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Journal articles on the topic 'Utility computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Mondal, Ranjan Kumar, and Debabrata Sarddar. "Utility Computing." International Journal of Grid and Distributed Computing 8, no. 4 (2015): 115–22. http://dx.doi.org/10.14257/ijgdc.2015.8.4.11.

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2

Denne, Mark. "Pricing Utility Computing Services." International Journal of Web Services Research 4, no. 2 (2007): 114–27. http://dx.doi.org/10.4018/jwsr.2007040105.

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3

Eilam, T., K. Appleby, J. Breh, et al. "Using a utility computing framework to develop utility systems." IBM Systems Journal 43, no. 1 (2004): 97–120. http://dx.doi.org/10.1147/sj.431.0097.

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4

Drewes, Sarah, and Sebastian Pokutta. "Computing Discrete Expected Utility Maximizing Portfolios." Journal of Investing 23, no. 4 (2014): 121–32. http://dx.doi.org/10.3905/joi.2014.23.4.121.

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5

Chana, Inderveer, and Tarandeep Kaur. "Resource Scheduling Techniques in Utility Computing." International Journal of Systems and Service-Oriented Engineering 4, no. 2 (2014): 44–65. http://dx.doi.org/10.4018/ijssoe.2014040104.

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Utility Computing offers on-demand services from a shared pool of resources and can be envisaged to be a benchmark in the IT development. The capability to provide on-demand services involves management of large number of resources that are geographically dispersed and thus poses a number of resource management and scheduling challenges in the domain of resource heterogeneity, dynamic resource locations and load balancing. Proficient resource allocations and efficient scheduling helps in achieving optimal resource utilization and hence enhances the performance of the system. This paper evaluates existing resource management systems, listing their key characteristic features and highlighting the factors that make the existing systems excel upon each other. It also discusses various resource scheduling techniques currently available and characterizes the techniques based on Quality of Service (QoS) parameters supported by them along with the classification on basis of their operating environment and further extends towards load balancing and energy efficiency support if available.

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6

Chen, T. M. "Perspectives - internet computing as a utility." IEEE Communications Magazine 40, no. 5 (2002): 10. http://dx.doi.org/10.1109/mcom.2002.1000206.

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7

Herzfeldt, Alexander Bogislav, Hans Peter Rauer, Reimar Weißbach, and Christoph Ertl. "Cloud Computing as the Next Utility." International Journal of Cloud Applications and Computing 10, no. 4 (2020): 28–47. http://dx.doi.org/10.4018/ijcac.2020100103.

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Cloud computing has become the dominant provisioning model for IT services. This research adopts the provider's perspective to explore profitable service provisioning strategies: industrialization and standardization vs. service individualization. Based on a survey and expert interviews among cloud service providers, the authors identify that focusing on service individualization does not pay off for cloud service providers. Instead, cloud service providers should adopt industrialization and automation. Even more, they find that exploitation mechanisms of organizational learning significantly improve the cloud service profitability.

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Penmatsa, Satish, and Anthony T. Chronopoulos. "Cost minimization in utility computing systems." Concurrency and Computation: Practice and Experience 26, no. 1 (2012): 287–307. http://dx.doi.org/10.1002/cpe.2984.

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9

Singh, Hardeep. "Cloud Computing: An Internet Based Computing." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 116–21. http://dx.doi.org/10.24297/ijct.v2i3b.2701.

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Present paper focuses on the overview of Cloud Computing. Cloud Computing is an Internet-based computing; where resources, software and information are provided to computers on-demand, like a public utility; is emerging as a platform for sharing resources like infrastructure, software and various applications. This paper does a literature survey on some of the prominent applications of Cloud Computing, and how they meet the requirements of reliability, availability of data, scalability of software and hardware systems and overall customer satisfaction.

