Browsing by Author "Alhashmi, Saadat M."

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A survey of economic models in grid computing
(Elsevier, 2011-04-16) Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
Grid computing offers the network of large scale computing resources. Economic models are effective in collaborating large scale heterogeneous grid resources that are typically owned by different organizations. Not all the models provide same benefits for users in utilizing the resources. Similarly, the profit earned by resource providers also differs for different economic models. We survey the economic models used in grid computing since its inception until 2010. We discuss their advantages and disadvantages and analyze their suitability for usage in a dynamic grid environment. To the best of our knowledge, no such survey has been conducted in the literature up to now. Highlights ► Economic approaches are efficient in Grid computing. ► Different economic models are proposed for Grid computing. ► Address strengths and weaknesses of different economic models. ► Different models are suitable for different scenarios. ► Proposal of using different models for different scenarios. Full Text Link: https://doi.org/10.1016/j.future.2011.04.009
An Inspiration for Solving Grid Resource Management Problems Using Multiple Economic Models
(Springer, 2012) Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
Economic models can motivate resource providers to share resources across multiple administrations in Grid computing. Our survey on existing economic models in Grid computing identified that different economic models are suitable for different scenarios. In this paper, we conduct an experiment to quantify the strengths and weaknesses of widely proposed economic models in the Grid - Commodity Market, Continuous Double Auction, English Auction, Contract-Net-Protocol and Bargaining. Based on this experimental analysis, we identify regions where a particular economic model outperforms others. Then, we indicate that switching between the economic models could be used to maximize benefits in a specific scenario. Full Text Link: https://doi.org/10.1007/978-3-642-28675-9_1
An optimization-based adaptive resource management framework for economic Grids: A switching mechanism
(Elsevier, 2014-10-23) Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
The application of Grid computing has been broadening day by day. An increasing number of users has led to the requirement of a job scheduling process, which can benefit them through optimizing their utility functions. On the other hand, resource providers are exploring strategies suitable for economically efficient resource allocation so that they can maximize their profit through satisfying more users. In such a scenario, economic-based resource management strategies (economic models) have been found to be compelling to satisfy both communities. However, existing research has identified that different economic models are suitable for different scenarios in Grid computing. The Grid application and resource models are typically very dynamic, making it challenging for a particular model for delivering stable performance all the time. In this work, our focus is to develop an adaptive resource management architecture capable of dealing with multiple models based on the models’ domains of strengths (DOS). Our preliminary results show promising outcomes if we consider multiple models rather than relying on a single model throughout the life cycle of a Grid. Full Text Link: https://doi.org/10.1016/j.future.2014.10.022
Continuous Double Auction in Grid Computing: An Agent Based Approach to Maximize Profit for Providers
(IEEE Xplore, 2010-11-01) Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
Economic models are found efficient in managing heterogeneous computer resources such as storage, CPU and memory for grid computing. Commodity market, double auction and contract-net-protocol economic models have been widely discussed in the literature. These models are suitable for sharing distributed computer resources that belong to different owners. Agent technology can be used to manage these heterogeneous resources without human intervention, since agents are autonomous and intelligent in behavior. In this paper, we develop and simulate an agent-oriented double auction economic model. We compare the performance of our agent-oriented model with traditional double auction model, and show that the agent-oriented model is good in maximizing profit for providers. Full Text Link: http://doi.org/10.1109/WI-IAT.2010.105
Identifying and Modeling the Strengths and Weaknesses of Major Economic Models in Grid Resource Management
(Springer, 2014-01-17) Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
Economic-based approaches have been found to be effective for distributed resource management in Grid computing. However, deciding which model to use is challenging, because (1) the performance stability of a particular model in a dynamic and distributed environment, is hard to establish (2) the performance objective of the Grid network may be complex, and it is difficult to know which model would best fit such an objective, (3) evidence indicates that no single model can cope with every scenario, and (4) no suitable tools exist to accurately predict and contrast the performances of one model with another model in a particular domain. Understanding the strengths and weaknesses of widely proposed economic models in terms of a range of scenarios is, therefore, crucial. To address this, the authors developed a general evaluation platform suitable for analyzing the performance of different economic models in the Grid. This work identifies domains of strength of individual models and highlights their use in various scenarios of Grid computing. Full Text Link: https://doi.org/10.1007/s10723-013-9289-9
