrasting) needs, and a first question to be answered relates to how many plans the application owner(s) is (are) are willing to rely on: if each application can be outsourced to a specific plan, then the selection process can operate on each application singularly taken, to select the best plan for it. If, on the contrary, a single plan has to be selected for all applications, it is essential to properly combine their requirements, to select a plan that is considered globally acceptable by all applications. Some preliminary efforts have been made in this direction in, 4 which, however, assumes that all applications to be outsourced are equally important, with the consequence that the requirements of all the applications have the same impact on the cloud plan selection process. However, the applications that need to be outsourced might differ, besides on their requirements, also on their perceived importance. In this case, it is essential to take application importance into consideration in the outsourcing process, to ensure that the requirements
International Journal of Intelligent Systems and Applications, 2016
Cloud users usually have different preferences over their applications that outsource to the clou... more Cloud users usually have different preferences over their applications that outsource to the cloud, based on the financial pro fit of each application's execution. Moreover, various types of virtual machines are offered by a cloud service provider with distinct characteristics , such as rental prices, availab ility levels , each with a different probability of occurrence and a penalty, which is paid to the user in case the virtual mach ine is not available. Therefore, the problem o f applicat ion scheduling in cloud computing environments, considering the risk of financial loss of application-to-VM assignment becomes a challenging issue. In this paper, we propose a riskaware scheduling model, using risk analysis to allocate the applications to the virtual machines , so that, the expected total pay-off o f an application is maximized, by taking into account of the priority of applications. A running examp le is used through the paper to better illustrate the model and its application to imp rove the efficiency of resource assignment in cloud computing scenarios.
Modularization is one of the important subjects in the software design area which leads to increa... more Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application.
2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016
An important problem when moving an application to the cloud consists in selecting the most suita... more An important problem when moving an application to the cloud consists in selecting the most suitable cloud plan (among those available from cloud providers) for the application deployment, with the goal of finding the best match between application requirements and plan characteristics. If a user wishes to move multiple applications at the same time, this task can be complicated by the fact that different applications might have different (and possibly contrasting) requirements. In this paper, we propose an approach enabling users to select a cloud plan that best balances the satisfaction of the requirements of multiple applications. Our solution operates by first ranking the available plans for each application (matching plan characteristics and application requirements) and then by selecting, through a consensus-based process, the one that is considered more acceptable by all applications.
Cloud computing is a key technology for outsourcing data and applications to external providers. ... more Cloud computing is a key technology for outsourcing data and applications to external providers. The current cloud market offers a multitude of solutions (plans) differing from one another in terms of their characteristics. In this context, the selection of the right plan for outsourcing is of paramount importance for users wishing to move their data/applications to the cloud. The scientific community has then developed different models and tools for capturing users' requirements and evaluating candidate plans to determine the extent to which each of them satisfies such requirements. In this chapter, we illustrate some of the existing solutions proposed for cloud plan selection and for supporting users in the specification of their (crisp and/or fuzzy) needs.
International Journal of Modern Education and Computer Science, 2015
In real information systems, there are few static documents. On the other hand, there are too man... more In real information systems, there are few static documents. On the other hand, there are too many documents that their content change during the time that could be considered as signals to improve the quality of information retrieval. Unfortunately, considering all these changes could be time-consuming. In this paper, a method has been proposed that the time of analyzing these changes could be reduced significantly. The main idea of this method is choosing a special part of changes that do not make effective changes in the quality of information retrieval; but it could be possible to reduce the analyzing time. To evaluate the proposed method, three different datasets selected from Wikipedia. Different factors have been assessed in term weighting and the effect of the proposed method investigated on these factors. The results of empirical experiments showed that the proposed method could keep the quality of retrieved information in an acceptable rate and reduce the documents' analysis time as a result.
Proceedings of the 1st ACM International Workshop on Technology Enablers and Innovative Applications for Smart Cities and Communities
An important problem in improving mobility services consists in analyzing the transportation offe... more An important problem in improving mobility services consists in analyzing the transportation offer with respect to the demand of mobility. The purpose is always the assessment of the service for its improvements. This activity can be approached having all the historical data, while in most cases is not realistic due to the expensive process of data collection and lack of details about the movements of travelers at the bus stops in terms of pickup and drop-off for each bus line. To deal with these issues, in this paper, a model is provided to support mobility analysis in public transport networks. Our model operates first by analyzing the service offer, provided by mobility operators, and the service demands. Then, the model allows to evaluate the number of people who are picked-up and dropped-off at a stop. The performance of the model has been validated by comparing the observed values obtained from a field observation. The research and tool have been developed in the context of MOSAiC research project partially funded by Tuscany Region, with DISIT lab, ALSTOM, Municipia/Engineering, TAGES and CNIT research centers.
A main key success for public transportation networks is their tuning by the analysis of mobility... more A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key ...
The Internet of things has produced several heterogeneous devices and data models for sensors/act... more The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to...
