Computing Services Research Articles

Perancangan Net Support School Sebagai Monitoring Praktikum pada Laboratorium Komputer SMP Negeri 2 Bukittinggi

This research was conducted on the importance of computer laboratories and the implementation of teaching and learning activities because most of the material is practical. It will be very difficult to carry out practical learning without learning tools or media that are in accordance with the guidance of the curriculum material, there are other subjects that cannot be done without a laboratory. The practicum learning process is less than optimal because one of the reasons is that students don't pay too much attention to the learning material. And there are no tools or software to monitor student work in the process of using computers. The Network Development Lifecycle method is used to develop and design network topologies, in this case the use of the Net Support School application to monitor learning in the laboratory at SMP Negeri 2 Bukittinggi. The result of implementing net support school is that teachers can share learning materials from the computer server. Teachers can also supervise and control students' activities in using computers while the learning process is in progress. So using the net support school application is very helpful for use in the learning process.

The Role of the Supercomputer in the Modern World of Innovative Technologies

What is a supercomputer and why is it needed? What place does it occupy among computing equipment? Thanks to this article, we will determine the evolution and role of the supercomputer in the modern world and in the life of an average citizen. A supercomputer is a class of the most powerful computer systems available, which are evaluated in comparison with currently existing general purpose computer systems and the level of technology development. In general, the term "supercomputer" refers to a complex of server computers. All of them are connected to each other and work in parallel through a high-speed network. The similarity of a home computer and a supercomputer lies in their functions – data storage and processing, belonging to the same von Neumann architecture (the architecture of computing machines, the feature of which is the joint storage of data and machine commands in cells of the same memory). But the difference is huge in quantitative characteristics: these are the arrays of data that can be processed on these devices and the speed of performing the assigned tasks. By the way, a new unit of measurement – FLOPS – was proposed for measuring the speed of supercomputers. It is thanks to this value that performance is measured - the higher the number, the more powerful the computer is and which serves as the basis for ranking supercomputers. Today, supercomputers help solve a number of tasks in various fields – medicine, physics, mathematics, energy, etc. and are considered advanced computing technology. Traditionally, supercomputers are not considered rare: their development is ongoing and they bring a lot of benefits in many areas. In the future, powerful computing machines can become the main assistants in fields closely related to machine learning and artificial intelligence. Keywords: supercomputer, development evolution, computer architecture, FLOPS.

Research on collaborative edge network service migration strategy based on crowd clustering

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency.

Risk Assessment Edge Contract for Efficient Resource Allocation

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Research on Smart Community Construction from the Perspective of Agile Governance

The concept of smart communities emphasizes the synergistic effects between decision-making at the computational level and service delivery, which aligns perfectly with the notion of ‘agile governance’. Leveraging ‘agile governance’ as an analytical framework for the study of smart community construction, this paper decomposes the construction of smart communities supported by big data analytics technology into four aspects: balancing multiple objectives, collaborative governance among stakeholders, efficiency of tool transformation, and adaptability propagation. It also proposes relevant suggestions for constructing a community agile governance model based on smart community technology platforms, aiming to achieve efficient, intelligent, and sustainable development of community governance.

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks.

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme.

PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.

Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation. Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service. Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation. Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.

BopSkyline: Boosting privacy-preserving skyline query service in the cloud

With the widespread adoption of cloud computing, there has been great popularity of storing and querying databases in the cloud. However, such service outsourcing also entails critical data privacy concerns, as the cloud providers are generally not in the same trust domain as the data owners/users and could even suffer from data breaches. In this paper, different from most existing works that propose security designs for keyword search, we focus on secure realizations of advanced skyline query processing, which plays an important role in multi-criteria decision support applications. We propose BopSkyline, a new system framework for privacy-preserving skyline query service in cloud computing. BopSkyline is designed to not only ensure the confidentiality of outsourced databases, skyline queries, and query results, but also conceal data patterns (like the dominance relationships among database tuples) and search access patterns that may indirectly lead to data leakages. Notably, through a delicate synergy of key ideas on secure database shuffling and differentially private database padding, BopSkyline achieves a significant performance boost over the state-of-the-art. Extensive experiments demonstrate that compared with the state-of-the-art prior work, BopSkyline is up to 4.7× better in query latency and achieves up to 99.38% cost savings in communication.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites.