Implementation of Autonomous Pricing Mechanism in Grid Computing
(IEEE Xplore, 2010-06-01) Haque, S.M. Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
Grid computing shares geographically distributed computer resources, which are owned by different owners, over the Internet. Sharing the resources dynamically in this environment becomes challenging. Multi-agents could be used to meet this challenge due to their distributed nature and autonomous behavior. This paper investigates suitable economic models for grid computing and finds different economic models suitable for different grid scenarios. It proposes an agent-based pricing framework for the grid that supports autonomous interaction between users and providers for settling resource prices. Our strategy could be helpful to maximize utilization of idle resources and thus to increase profit for providers. Full Text Link: http://doi.org/10.1109/APWeb.2010.68
Ontology-based decentralized resource provisioning in economic grids
(Elsevier, 2013-03-26) Shaikh, Abdul Khalique; Haque, Aminul; Alhashmi, Saadat M.; Parthiban, Rajendran
A system that aggregates distributed and heterogeneous resources to solve computationally complex applications is known as Grid computing. One of the major challenges that current Grid systems are facing is the low resource utilization, resulting from the lack of suitable tools and mechanisms that understand the language of distributed applications. An effective utilization of resources depends on a better resource provisioning mechanism. The selection of resources in a Grid system involves finding and locating resources based on user requirements. Moreover, the performance of a Grid primarily depends on successful resource provisioning through scheduling and allocating resources according to users requirements. In this paper, we present a sub-domain ontology-based resource provisioning mechanism to increase the utilization of resources. We further extend the model to understand the characteristics of Grid entities in an economic system's point of view. We evaluate the significance of using dynamic pricing over static pricing to deal with the dynamic nature of the Grid. The results show improved success probability and system's profit compared to the traditional resource provisioning mechanisms.
Survival Analysis of Thyroid Cancer Patients Using Machine Learning Algorithms
(IEEE, 2024-04-22) Alhashmi, Saadat M.; Polash, Md. Shohidul Islam; Haque, Aminul; Rabbe, Fazley; Hossen, Shazzad; Faruqui, Nuruzzaman; Hashem, Ibrahim Abaker Targio; Abubacker, Nirase Fathima
The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to predict the prognosis of people with malignant growth rely on classification techniques. We could experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology, and End Results) data. These models were created using machine learning techniques by selecting univariate features and calculating correlations. We illustrated the variation in results and discrepancy of impurity that can result from varying data quantities and critical factors. Seventeen crucial factors were identified, and a group of classification algorithms were trained to evaluate the effectiveness of an estimation technique. In the display mode, the accuracy of these computations ranges from 97% to 99% A˙ long with accuracy, the models are further evaluated regarding the F1 score, precision, recall, and the AUC score. Compared to earlier studies, a more accurate model has been developed, and, to the best of our knowledge, our prediction model is superior to the models studied in the previous works.
Survival Analysis of Thyroid Cancer Patients Using Machine Learning Algorithms
(Scopus, 2024-04-22) Alhashmi, Saadat M.; Polash, Md. Shohidul Islam; Haque, Aminul; Rabbe, Fazley; Hossen, Shazzad; Faruqui, Nuruzzaman
The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to predict the prognosis of people with malignant growth rely on classification techniques. We could experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology, and End Results) data. These models were created using machine learning techniques by selecting univariate features and calculating correlations. We illustrated the variation in results and discrepancy of impurity that can result from varying data quantities and critical factors. Seventeen crucial factors were identified, and a group of classification algorithms were trained to evaluate the effectiveness of an estimation technique. In the display mode, the accuracy of these computations ranges from 97% to 99% A˙ long with accuracy, the models are further evaluated regarding the F1 score, precision, recall, and the AUC score. Compared to earlier studies, a more accurate model has been developed, and, to the best of our knowledge, our prediction model is superior to the models studied in the previous works.