Computer System Resources Research Articles

Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.

Comparative Study: Preemptive Shortest Job First and Round Robin Algorithms

Abstract: Operating system is a software acting as an interface between computer hardware and user. Operating system is known as a resource manager. The main responsibility of operating system is to handle resources of computer system. Scheduling is a key concept in computer multitasking and multiprocessing operating system design by switching the CPU among process. Shortest job first (SJF) and round robin are two wellknown algorithms in CPU processing. For shortest job first, this algorithm can be preemptived. In preemptive shortest job first, when a new process coming in, the process can be interupted. Where with round robin algorithm there will be time slices, context switching, or also called quantum, between process. In this journal we wil discuss comparative study between preemptive shortest job first and round robin algorithms. Three comparative studies will be discussed to understand these two algorithms more deeply. For all comparative study, the average waiting time and average turnaround time is more for round robin algorithm. In the first comparative study, we get average waiting time 52% more. For average turnaround time, 30% more. In second comparative analysis, we get 52 % average waiting time more and we get 35 % average turnaround time more. For third comparative analysis, average waiting time we get 50% more and for average turnaround time, we get 28% more. Thus it is concluded in our comparative study for these kind of data the preemptive shortest job first is more efficient then the round robin algorithm.
 
 Keywords: comparative study, premptive shortest job first algorithm, round robin algorithm, turn around time, average waiting time, time slice

Blockchain-Assisted Access Control with Shuffled Shepherd Fire Hawk Optimisation-Key Generation for Privacy Protection in Cloud

Cloud is a model of computing providing on-demand availability of computer system resources, mainly data storage and computing power, without the user’s active management. This provides sharing and reduces user computing and storage costs. With the development of cloud scale and intensification, cloud security is becoming a vital issue in the cloud computing field. Here, a newly optimised hybridisation algorithm, namely, Shuffled Shepherd Fire Hawk Optimization (SSFHO) algorithm is proposed for key generation to initiate privacy protection in the cloud. The different entities involved in this approach are Data Owner (DO), Data User (DU), blockchain, and Cloud Service Provider (CSP). Here, the access control process includes seven categories, such as initialisation, registration, generation of key, data access control, authorisation, authorisation revocation and data protection. Also, registration is done in three phases, like registration of cloud, registration of user and resource publishing. Moreover, SSFHO is the integration among Shuffled Shepherd Optimization Algorithm (SSOA) and Fire Hawk Optimization (FHO) algorithm. The performance of the model is done with various performance metrics like, authorisation time, privacy rate, and memory. These revealed high values of privacy rate of 0.93, low values of authorisation time and memory of 0.559 and 0.843 GHz.

Secure data storage and access for fish monitoring in cloud environment

Cloud computing (CC) refers to the on-demand availability of computer system resources, specifically data storage and computational power, without the user's direct active administration. Large cloud functions are usually split across multiple sites, each of which is a data center. Improve the security howerver, these method possess some disadvantages such as access time, resource utilization, limited computation and storage capacity. To overcome these challenges trapdoor-based NDSGA-II AC (Non-Dominated Sorting Genetic Algorithm Access Control) is proposed, In the proposed technique trapdoor has been used which reduces the energy consumption when compared to the existing systems. With the help of trapdoor access control and NDSGA-II, this paper stores water tank parameters in the cloud. A stream cipher algorithm (SCA) is utillize to encrypt the input data based on the water tank parameters collected with sensors. It is possible to store encrypted data in the cloud once the encrypting process is complete. To decrease energy consumption, average resource utilization, and access time, the trapdoor-based NDSGA-II access control is used. In this paper, Number Theory Research Unit (NTRU) cryptosystem, CP-ABE, and RBAC are used to analyze the performance of the proposed method. This proposed method reduces the encryption time by 11%, 16%, and 23% compared to the current method, and the access time by 8% and 15% compared to CP-ABE and RBAC.