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

User perception as a factor for improving Trustworthiness in e-commerce systems

The User Interface (UI) is the first artefact that the user interacts with while developing a sense of trust that motivates him to use software applications more effectively. A badly designed UI can deceive users and bring the system into disrepute. Trustworthiness in UI is mandatory, as a poorly implemented UI can lead to the user misusing the system and jeopardizing the expected result. Trust in computational systems involves not only technical aspects, such as computational infrastructure, storage space, and service composition, but also aspects of Human-Computer Interaction (HCI). While technological aspects have received considerable attention, there are few research on human-computer interaction in terms of trust. This paper describes a way for assessing a system’s trustworthiness based on the user’s perception. The approach relies on a quality model to aggregate interface quality criteria in order to get a trustworthiness score. Three sets of experiments involving more than 300 individuals were carried out to validate the suggested methodology. A comparison was made between the trustworthiness score obtained through the methodology and the answers to open questions obtained through the users’ questionnaires. The results were consistent, and statistical analysis corroborated the positive assessment. Based on these results, examples of improvements were developed to highlight the usefulness of the approach for developing more trustworthiness interfaces.

Enhanced FRER Mechanism in Time-Sensitive Networking for Reliable Edge Computing.

Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9.

Sistem Absensi Dosen Berbasis Radio Frequency Identification (RFID) Sebagai Perhitungan Honor Dosen

This research designed a sense to find out the number of lecturers who enter and finish teaching lectures. Fikom, UMI still uses a manual system in managing lecturers' teaching salaries, so that it has several battles that usually occur in manual systems, including the attendance process and attendance recapitulation inefficient, less transparent because lecturers cannot access the issue of honorarium that lecturers will receive, and what will happen is that the salary calculation is not accurate so that it often results in disputes between lecturers and the finance department. on this occasion the author tries to design a working system of a tool that can recap lecturer attendance that can function to increase efficiency, transparency, and accuracy in the management of lecturer teaching honoraria. The system designed in this study was made using the RFID module and Raspberry Pi as the primary component of the system. The original way of working of this system comes from reading identification data from the RFID card carried by the lecturer. The data read by the RFID will be sent to the Raspberry Pi as input data. Raspberry pi will send RFID data readings to the server computer into input data. personal computer server will get data from Raspberry pi and will check the data, when the data received is synchronized with the ongoing course schedule, the server computer will send the data to Raspberry and will be displayed on the LCD. which will occur from this research shares that the process of computerized honorarium management tends to be easier and management has a better level of accuracy than using manual methods. in terms of news, reports that are designed tend to be simpler and faster because they are automatically in accordance with the input data entered previously.

Many-objective joint optimization of computation offloading and service caching in mobile edge computing

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

End-to-end network slicing for edge computing optimization

User-centric services proliferated by the smart devices is getting more demanding and characteristically diversified. Fall-risk assessment, augmented reality and similar services coexist in a shared heterogeneous setting. To meet the diversified and often conflicting requirements of the services, the physical network is decomposed into virtual slices. In order to address the optimal network slicing problem for various service types with different performance requirements, this study proposes a Mixed Integer Programming (MIP) model. This optimization model aims to satisfy the demands of the services and provide complete isolation among them through virtual resources reservation, including both networking and computation. Additionally, a heuristic implementation NESECS (NEtwork Slicing for Edge Computing Services) is proposed as an efficient solution for the cases where the optimization tools remain inadequate. The performance of the proposed solutions is evaluated with an extensive set of experiments. The obtained results indicate that the proposed optimization model is capable of providing optimal or near optimal solutions for small network instances, and NESECS algorithm can provide good solutions for larger instances by eliminating the time complexity.

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this paper, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Comprehensive Study on Cloud Computing Services and their Implications for Businesses and Industries

A review of the literature on cloud computing is conducted in this work. With its advantages for many kinds of enterprises, cloud computing is one of the computer industry's fastest-growing technologies. Since cloud computing entered the marketplace, service providers have developed a variety of amenities and made them accessible to potential clients. This paper presents the service model for cloud computing. In particular, Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). The study used a mixed research methodology. It entails compiling and assessing both quantitative and qualitative data from the identical literature review and combining the findings of previous, relevant studies. Results introduce readers to the difficulties and issues related to cloud computing while highlighting the trends in cloud computing both now and in the future. The literature study indicated that the use of technology is projected to increase in the future and shows promise. Researchers to solve the issues and difficulties associated with cloud computing, including privacy and security threats, mobile cloud computing, and cloud computing governance. Keywords—Cloud Computing Services, Cloud providers.

Athena – The NSF AI Institute for Edge Computing

AbstractThe National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

SummaryUnmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy‐efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV‐enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)‐oriented partial offloading framework for UAV‐enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k‐means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non‐orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.