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Yeo, Chee Shin, and Rajkumar Buyya. "Integrated Risk Analysis for a Commercial Computing Service in Utility Computing." Journal of Grid Computing 7, no. 1 (2008): 1–24. http://dx.doi.org/10.1007/s10723-008-9103-2.

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Thistle, Paul D., and John E. Burnett. "Computing sets of expected utility maximizing distributions for common utility functions." Applied Financial Economics 11, no. 3 (2001): 269–77. http://dx.doi.org/10.1080/096031001300138663.

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12

Fox, Geoffrey, and Shrideep Pallickara. "Recent work in utility and cloud computing." Future Generation Computer Systems 29, no. 4 (2013): 986–87. http://dx.doi.org/10.1016/j.future.2013.01.002.

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13

Bi, Ran, Qian Liu, Jiankang Ren, and Guozhen Tan. "Utility aware offloading for mobile-edge computing." Tsinghua Science and Technology 26, no. 2 (2021): 239–50. http://dx.doi.org/10.26599/tst.2019.9010062.

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14

Marsh, P. "Just plug-in and go [utility computing]." Information Professional 2, no. 3 (2005): 20–27. http://dx.doi.org/10.1049/inp:20050304.

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15

Kumar, Dr Vinod, Er Gagandeep Raheja, and Ms Jyoti Sodhi. "CLOUD COMPUTING." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 5–7. http://dx.doi.org/10.24297/ijct.v4i1a.3025.

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Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the internet). Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Parallel to this concept can be drawn with the electricity grid, wherein end-users consume power without needing to understand the component devices or infrastructure required to provide the service.

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16

Elarbi, Badidi. "A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 6, no. 2 (2019): 1–19. https://doi.org/10.5281/zenodo.3565434.

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In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the quality of the services that consumers obtain from their providers. In this work, we propose a broker-based framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS) provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using a utility-driven selection algorithm that ranks the QoS offerings of potential SaaS providers. Then, it negotiates the SLA terms with that provider based on the quality requirements of the service consumer. The monitoring infrastructure observes SLA-compliance during service delivery by using measurements obtained from third-party monitoring services. We also define a utility-based bargaining decision model that allows the service consumer to express her sensitivity for each of the negotiated quality attributes and to evaluate the SaaS provider offer in each round of negotiation. A use-case with few quality attributes and their respective utility functions illustrates the approach.

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17

Zhang, Wenzhu, and Kaihang Tuo. "Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm." Electronics 12, no. 11 (2023): 2533. http://dx.doi.org/10.3390/electronics12112533.

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With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption.

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18

Von Solms, R., and M. Viljoen. "Cloud computing service value: A message to the board." South African Journal of Business Management 43, no. 4 (2012): 73–81. http://dx.doi.org/10.4102/sajbm.v43i4.483.

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This paper aims to alert the board to their duty of adding value to the organizations they represent by recognizing opportunities presented by new developments in information technology. Cloud computing is one such development, which is associated with opportunities and benefits. The service value that can be achieved by using this computing model will be influential in the adoption of cloud computing services. Service value is determined by the warranty and utility associated with that service. Thus, if an organization can associate itself with the utility and warranty on offer via cloud computing, it should consider the adoption of these services. Cloud computing is discussed in terms of service value. This promotes an understanding of factors to be considered when making decisions about the adoption of cloud computing.

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Buco, M. J., R. N. Chang, L. Z. Luan, C. Ward, J. L. Wolf, and P. S. Yu. "Utility computing SLA management based upon business objectives." IBM Systems Journal 43, no. 1 (2004): 159–78. http://dx.doi.org/10.1147/sj.431.0159.

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20

Stößer, Jochen, and Dirk Neumann. "GreedEx—a scalable clearing mechanism for utility computing." Electronic Commerce Research 8, no. 4 (2008): 235–53. http://dx.doi.org/10.1007/s10660-008-9023-z.

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21

Yolken, Benjamin, and Nicholas Bambos. "Game based capacity allocation for utility computing environments." Telecommunication Systems 47, no. 1-2 (2010): 165–81. http://dx.doi.org/10.1007/s11235-010-9309-z.