A systematic review on resource provisioning in fog computing

AbstractResource provisioning is allocating resources to clients over the Internet and plays a prominent role in cloud computing infrastructure‐as‐a‐service (IaaS). The main issues of resource provisioning are meeting user demands like bandwidth, response time, throughput, availability, and so on. The cloud computing framework uses virtualization technology to provide on‐demand access to computer system resources, specifically computation and storage. In cloud computing, computing resources are shared across another communication network using virtualization. However, the cloud computing mechanism cannot meet the needs of a large number of Internet of Things (IoT) services. To consider this, fog computing was introduced as a modified mechanism of the cloud paradigm by providing cloud services for the end devices. Fog computing is a decentralized computing model placed between devices, edges, data centers or the cloud. In this way, it helps to meet the demands initiated by IoT services, such as reduced latency, wide mobility coverage, and so on. Several solutions regarding resource allocation in fog computing are available in the research field, but such solutions have not achieved satisfactory results. Therefore, finding a solution by analyzing recent problems is open research. Therefore, this paper reviews different methods established for resource provisioning in fog computing using different parameters from 2015 to 2021 and introduces the limitations, advantages and future directions related to different resource provisioning techniques.

Integrated cloud computing and blockchain systems: A review

Blockchain technology is one of the crypto-currency technologies that has received a lot of attention. It has also found use in various applications, including the Internet of Things (IoT) and Cloud computing. Nonetheless, Blockchain has a significant scalability issue, restricting its ability to support services with various transactions. On the other hand, cloud computing is the on-demand availability of shared computer system resources, although issues now beset it in automation, processes, management, policies, and human aspects. Combining cloud computing and blockchain technology into a single system can improve network control, task scheduling, data integrity, resource management, pricing, fair payment, and resource allocation. In this article, we offered a comprehensive and up-to-date survey of cloud computing and Blockchain integration, a critical service for business applications due to the benefits of privacy, security, and service support. The lack of a comprehensive assessment examining the significance of BaaS platforms used in cloud computing prompted this review. We focus on the various BaaS tools that are currently in use. This report also examines the most common BaaS platforms incorporating Blockchain as a cloud service, such as Alibaba, Oracle, Azure, Amazon, and IBM. Furthermore, this research highlighted some major technological issues associated with merging Blockchain with cloud computing.

Cloud-based deep learning-assisted system for diagnosis of sports injuries

At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.

Service Caching Based Aerial Cooperative Computing and Resource Allocation in Multi-UAV Enabled MEC Systems

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Classifying jobs towards power-aware HPC system operation through long-term log analysis

The efficient utilization of high-performance computing (HPC) system resources under rigorous electric power budget or I/O workload constraints is among the most important goals set by system operators to deal with the demanding requirements of application users. In most cases, the effective utilization of CPU and memory devices, which is tightly linked to electric power consumption, is a counterpart metric of I/O activities in most HPC jobs. Towards higher utilization of HPC systems under strict electric power consumption and I/O activity management constrains, we must be careful to prevent hot-spots from developing in power consumption or I/O operations that could lead to unstable system operations by exceeding electric power supply or I/O subsystem capabilities. One of the feasible solutions is arranging compute node assignment not to have such hot-spots in electric power or I/O operations. To address this issue, we analyzed vast amounts of log data collected from the K computer and found strong positive correlations between CPU and memory device utilization rates and electric power consumption levels. On the one hand, we also observed strong negative correlations and reduced electric power consumption in relation to file I/O activities in a specific compute node-layout, thereby indicating unique characteristics in some I/O-intensive HPC jobs in the node-layout. Our investigation revealed that HPC jobs could be divided into two groups when classified in terms of required electric power — jobs consuming high electric power levels and I/O-intensive jobs with reduced electric power levels. Then, we achieved high levels of accuracy when classifying jobs in terms of electric power levels using RandomForestClassifier among multiple machine learning classification models provided from scikit-learn . The classification can prevent us from hot-spots in electric power consumption in compute node assignment in job scheduling. Thus we demonstrated efficient job classifications towards power-aware system operations in the supercomputer Fugaku, which is the successor to the K computer.

