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In monitoring transmission line external damage prevention, due to the limited memory computing power of the equipment, the image needs to be transmitted to the data center at regular intervals, resulting in a high false negative rate. Therefore, this paper proposes a target detection method based on lightweight YOLOv5s. First, DSConv and improved E‐ELAN are used in Backbone to reduce the model's parameters. Then, GSConv and VoV‐GSCSP are introduced in Neck to reduce the complexity of the model. Finally, the Mish activation function achieves more effective feature transfer. According to the experimental findings, the proposed model's parameters are about 37% smaller than the original model's, and the calculation amount is about 53% smaller. The detection accuracy on the self‐built data set is the same, which proves that the proposed algorithm can reduce the model while maintaining high detection performance. It has specific practical significance for the terminal real‐time detection of external mechanical damage targets. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of sustainable and eco-friendly cloud computing systems. To enhance the resource allocation process, we introduce a novel knowledge-based optimization algorithm. In this study, we rigorously evaluate its efficacy by comparing it to existing algorithms, including the Flower Pollination Algorithm (FPA), Spark Lion Whale Optimization (SLWO), and Firefly Algorithm. Our findings reveal that our proposed algorithm, Knowledge Based Flower Pollination Algorithm (KBFPA), consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction. This paper underscores the profound significance of resource allocation in the realm of cloud computing. By addressing the critical issue of adaptability and energy efficiency, it lays the groundwork for a more sustainable future in cloud computing systems. Our contribution to the field lies in the introduction of a new resource allocation strategy, offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures. [ABSTRACT FROM AUTHOR]

One of the major challenges in managing 5G networks is the reconfiguration of network slices. The task covers in particular reconfiguration and relocation of virtual network functions (VNFs) so as to match the service requirements of the slices and the availability of resources of the data centers. In this article, we study in deep the problem of optimal VNFs reconfiguration analyzing a number of its variants. We define two exact, compact integer linear programming formulations of the VNFs reconfiguration problem, and derive two approximate solution algorithms, one of them based on the column generation method. Numerical tests and comparisons illustrate the performance of the algorithms and the quality of the provided solutions. [ABSTRACT FROM AUTHOR]

The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions. [ABSTRACT FROM AUTHOR]

With due span of time, Cloud computing has grown phenomenally as it is a best approach for using the scalable shared pool of resources at economical cost. Though it seems easy but distribution of the jobs assigned to various Virtual Machines (VMs) is a cumbersome task for the Data Centre (DC) Controller. Various techniques have been proposed from time-to-time for balancing the load on distinct VMs available in the DCs. The primarily used load balancing techniques are Round Robin, Equal Spread Current Execution and Throttled. The major thrust for using the aforementioned techniques is to improve the performance parameters such as Overall Response Time (ORT), Data Center Processing Time (DCPT) and Cost. In this manuscript, a new algorithm named Pragmatic Load Balancing (PLB) has been designed and developed for optimization of afore-said parameters and various simulation scenarios have also been created for the evaluation of these parameters. In the given Scenarios created by using Cloud Analyst Simulator, different number of VMs like 20, 40, 60, 80 and 100 have been allotted with distinct number of DCs from 1 to 5 to the application at each Cloud Configuration and the divulged that the value of the performance parameters have been improved by using the PLB Algorithm in comparison to other available Load Balancing Algorithms. [ABSTRACT FROM AUTHOR]

Mobile cloud computing is a very attractive service paradigm that outsources users' data computing and storage from mobile devices to cloud data centers. To protect data privacy, users often encrypt their data to ensure data sharing securely before data outsourcing. However, the bilinear and power operations involved in the encryption and decryption computation make it impossible for mobile devices with weak computational power and network transmission capability to correctly obtain decryption results. To this end, this paper proposes an outsourcing decryption algorithm of verifiable transformed ciphertext. First, the algorithm uses the key blinding technique to divide the user's private key into two parts, i.e., the authorization key and the decryption secret key. Then, the cloud data center performs the outsourcing decryption operation of the encrypted data to achieve partial decryption of the encrypted data after obtaining the authorization key and the user's outsourced decryption request. The verifiable random function is used to prevent the semi-trusted cloud data center from not performing the outsourcing decryption operation as required so that the verifiability of the outsourcing decryption is satisfied. Finally, the algorithm uses the authorization period to control the final decryption of the authorized user. Theoretical and experimental analyses show that the proposed algorithm reduces the computational overhead of ciphertext decryption while ensuring the verifiability of outsourcing decryption. [ABSTRACT FROM AUTHOR]