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22

Yeo, Chee Shin, Srikumar Venugopal, Xingchen Chu, and Rajkumar Buyya. "Autonomic metered pricing for a utility computing service." Future Generation Computer Systems 26, no. 8 (2010): 1368–80. http://dx.doi.org/10.1016/j.future.2009.05.024.

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23

Liang-Jie Zhang, Haifei Li, and Herman Lam. "Toward a Business Process Grid for Utility Computing." IT Professional 6, no. 5 (2004): 64–63. http://dx.doi.org/10.1109/mitp.2004.70.

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24

Pallickara, Shrideep. "Some Recent Advances in Utility and Cloud Computing." Future Generation Computer Systems 56 (March 2016): 315–16. http://dx.doi.org/10.1016/j.future.2015.11.018.

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25

Ogawa, Hirotaka, Satoshi Itoh, Tetsuya Sonoda, and Satoshi Sekiguchi. "GridASP: an ASP framework for Grid utility computing." Concurrency and Computation: Practice and Experience 19, no. 6 (2007): 885–91. http://dx.doi.org/10.1002/cpe.1092.

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26

Meng, Haoyu, Ying Zhu, and Ruilong Deng. "Optimal Computing Resource Management Based on Utility Maximization in Mobile Crowdsourcing." Wireless Communications and Mobile Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1494851.

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Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR) to outsource computation tasks to each computing resource provider (CRP). Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.

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Dr., R. Sridevi*1 &. C. B. Banupriya2. "A SURVEY ON CRYPTOGRAPHIC CLOUD STORAGE TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 602–5. https://doi.org/10.5281/zenodo.829787.

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The cloud computing has become a revolution in information technology, it includes elements from grid computing, utility computing and autonomic computing into a creative deployment architecture. The web service use web service to provide high performance and easy access of storage infrastructure.In this paper a survey of existing cryptographic storage techniques and benefits in cloud computing is discussed

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28

Wu, Jimmy Ming-Tai, Qian Teng, Gautam Srivastava, Matin Pirouz, and Jerry Chun-Wei Lin. "The Efficient Mining of Skyline Patterns from a Volunteer Computing Network." ACM Transactions on Internet Technology 21, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3423557.

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In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.

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29

HWANG, JUNSEOK, SO YOUNG KIM, HAK-JIN KIM, and JIHYOUN PARK. "AN OPTIMAL TRUST MANAGEMENT METHOD TO PROTECT PRIVACY AND STRENGTHEN OBJECTIVITY IN UTILITY COMPUTING SERVICES." International Journal of Information Technology & Decision Making 10, no. 02 (2011): 287–308. http://dx.doi.org/10.1142/s0219622011004336.

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Utility computing services constitute business opportunities in the customer-to-customer (C2C) marketplace. Providers deal with the risks and uncertainties faced by users while transacting with unfamiliar counterparts. Ensuring trustworthy transactions among unfamiliar users is an important condition of utility-computing service. Many peer-to-peer (P2P) sites utilize reputation systems; however, such systems are subject to lack of reviewer objectivity and robustness against attacks as well as unfair ratings. In this study, we propose the manual adjustment of reputation ratings by means of transaction monitoring and establish that the resulting enhanced objectivity and robustness in utility-computing service transactions will strengthen the trust management system. We also propose the optimal level of monitoring and penalizing activities based on the level of privacy concerns among users and the appropriate complementary service.

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30

Cho, Adrian. "Australia bets big on optical quantum computing." Science 384, no. 6696 (2024): 607–8. http://dx.doi.org/10.1126/science.adq3091.

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31

Li, Yan, Yubo Wang, Shiyong Chen, Xinyu Huang, and Tiancong Huang. "Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System." Sensors 23, no. 10 (2023): 4943. http://dx.doi.org/10.3390/s23104943.

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Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility.