Cost-Efficient Deadline Constrained Scientific Workflow Scheduling in Infrastructure-as-a-Service Clouds by Disqualifying Tasks with Anomalies

Cloud computing has transformed the way businesses and consumers think about their data and businesses. As a result, cloud computing is described as the on-demand availability of all computer system resources via the Internet as a paid service. Enhancing security is a major problem in cloud computing, which is also a major research topic because data is stored and processed in remote locations held by third parties. Another key research subject is the allocation of virtual machines to incoming workloads to decrease the cost of consumers' workload execution in cloud environments. Both of the aforementioned difficulties are addressed in this study. Deadline-constrained workflows are submitted to the application server, which goes through a pre-processing step, that identifies the presence of anomalies in the workflow tasks and disqualifies those tasks with anomalies, and schedules the adjusted workloads into heterogeneous Virtual machines using a modified-PCP algorithm. Our approach is compared to the IC-LOSS and IC-PCP algorithms that are already in use. In comparison to the existing IC-PCP and IC-LOSS algorithms, experimental results suggest that using the modified-PCP algorithm for deadline constrained workflows after deleting those anomalous tasks produces better results.

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.

NFC Based Login for Mobile Apps and Websites

Abstract: This paper is concerned to show how a mobile phone equipped with Near Field Communication (NFC) can be used to construct a Single Sign-On authentication system (Celikkan & Gelis, 2014, August). When logging in to numerous online sites, customers no longer have to juggle a plethora of usernames and passwords to keep track of. As omnipresent computing devices, smartphones are utilised for a wide range of functions, from authentication to tracking to medical care to entertainment and epayment. Among NFC's many advantages is that it uses short-range communication, which enhances security while also making it simple to use. Our Chrome browser extension allows users to easily authenticate and manage their personal information from their mobile devices. Owing to browser security restrictions, JavaScript code (used in the Google Chrome extension) cannot access computer system resources when executing in a browser. As a result, the Java applet is used to run the software on the user's computer. With the help of the extension, NFC Reader may be accessed and used, and a connection between Java and JavaScript can be established. Using NFC, the user does not have to input any account information because it is automatically uploaded to the online login page. IndexTerms: NFC, Login, Mobile apps , website, RFID , Authentication, Sign-in

A Dynamic Four-Step Data Security Model for Data in Cloud Computing Based on Cryptography and Steganography.

Cloud computing is a rapidly expanding field. It allows users to access computer system resources as needed, particularly data storage and computational power, without managing them directly. This paper aims to create a data security model based on cryptography and steganography for data in cloud computing that seeks to reduce existing security and privacy concerns, such as data loss, data manipulation, and data theft. To identify the problem and determine its core cause, we studied various literature on existing cloud computing security models. This study utilizes design science research methodology. The design science research approach includes problem identification, requirements elicitation, artifact design and development, demonstration, and assessment. Design thinking and the Python programming language are used to build the artifact, and discussion about its working is represented using histograms, tables, and algorithms. This paper’s output is a four-step data security model based on Rivest–Shamir–Adleman, Advanced Encryption Standard, and identity-based encryption algorithms alongside Least Significant Bit steganography. The four steps are data protection and security through encryption algorithms, steganography, data backup and recovery, and data sharing. This proposed approach ensures more cloud data redundancy, flexibility, efficiency, and security by protecting data confidentiality, privacy, and integrity from attackers.

Techniques of migration in live virtual machine and its challenges

Cloud computing is the on-demand availability of computer system resources. Most technology industries are moving to the cloud. Cloud structures can be costly for users. Virtualization is used in cloud computing that helps the cloud at a low cost. Migrating virtual machines (VMs) helps to manage computation. Migration of virtual machines is a core feature of virtualization. The technique of migrating a running virtual machine from one physical host to another with minimal downtime is called "live virtual machine migration." This paper discusses the migration technique, i.e., migration before and after copying, and also issues related to live migration. This paper presents a better approach to the VM migration method and future challenges by differentiating from the previous live VM migration method.

Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.

Optical Disk : Review and Analysis.(Dept.E)

The operating system is a collection of programes (algorithms) designed to manage all the comp uter system resources, such as processors, main storage and secondary storage, and input/output devices. Optical and magnetic disks are the most common secondary storage devices. The work described in this paper is mainly directed to the optical disk. Different optical disk storage technologies are discussed in terms of information about physical disk storage, file systems and access methods. Comparisons are also made between magnetic and optical disk storages.