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss. [ABSTRACT FROM AUTHOR]

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by H S D C m (m) , are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an H S D C m (m) within three test rounds, and at most N + 4 m (m − 2) tests with m ≥ 3 (resp. at most nine tests with m = 2 ), where N = m · 2 m is the total number of nodes in H S D C m (m) . Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests. [ABSTRACT FROM AUTHOR]

Numerous data centers have adopted heterogeneous computing server to accelerate the processing speed with various applications. However, as the growth of efficiency, the issue of high power consumption is increasingly becoming crucial. Traditional researchers focus on the developing strategies for power efficiency that neglects the importance of power prediction that substantially impacts on energy planning on data center. Different to the previous work, this paper utilizes the advantage of Artificial Neural Network (ANN) to design and implement an improved algorithm of power consumption prediction on heterogeneous computing server with various types of jobs. First, the impact of multi-type of jobs has been taken into account, i.e., CPU-intensive, memory-intensive, I/O-intensive and GPU-intensive. We collect the data trace from these jobs by a set of benchmarks. Then, based on the collected data trace, power prediction algorithm has been established, so that developing an improved ANN-based power consumption prediction algorithm with CNN-BiLSTM neural network and attention mechanism. Specifically, one dimensional CNN is utilized to perform local feature extraction and dimension reduction on the input, the BiLSTM network is adopted to extract high-level features from the above results. The miscellaneous features extracted are filtered out by using attention mechanism. Last, the experiment has been conducted, and the peculiarity for four types of jobs has been discussed. Compared with the latest proposed ANN-based prediction power algorithms, our proposed algorithm has better performance in prediction accuracy on power consumption. [ABSTRACT FROM AUTHOR]

This article analyses how algorithmic innovation in contemporary warfare unfolds through new alliances and contestations among civil and military actors in the face of an overarching rhetoric around the need to lead in "information manoeuvre". Drawing on assemblage thinking and applying it to the case of the Land Information Manoeuvre Centre (LIMC)—a data centre founded by the Dutch Army that unlawfully tracked and algorithmically predicted its citizen's sentiment and behaviour during the COVID-19 pandemic—the authors identify three logics that held this centre together and helped ward off critique: entrepreneurialism, informality, and experimentation. Emulating innovation practices elsewhere, together, these logics have important political repercussions beyond the Dutch case, pushing the expansion of military surveillance, pattern-finding and targeting, while undermining the rule of law and democratic accountability within algorithmic warfare. [ABSTRACT FROM AUTHOR]

The energy-efficient and secure allocation of virtual machines (VMs) plays an important role at the data center. As cloud computing continues to expand rapidly and the number of cloud users increases day by day, the issue of high energy consumption in complex cloud data centers has become a significant concern. To address this challenge, the consolidation of virtual machines (VMs) emerges as a crucial strategy for optimizing cloud resources efficiently. In this study, a novel security evaluation method is proposed to assist the model available for the virtualized system. A multi-objective model-based firefly algorithm (FA) and harmony search (HS) algorithm are used for the system configuration in VM migration is proposed to measure the security threats such as denial of service (DoS), distributed denial of service (DDoS) and Man-in-the-middle attack. The proposed method also decreases the power consumption, network usage and resource wastage in virtual machines. The proposed algorithm achieves better results compared to other existing methods by utilizing the number of virtual machine blocks with cost migration. The experimental results shows that the proposed FA+HS delivers the performance metrics such as makespan, execution cost and resource utilization and achieved at the 1000VMS of 950, 0.001 and 62 respectively, which ensures the better results compared with the existing methods such as whale optimization genetic algorithm (WOGA), multi-objective whale optimization algorithm-based differential evolution (M-WODE) and joint task scheduling and virtual machine placement (JTSVMP). [ABSTRACT FROM AUTHOR]