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Markova, Oksana M., Serhiy O. Semerikov та Andrii M. Striuk. "ХМАРНІ ТЕХНОЛОГІЇ НАВЧАННЯ: ВИТОКИ". Information Technologies and Learning Tools 46, № 2 (2015): 29. http://dx.doi.org/10.33407/itlt.v46i2.1234.

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The research goal is to investigate the evolution of the concept of utility computing in the works of foreign researchers in the years 1959-1966. First the A. O. Mann’s results and expanded overview of the D. F. Parkhill’s results on the concept of computer (information) utility were introduced in the domestic scientific circulation. Functionally identity of the computer utility and cloud computing concepts was proved, as well as refined the primary sources of cloud service models. There was proposed the interpretation of the “cloud technologies of learning” concept. Continuity of the development of cloud technologies over the past 55 years and their relationship with the development of ICT in general was concluded. The research results make it possible to determine the prospects of the development of cloud computing in general and cloud technologies of learning in particular.

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33

Rath, Mamata. "Technical and Operational Utility of Ubiquitous Devices with Challenging Issues in Emerging Ubiquitous Computing." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 1 (2018): 16–35. http://dx.doi.org/10.4018/ijmdwtfe.2018010102.

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Ubiquitous computing is an advanced technology in the age of wireless networks that supports infiltration, activation and interconnectability of all the electronic and sensor-based objects related to everyday issues, and in this manner endeavours to empower a ubiquitous stream of information and data by coordinating learning and training. Ubiquitous computing is the method of enhancing computer-based computations by making use of many computers available throughout the physical environment, but making them effectively invisible to the user. The advanced technology of science and networks is instinctively welcoming the introduction of this revolutionary computing concept that guarantees to profoundly affect the way the authors associate with computing machineries, electronic gadgets, mobile devices, cyber spaces, and related peripherals. This new emerging technology of ubiquitous computing imagines a world where inserted processors, PCs, sensors, and advanced correspondences are reasonable products that are accessible at any time. Ubiquitous computing will encompass clients with an agreeable and helpful data condition that unites physical and computational foundations into incorporated natural surroundings. This living space will include an expansion of hundreds or thousands of computing gadgets and sensors that will give new usefulness, offer specific administrations, and lift efficiency and cooperation. The current research article projects technical and operational utility of ubiquitous devices with challenging issues in emerging ubiquitous computing.

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Zhu, Quan Sheng, Wei Liu, and Kun Gao. "Study on Simulation Cloud Computing of Power System." Applied Mechanics and Materials 441 (December 2013): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.441.1049.

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Cloud computing technology is an innovative IT application mode, which integrates computing, storage, network, information services infrastructure, operating system, application platform, WEB services and software resources. By using cloud computing technology, users can use many different services though the internet. This technology is the result of a variety of technologies evolution and integration, including distributed computing, grid computing, utility computing, visualization's technology, and SOA, etc. This paper introduces cloud computing principles and analyzes the necessity, key points and difficulties of the simulation cloud computing project of power system.

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35

Yang, Yu, Hua Zhou, Jun Hui Liu, and Yun Feng. "Dynamic Pricing Strategy and Optimal Resource Selection Strategy Based on Credibility Model." Applied Mechanics and Materials 668-669 (October 2014): 1615–20. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1615.

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With the gradual development of Cloud Computing, the model of work and business will fundamentally change in the future. Current market trading mechanism under the cloud computing environment is lacking in flexibility and most of companies adopt a fixed-rate pricing model, which is difficult to meet the different needs of users. Based on cloud bank model, this paper introduces economic theory to provide a theoretical basis for the development of resource prices and propose a dynamic pricing strategy and maximize utility resource selection strategy based on market supply and demand and credit for cloud bank. In the last part of this paper, we use simulation platform to do a simple experiment to test this dynamic pricing strategy. Experiment result shows the pricing strategy could adjust computing resource prices automatically under the general market price rule conditions and maximize utility resource selection strategy could get the max utility for resource consumers.

