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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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SDVADC: Secure Deduplication and Virtual Auditing of Data in Cloud

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Procedia computer science 2020年 171卷 2225-2234页

作者： Geeta C M Shreyas Raju R G Raghavendra S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia Vice Chancellor Bangalore University Bengaluru Department of Computer Science and Engineering Florida International University USA INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.

关键词： Cloud Computing Deduplication Virtual Auditing Public Auditing Proof of Retrievability Proof of Ownership

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Dynamic Management of Traffic Signals through Social IoT

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Procedia computer science 2020年 171卷 1908-1916页

作者： Roopa M S Ayesha Siddiq S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik IoT Lab University Visvesvaraya College of Engineering Bangalore University Bengaluru India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University USA National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other.

关键词： Dynamic Traffic Control Traffic Congestion Management Social Internet of Vehicles (SIoV) Social Relationships

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PRIPRO:Solution for user profile control and management based on data privacy

PRIPRO:Solution for user profile control and management base...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Jonas Cesconetto Luís A. Silva R. Q. Valderi Leithardt María N. Cáceres Nuno M. Garcia Laboratory of Embedded and Distributed Systems University of Vale do Itajaí Brazil Department of Computer Science University of Salamanca Spain Departamento de Informática Universidade da Beira Interior Portugal

ISBN: (数字)9789895465903

ISBN: (纸本)9781728167244

Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles).

关键词： Androids Humanoid robots Internet Privacy Smart phones Hardware Information systems

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Work-in-Progress: Migration Hints in Real-Time operating systems

Work-in-Progress: Migration Hints in Real-Time Operating Sys...

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Real-Time systems Symposium (RTSS)

作者： Phillip Raffeck Peter Ulbrich Wolfgang Schröder-Preikschat Department of Computer Science Distributed Systems and Operating Systems Fricdrich-Alexanderr-Universit??t Erlangen-N??rnberg (FAU) Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9781728164632

ISBN: (纸本)9781728164649

Task migration is a potent instrument to exploit multi-core processors. Like full preemption, full migration is particularly advantageous as it allows the scheduler to relocate tasks at arbitrary times between cores. However, in hard real-time systems, migration is accompanied by a tremendous drawback: poor predictability and thus inevitable overapproximations in the worst-case execution-time analysis. This is due to the non-constant size of the tasks' resident set and the costs associated with its transfer between cores. As a result, migration is banned in many real-time systems, regressing the developer to a static allocation of tasks to cores with disadvantageous effects on the overall utilization and schedulability. In previous work, we successfully alleviated the shortcomings of full migration in real-time systems by reducing the associated costs and increasing its predictability. By employing static analysis, we were able to identify beneficial migration points and thus generate static schedules migrating tasks at these identified points. In ongoing work, we extend this approach to dynamic scheduling by providing information about advantageous migration points to an operating system which then makes migration decisions at runtime.

关键词： Task analysis Schedules operating systems Real-time systems Runtime Optimal scheduling Multicore processing

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A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings

A Stacking Multi-Learning Ensemble Model for Predicting Near...

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IEEE International Conference on Intelligent computer Communication and Processing (ICCP)

作者： Andreea Valeria Ves Nicoleta Ghitescu Claudia Pop Marcel Antal Tudor Cioara Ionut Anghel Ioan Salomie Department of Computer Science Technical University of Cluj-Napoca Distributed Systems Research Laboratory

The aim of this paper is to present a novel energy consumption forecasting solution for predicting energy demand at the level of residential buildings based on their historical consumption profile for a seamless integration with the session-based Energy Markets developed within Smart Energy Grids that integrate renewable energy sources. To overcome the drawbacks and lack of accuracy of existing prediction models, a stacked multi-learning ensemble model is proposed combining Gradient Boosting Regression, Multi-Layer Neural Networks and Long Short Term Memory Networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels. The proposed ensemble predictor is evaluated using the open-access UK-DALE dataset containing historical energy traces for 5 households spreading over several years, obtaining a best MAPE of 1.59%, a RMSE of 6. 19 kWh and a MAE of 5. 60 kWh on the aggregated dataset, proving the high accuracy and stability of the proposed solution as well as the feasibility of using ensemble models for residential building energy demand forecast for integration with session-based energy marketplaces.

