Cloud Computing Technology to Assist Government in Decision Making Process

Cojoacă, Eugen Ștefan Dorel; Popescu, Mihai Octavian; Ambăruș, Gabriel Cristian

doi:10.24846/v26i2y201713

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Big data analytics presents a promising approach to address the energy consumption issue in mobile cloud computing. The wealth of usage, connectivity, and contextual data generated from mobile devices and networks can provide valuable insights into energy optimization [6][7][8][9]. For instance, big data analysis can uncover usage patterns to dynamically adjust system parameters for minimizing energy drainage.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

Mohammed,

Țăpuș

2023

Mesopotamian Journal of Big Data

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With the rapid proliferation of smart mobile devices and increasing adoption of cloud computing services, energy efficiency has become an important issue in mobile cloud environments. High energy consumption not only results in higher operational costs but also creates sustainability concerns related to cloud infrastructure and services. This paper proposes leveraging big data techniques such as machine learning and predictive analytics to optimize resource allocation and reduce energy consumption in mobile cloud computing. The massive amount of data on factors like user behavior, mobility patterns, network availability, and resource utilization can provide key insights to improve energy efficiency. We present an intelligent predictive framework to forecast mobile cloud resource demands and enable dynamic scaling of cloud configurations aligned to current needs. By proactively adapting cloud resources based on learned models and detected usage patterns, over-provisioning and under-utilization can be minimized. Specifically, we demonstrate how clustering, classification, regression, and times series models derived from contextual usage data can significantly improve energy efficiency when integrated with mobile cloud management systems. The proposed approaches are validated experimentally using simulated workloads and real-world trajectory data sets. Results indicate average energy savings of 42% and up to 62% for certain user groups compared to conventional cloud resource allocation techniques. This work provides an important contribution toward building more sustainable and energy efficient mobile cloud computing systems to meet the mobility and computing demands of the future through the transformative power of big data analytics.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

Mohammed,

Țăpuș

2023

Mesopotamian Journal of Big Data

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“…In the past years the IT market has been revolutionized by the advent of Cloud Computing, which has appealed to both small and big enterprises, and to public Governments [8] thanks to the benefits it brings: the "pay as you go" paradigm, according to which customers can rent hardware and software resources instead of buying them, thus reducing upfrontinvestments; the world-wide distribution of the hardware resources, which makes Cloud infrastructures more robust to natural disasters and thus more reliable; the possibility to make Cloud applications automatically scale, which implies a better use of existing resources and less management costs. Besides these and many more advantages we haven't mentioned here, Cloud Computing also offers the opportunity to compose services from different providers, in order to obtain complex applications, which exploit the best characteristics of different platforms.…”

Section: Introductionmentioning

confidence: 99%

A Tool for Mapping and Editing of Cloud Patterns: the Semantic Cloud Patterns Editor

Martino¹,

Esposito²

2018

STUD INFORM CONTROL

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Abstract:Owing to the huge number of Cloud services which are currently available on the market and to the lack of a commonly accepted standard for a machine-readable description of their interfaces, automatic discovery and composition tools and techniques for interoperable Cloud services are still in an early stage of development. Moreover, the current tools do not provide a very user-friendly interface to interact with. In this paper a service for the automatic discovery and composition of Cloud services, guided by Cloud Patterns, is presented. By means of a user-friendly interface, the user can both define a new services' composition, with the creation of a new pattern, or modify an existing one. The proposed service exploits a semantic based representation of Cloud services and patterns, complemented by a description of the input and output parameters for the several described services and of the patterns' workflow.

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“…Nowadays, the main researches in the area of MCC are based on the deduction of the mobile device energy consumption, coupled with making more offloading decisions [6]. The mobile cloud networking is a mushrooming technology whereby mobile devices are connected to a cloud server.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Mohammed¹,

Țăpuș²

2017

STUD INFORM CONTROL

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Abstract:The Mobile Cloud Computing (MCC) technology is a growing technology that aids in improving the quality of mobile services. The resources in MCC are dynamically allocated to the users based on their needs. The users pay for the resources consumed by their programs, but the drawbacks of process failures and knapsack problems of resource allocation still exist in MCC. Furthermore, the scheduling of energy consumption and computational cost is very high. To solve these issues, an optimized energy efficiency resource management technique is set forth in this study. The recommended method holds two stages: a) the initial stage, when the task loss, transmission probability, delay, utilization and reputation for every single task is individually measured and the enthalpy was calculated, and b) the second stage, when the enthalpy-related Cuckoo Search Optimization (CSO) algorithm was used to optimize and prioritize the resources to the powerful resource management. The execution of the recommended method automatically reduced the knapsack issue, energy and cost. A formal analysis of the resource management framework has ensured that the provider executes resources based on the power consumption. The performance of the suggested algorithm was benchmarked against the performance of other conventional algorithms.

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Cloud Computing Technology to Assist Government in Decision Making Process

Cited by 4 publications

References 3 publications

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

A Tool for Mapping and Editing of Cloud Patterns: the Semantic Cloud Patterns Editor

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

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