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36

Yu, Jia, and Rajkumar Buyya. "Scheduling Scientific Workflow Applications with Deadline and Budget Constraints Using Genetic Algorithms." Scientific Programming 14, no. 3-4 (2006): 217–30. http://dx.doi.org/10.1155/2006/271608.

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Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.

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37

Wu, Yina, Hui Ma, and Lina Chen. "Analyzing and Researching of the Economic Utility of Information Resource Configuration Based on Cloud Computing." Applied Mechanics and Materials 291-294 (February 2013): 2863–68. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2863.

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The amount of investment in the information technology facilities continue to improve, but the use of information resources is far from the desired target and resources are not utilized effectively,. The lower level configuration is gradually emerging. Cloud computing can help improve effective, rational, and scientific use of information resources to promote the information resource utility, thus contributing to the sustainable development of the information industry. The article from the economic point of view, especially from the economic utility and cost saving view, analyze the economic value of cloud computing configuration. Thus, It demonstrate the positive economic value of technological revolution application mode represented by the cloud computing from the point of view of economics.

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SUN, Ning-Hui, Kai LI, and Ming-Yu CHEN. "HPP: An Architecture for High Performance and Utility Computing." Chinese Journal of Computers 31, no. 9 (2009): 1503–8. http://dx.doi.org/10.3724/sp.j.1016.2008.01503.

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39

Tariq, Hasan, Anas Tahir, Farid Touati, Mohammed Abdulla E. Al-Hitmi, Damiano Crescini, and Adel Ben Manouer. "Geographical Area Network—Structural Health Monitoring Utility Computing Model." ISPRS International Journal of Geo-Information 8, no. 3 (2019): 154. http://dx.doi.org/10.3390/ijgi8030154.

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In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

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40

Padala, Pradeep, Kang G. Shin, Xiaoyun Zhu, et al. "Adaptive control of virtualized resources in utility computing environments." ACM SIGOPS Operating Systems Review 41, no. 3 (2007): 289–302. http://dx.doi.org/10.1145/1272998.1273026.

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41

Lysne, Olav, Sven-Arne Reinemo, Tor Skeie, et al. "Interconnection Networks: Architectural Challenges for Utility Computing Data Centers." Computer 41, no. 9 (2008): 62–69. http://dx.doi.org/10.1109/mc.2008.391.

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42

Dewitt, Barry, Hawre Jalal, and Janel Hanmer. "Computing PROPr Utility Scores for PROMIS® Profile Instruments." Value in Health 23, no. 3 (2020): 370–78. http://dx.doi.org/10.1016/j.jval.2019.09.2752.

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43

Brandic, Ivona, and Rajkumar Buyya. "Special section: Recent advances in utility and cloud computing." Future Generation Computer Systems 28, no. 1 (2012): 36–38. http://dx.doi.org/10.1016/j.future.2011.06.001.

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44

Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh. "Utility-Centric Service Provisioning in Multi-Access Edge Computing." Applied Sciences 9, no. 18 (2019): 3776. http://dx.doi.org/10.3390/app9183776.

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Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.

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Kallahalla, M., M. Uysal, R. Swaminathan, et al. "SoftUDC: a software-based data center for utility computing." Computer 37, no. 11 (2004): 38–46. http://dx.doi.org/10.1109/mc.2004.221.

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Kaur, Kuljeet, and Dr Pankaj Deep Kaur. "USE F CLOUD COMPUTING AND ITS BENEFITS." EPH - International Journal of Science And Engineering 6, no. 3 (2020): 34–38. http://dx.doi.org/10.53555/eijse.v6i3.80.

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With the initiation of internet in the 1990s to the present day facilities of universal computing, the internet has reformed the computing world in extreme way. It has travelled from the concept of parallel computing to distributed computing to cluster computing to grid computing to utility computing to virtualization and recently to cloud computing, in future Internet of Things. Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. In this paper explain the cloud computing deployment models and delivery models, with the help of model cloud computing do work properly in the area of IT sector. In this paper describe the traits and benefits of cloud computing.