Pay-as-you-go" is one of the basic properties of Cloud computing. It means that people pay for th... more Pay-as-you-go" is one of the basic properties of Cloud computing. It means that people pay for the resources or services that they use. Moreover, the concept of load balancing has been a controversial issue in recent years. It is a method that is used to split a task to some smaller tasks and allocate them fairly to different resources resulting in a better performance. Considering these two concepts, the idea of "Elasticity" comes to attention. An Elastic system is one which adds or releases the resources based on the changes of the system variables. In this thesis, we extended a distributed storage called Voldemort by adding a controller to provide elasticity. Control theory was used to design this controller. In addition, we used Yahoo! Cloud Service Benchmark (YCSB) which is an open source framework that can be used to provide several load scenarios, as well as evaluating the controller. Automatic control is accomplished by adding or removing nodes in Voldemort by considering changes in the system such as the average service time in our case. We will show that when the service time increases due to increasing the load, as generated by YCSB tool, the controller senses this change and adds appropriate number of nodes to the storage. The number of nodes added is based on the controller parameters to decrease the service time and meet Service Level Objectives (SLO). Similarly, when the average service time decreases, the controller removes some nodes to reduce the cost of using the resources and meet "pay-as-you-go" property.
Selecting the right plan is a key issue when moving to the cloud. When multiple applications need... more Selecting the right plan is a key issue when moving to the cloud. When multiple applications need to be outsourced at the same time, the problem becomes more complicated, and different challenges might need to be solved. In this paper, we address this problem, characterize different outsourcing scenarios, and propose a brokerage-based approach that, depending on the specific scenario, is able to compute one (or more) ranking(s) over a set of candidate plans, based on how they satisfy the specific application requirements. Our approach is able to combine application requirements to determine a plan that is suitable for all applications, possibly taking their importance into consideration (differentiating the impact that their requirements should have in the selection process), and allows different stakeholders to express such importance through linguistic variables, hence simplifying their definition and capturing the imprecision of human judgment. KEYWORDS application importance, application requirements, cloud plan, cloud plan selection, fuzzy numbers 1 INTRODUCTION The cloud is more and more becoming the reference paradigm for companies, public organizations, and private individuals for deploying their applications, which can thus be made available to clients over the Internet, hence reducing the economic costs and management burden as compared to a traditional in-house deployment. The cloud market features a multitude of different plans, differing in their characteristics and offered services. When an application needs to be outsourced to the cloud, selecting the right plan among those made available by providers is a key issue, and one plan is typically considered more suitable than another if its characteristics better match the specific requirements of the application itself. For instance, a mission-critical application would most likely need a highly performant cloud plan, while a secure plan would be the best choice for an application managing sensitive data. It is then important to have models that capture and express application requirements, and techniques that assess how much a candidate cloud plan can satisfy them. 1-7 Whenever moving to the cloud involves different applications, depending on the specific scenario, different challenges that require appropriate solutions can arise. For instance, different applications can have different (and possibly contrasting) needs, and a first question to be answered relates to how many plans the application owner(s) is (are) are willing to rely on: if each application can be outsourced to a specific plan, then the selection process can operate on each application singularly taken, to select the best plan for it. If, on the contrary, a single plan has to be selected for all applications, it is essential to properly combine their requirements, to select a plan that is considered globally acceptable by all applications. Some preliminary efforts have been made in this direction in, 4 which, however, assumes that all applications to be outsourced are equally important, with the consequence that the requirements of all the applications have the same impact on the cloud plan selection process. However, the applications that need to be outsourced might differ, besides on their requirements, also on their perceived importance. In this case, it is essential to take application importance into consideration in the outsourcing process, to ensure that the requirements
International Journal of Intelligent Systems and Applications, 2016
Cloud users usually have different preferences over their applications that outsource to the clou... more Cloud users usually have different preferences over their applications that outsource to the cloud, based on the financial pro fit of each application's execution. Moreover, various types of virtual machines are offered by a cloud service provider with distinct characteristics , such as rental prices, availab ility levels , each with a different probability of occurrence and a penalty, which is paid to the user in case the virtual mach ine is not available. Therefore, the problem o f applicat ion scheduling in cloud computing environments, considering the risk of financial loss of application-to-VM assignment becomes a challenging issue. In this paper, we propose a riskaware scheduling model, using risk analysis to allocate the applications to the virtual machines , so that, the expected total pay-off o f an application is maximized, by taking into account of the priority of applications. A running examp le is used through the paper to better illustrate the model and its application to imp rove the efficiency of resource assignment in cloud computing scenarios.
Modularization is one of the important subjects in the software design area which leads to increa... more Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application.
2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016
An important problem when moving an application to the cloud consists in selecting the most suita... more An important problem when moving an application to the cloud consists in selecting the most suitable cloud plan (among those available from cloud providers) for the application deployment, with the goal of finding the best match between application requirements and plan characteristics. If a user wishes to move multiple applications at the same time, this task can be complicated by the fact that different applications might have different (and possibly contrasting) requirements. In this paper, we propose an approach enabling users to select a cloud plan that best balances the satisfaction of the requirements of multiple applications. Our solution operates by first ranking the available plans for each application (matching plan characteristics and application requirements) and then by selecting, through a consensus-based process, the one that is considered more acceptable by all applications.
Cloud computing is a key technology for outsourcing data and applications to external providers. ... more Cloud computing is a key technology for outsourcing data and applications to external providers. The current cloud market offers a multitude of solutions (plans) differing from one another in terms of their characteristics. In this context, the selection of the right plan for outsourcing is of paramount importance for users wishing to move their data/applications to the cloud. The scientific community has then developed different models and tools for capturing users' requirements and evaluating candidate plans to determine the extent to which each of them satisfies such requirements. In this chapter, we illustrate some of the existing solutions proposed for cloud plan selection and for supporting users in the specification of their (crisp and/or fuzzy) needs.
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Papers by Ala Arman