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[email protected] : Electricity price and source aware resource management in geographically distributed heterogeneous datacenters

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Journal of systems and Software 2021年 175卷

作者： Hashim Ali Muhammad Zakarya Izaz Ur Rahman Ayaz Ali Khan Rajkumar Buyya Department of Computer Science Abdul Wali Khan University Mardan Pakistan Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users’ applications and their performance, therefore, the profit of the service providers. Furthermore, services could be offered from those geographical locations where electricity is relatively cheaper than other locations; which may degrade the applications’ performance and potentially increase users’ costs. To ensure larger providers’ profits and lower users’ costs, certain non-interactive workloads could be either: moved and executed in geographical locations offering the lowest energy prices; or could be queued and delayed to execute later (in day or night time) when renewables, such as solar and wind energies, are at peak. However, these may have negative impacts on the energy consumption, workloads performance, and users’ costs. Therefore, to ensure energy, performance and cost efficiencies, appropriate workload scheduling, placement, migration, and resource management techniques are required to mange the infrastructure resources, workloads, and energy sources. In this paper, we propose a workload placement and three different migration policies that maximize the providers’ revenues, ensure the workload performance, reduce energy consumption, along with reducing ecological impacts and users’ costs. Using real workload traces and electricity prices for several geographical locations and distributed, heterogeneous, datacenters, our experimental evaluation suggest that the proposed approaches could save significant amount of energy ( ∼ 15.26%), reduces service monetary costs ( ∼ 0.53% - ∼ 19.66%), improves ( ∼ 1.58%) or, at least, maintains the expected level of applications’ performance, and increases providers’ revenue along with

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A bilateral game approach for task outsourcing in multi-access edge computing

arXiv

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arXiv 2020年

作者： Xiao, Zheng He, Dan Chen, Yu Chronopoulos, Anthony Theodore Dustdar, Schahram Du, Jiayi College of Computer Science and Electronic Engineering Hunan University Hunan410082 China Department of Computer Science University of Texas San AntonioTX78249 United States Dept Computer Engineering Informatics University of Patras Rio26500 Greece Distributed Systems Group Vienna University of Technology Vienna Austria

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of edge servers’ resource allocation has been widely studied. Most of previous works focus on a single edge server (ES) serving multiple terminal entities (TEs), which restricts their access to sufficient resources. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. Game theory can be used as an effective tool to realize the interests of two or more conflicting individuals in the trading market. Therefore, we propose a bilateral game framework among multiple ESs and multiple TEs by modeling the task outsourcing problem as two noncooperative games: the supplier and customer side games. In the first game, the supply function bidding mechanism is employed to model the ESs’ profit maximization problem. The ESs submit their bids to the scheduler, where the computing service price is computed and sent to the TEs. While in the second game, TEs determine the optimal demand profiles according to ESs’ bids to maximize their payoff. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoff, as well as flattening the peak and off-peak load. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

arXiv

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arXiv 2020年

作者： Löffler, Felicitas Wesp, Valentin König-Ries, Birgitta Klan, Friederike Heinz-Nixdorf Chair for Distributed Information Systems Department of Mathematics and Computer Science Friedrich Schiller University Jena Jena Germany Michael-Stifel-Center for Data-Driven and Simulation Science Jena Germany Halle-Jena-Leipzig Germany Citizen Science Group DLR-Institute of Data Science German Aerospace Center Jena Germany

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. We analyze the primary source in dataset search - metadata - and determine if they reflect scholarly search interests. We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled. To determine search interests in biodiversity research, we gathered 169 questions that researchers aimed to answer with the help of retrieved data, identified biological entities and grouped them into 13 categories. The categories were evaluated with nine biodiversity scholars who assigned one of the types to pre-labeled biological entities in the questions. Our findings indicate that environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important search interests in biodiversity research. The comparison with existing metadata standards shows that domain-specific standards cover search interests quite well, whereas general standards do not explicitly contain elements that reflect search interests. We inspect metadata from five large data repositories. Our results confirm that metadata currently poorly reflect search interests in biodiversity research. From these findings, we derive recommendations for researchers and data repositories how to bridge the gap between search interest and metadata provided.

关键词： Semantic Web

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Enhancing data harvesting systems: Performance quantification of Cloud–Edge-sensor networks using queueing theory

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ICT Express 2025年

作者： Rocha, Jose Wanderlei Gomes, Eder Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Callou, Gustavo Silva, Francisco Airton Choi, Eunmi Nguyen, Tuan Anh Min, Dugki Lee, Jae-Woo Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piaui (UFPI) Picos Piaui R. Cicero Duarte No. 905 - Junco 64607-670 Brazil School of Software College of Computer Science Kookmin University Seoul 02707 South Korea Department of Software Engineering Faculty of Information Technology Ho Chi Minh City University of Industry and Trade (HUIT) Ho Chi Minh City 700000 Viet Nam Department of Artificial Intelligence Graduate School Konkuk University Seoul 05029 South Korea Department of Computer Science and Engineering College of Engineering Konkuk University Seoul 05029 South Korea Department of Aerospace Engineering Konkuk University Seoul 05029 South Korea

This study investigates a Cloud–Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model's applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud–edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management. © 2025 The Authors

关键词： Agricultural monitoring system Data harvesting Performance Queuing model Sensors

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