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Huang, Xiaoge, Xuesong Deng, Chengchao Liang, and Weiwei Fan. "Blockchain-Enabled Task Offloading and Resource Allocation in Fog Computing Networks." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/7518534.

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To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain-enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain-enabled fog computing network task offloading and resource allocation algorithm (TR-BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two-stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR-BFCN algorithm can effectively reduce the network utility compared with other algorithms.

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SLAWINSKI, JAROSLAW, and VAIDY SUNDERAM. "TOWARDS COMPUTING AS A UTILITY VIA ADAPTIVE MIDDLEWARE: AN EXPERIMENT IN CROSS-PARADIGM EXECUTION." Parallel Processing Letters 23, no. 02 (2013): 1340002. http://dx.doi.org/10.1142/s0129626413400021.

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Rapid advances in cloud computing have made the vision of utility computing a near-reality, but only in certain domains. For science and engineering parallel or distributed applications, on-demand access to resources within grids and clouds is hampered by two major factors: communication performance and paradigm mismatch issues. We propose a framework for addressing the latter aspect via software adaptations that attempt to reconcile model and interface differences between application needs and resource platforms. Such matching can greatly enhance flexibility in choice of execution platforms — a key characteristic of utility computing — even though they may not be a natural fit or may incur some performance loss. Our design philosophy, middleware components, and experiences from a cross-paradigm experiment are described.

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Ridhi R Hegde, Devadiga Roshni Narayan, Sandhya Moolya, Chethan, and Dr. Pushparani M K. "A Review on The Future of Technology: How Cloud Computing is Changing the Game." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 06 (2024): 1784–93. http://dx.doi.org/10.47392/irjaeh.2024.0245.

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Cloud Computing is situated to industrialize the IT conveyance of the future. It is a common advancement of the broad selection of numerous specialized progresses in the conveyed computing range counting virtualization, lattice computing, autonomic computing, utility computing and software-as-a-service[2]. In addition, it explores the obstacles and concerns that influence increasing adoption and usage of the technology by clients. Findings illuminate present and future trends in cloud computing while exposing readers to associated challenges and issues. Reviewed literature indicates the technology's potential and expected growth in the coming years. Researchers have proposed various approaches to tackle cloud computing challenges, including security risks, through adaptive cloud computing and management of cloud computing [3].

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Badugu Ranjith Kumar, Et al. "KBD-Share: Key Aggregation, Blockchain, and Differential Privacy based Secured Data Sharing for Multi-User Cloud Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1290–301. http://dx.doi.org/10.17762/ijritcc.v11i10.8672.

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In today's era of widespread cloud computing and data sharing, the demand for secure and privacy-preserving techniques to facilitate multi-user data sharing is rapidly increasing. However, traditional approaches struggle to effectively address the twin objectives of ensuring privacy protection while preserving the utility of shared data. This predicament holds immense significance due to the pivotal role data sharing plays in diverse domains and applications. However, it also brings about significant privacy vulnerabilities. Consequently, innovative approaches are imperative to achieve a harmonious equilibrium between the utility of shared data and the protection of privacy in scenarios involving multiple users. This paper presents KBD-Share, an innovative framework that addresses the intricacies of ensuring data security and privacy in the context of sharing data among multiple users in cloud computing environments. By seamlessly integrating key aggregation, blockchain technology, and differential privacy techniques, KBD-Share offers an efficient and robust solution to protect sensitive data while facilitating seamless sharing and utilization. Extensive experimental evaluations convincingly establish the superiority of KBD-Share in aspects of data privacy preservation and utility, outperforming existing approaches. This approach achieves the highest R2 value of 0.9969 exhibiting best data utility, essential for multi-user data sharing in diverse cloud computing applications.

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