MODEL OF SEMANTIC INTEGRATION OF INFORMATION SYSTEMS PROPERTIES IN RELAY DATABASE REENGINEERING PROBLEMS

The subject of research is methods of semantic integration of subject areas of heterogeneous information systems and distributed databases. This class of systems are created on the basis of database technologies, are widespread and used in all areas of economic activity. Such systems are characterized by high complexity of design, maintenance and modification. The purpose of the research is the development of a subject area model based on the semantic properties and relationships of data elements; development of effective technology of integration of information resources of heterogeneous computer systems on the basis of technology of management of database systems; research and formalization of classes of inhomogeneity of data structures aimed at solving the problem of determining the types of information objects. Development of tools for the design and maintenance of application problems for the integration of heterogeneous information systems and distributed databases. Results: the analysis of existing methods and models of integration of subject areas on the basis of semantic properties and connections of data elements is carried out; developed an effective mathematical model, technology and algorithm for semantic integration of information resources of heterogeneous computer systems for relational databases; investigated and formalized classes of inhomogeneity of data structures aimed at solving the problem of determining the types of information objects; the model is developed and means of the logical description of properties of information objects for definition of border of the considered subject area are investigated. Conclusion: the article considers a formalized infographic model of the subject area, which focuses on the semantic relationships between information objects of databases. An axiomatic approach to the description of the subject area is formulated, which allows to consider the problem of modeling the relations of the elements of the subject area in the form of a set of rules that determine the existence of data elements. On the basis of the analysis of structures and models of databases of information systems the general approach to construction of the universal technology focused on the decision of problems of management of heterogeneous information resources of computer systems is defined.

Automated SSH Key Management Using Custom Certificate Authority and Temporary Keys

In this era of massive knowledge, cloud computing has emerged as one of the most on-demand accessibility of computing system resources, a plan that modified the ways of computing. However, data security remains a tangle in creating cloud computing vulnerable. Therefore, SSH users and businesses victimization SSH coding should do everything necessary to shield the safety of their SSH encryption keys and different components so as to uphold the trust placed within the system. SSH keys automate the manner of stable get admission to servers, bypassing the want to manually input log-in credentials. SSH is likewise immune to brute pressure assaults and protects against sure assault vectors getting used to advantage get admission to far off machines. The current research work carries out a detailed functioning of a publicly available, fully managed and automated solution for secure management and distribution of SSH keys with a granular level access control which will prevent any potential leak of a private key.

Resource management in client-server computing systems based on trust assessment

The article describes a trust assessment model and a method based on it for managing client access to scarce resources in a client-server computing system. Trust in a client is the degree to which it meets the standard. The trust assessment score is based on an ordinal preference scale with a limited normalized weight of criteria. The model allows you to get a numerical assessment of the client’s compliance with a given set of criteria. The method of managing computer system resources is based on a controlled impact on the communication channel between the client and the server of the system. This method describes how to select a threshold value for the trust assessment score. The method allows you to reasonably limit the total number of clients in the system and thereby increase the probability of access to scarce computing resources of the server for trusted clients.

Research on the Way of Obtaining Electronic Physical Evidence Based on Python—Take Windows Log Document as an Example

With the popularization of computer technology such as smart phone, cloud computing and so on, high-tech crime is becoming more and more common. However, the number of Internet users in China is large, the number of cybercrime cases is large, the electronic data that need to be collected is many and complex, and the research of electronic data forensics in China is mainly based on technical framework and model establishment, and the hardware development is mainly introduced and cooperated. So that China’s electronic data forensics technology cannot meet the needs. Electronic data evidence and related legal issues, network forensics, mobile intelligent terminal forensics analysis technology, malicious code forensics analysis, cross-platform forensics, data recovery, intelligent association analysis, data depth mining, password cracking and other technical aspects need to be continuously combined. Accordingly, this project aims to carry on the analysis and the application research to the electronic data forensics technology of the network crime case, take the Windows log file as an example, through the reading and analysis of the user system log, we can understand the details of the user computer operating system, the behavior of the application program, the behavior of the user itself and the abnormal events in the system. Then it rebuilds the computer operation scene, monitors the computer system resources, audits the user’s related behavior, carries on the alarm to the suspicious behavior, looks up and determines the scope of the intrusion behavior and so on, provides the evidence source and the key clue for the fight against the computer network crime.