Summary: Task scheduling is a critical problem in the cloud computing environment since it has a significant impact on the system's performance. Security and robust based task allocation model improves the security of real‐time solicitations whereas preserving a high‐level performance of the data center. Here, to minimize the makespan in task allocation existing research proposed a bio‐inspired algorithm in a multi‐cloud environment, but, it needs more memory to update velocity and increases the processing time. Therefore, this research proposed a Machine Learning based Secure and Efficient Task Allocation in Multi‐Cloud Environment. First, the proposed model has a task classifier module that categorizes the matching it to the appropriate VM type for the task that was submitted. The next is the task allocation module, in which, an Intellectual Gravitational Search‐Genetic Algorithm is suggested to allocate the task to a VM. The tasks are distributed to the VMs in the order specified after the allocating phase in which they will be executed. Finally, to secure the task allocation in multi‐cloud, this research proposed a Naïve Bayes module to detect the attacks. The models and experimental findings demonstrate that our proposed approach produces a shorter makespan, lower cost, improved resource utilization, and better trade‐off between time and economic cost. It is more dependable and effective in multi‐cloud environment. [ABSTRACT FROM AUTHOR]

The emergence of new applications is driving a dramatic growth in the capacity of data center interconnects. Intensity modulation and direct detection (IM/DD) has the characteristics of low cost, low power consumption and a small footprint. Industry and academia have conducted much research on IM/DD systems as a cost-effective solution. However, optical/electronic bandwidth and fiber dispersion are the restricting factors for the improvement of transmission capacity. Pattern-dependent distortion is an important aspect that affects system performance. In this paper, we propose a look-up table (LUT)-based direct-detection-faster-than-Nyquist (DDFTN) algorithm to compensate for pattern-dependent distortion. The performances of feedforward-equalization (FFE) only, the original DDFTN, least-squares (LS)-based DDFTN, and LUT-based DDFTN algorithms in IM/DD-based 112/140 Gbit/s four-level pulse-amplitude modulation (PAM-4) signal transmission were evaluated. The experimental results indicate that LUT-based DDFTN performs better with low computational complexity. [ABSTRACT FROM AUTHOR]

The cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service. [ABSTRACT FROM AUTHOR]

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%. [ABSTRACT FROM AUTHOR]

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing's limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies. [ABSTRACT FROM AUTHOR]

The evolution of cloud computing results in the development of numerous huge data-centers. These data-centers use large quantities of electrical energy, which in turn results in high operating cost and high emissions of CO 2 . The grounds for this excessive usage of energy is the inefficient usage of data-center resources (hosts). The resources in the data-center can be in either peak or idle or sleep state. It has been experimentally proven that hosts in the idle state consumes more energy than hosts in sleep state. Through an efficient and optimal VM placement strategy, an energy efficient resource utilization can be achieved, thereby reducing the number of idle hosts in the data-center. This paper introduces two energy efficient VM placement algorithms based on bin packing heuristics considering the physical machine's energy efficiency, Energy Efficient VM Placement (EEVMP) and Modified Energy Efficient VM Placement (MEEVMP), that can reduce the overall energy usage in the data-center. The EEVMP algorithm when compared with the default VM placement algorithm Power-Aware Best-Fit Decreasing algorithm (PABFD) of CloudSim, it reduces the energy consumption by 53%, average SLA violation by 3.5% and number of VM migrations by 64.47%. Further, we have performed MEEVMP algorithm where we achieve a reduction in energy consumption by 54.24%, average SLA violation by 4.39% and number of VM migrations by 67.713 % as compared to PABFD. [ABSTRACT FROM AUTHOR]

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. [ABSTRACT FROM AUTHOR]

In order to effectively ensure the retrieval effect of enterprise data center resources, improve the retrieval accuracy of enterprise data center resources, and shorten the retrieval time of enterprise data center resources, a retrieval technology of enterprise data center resources based on density peak clustering algorithm is proposed. Analytical clustering algorithms, density clustering algorithms, and density peak clustering algorithms are all types of clustering algorithms. To reduce the dimensionality of enterprise data center resources, the kernel principal component analysis method is used. The structure of the enterprise data center resource set is reorganized and the feature quantity of the enterprise data center resource distribution is extracted using feature space reorganization technology. On this basis, the density peak clustering is carried out on the data center resource set of enterprise, and the semantic association distribution model of data center resource retrieval in enterprise is constructed. Through the semantic registration and weighted vector combination control method, the retrieval of enterprise data center resources is realized. The experimental results show that the proposed algorithm has a good effect on the retrieval of enterprise data center resources, which can effectively improve the resource retrieval accuracy and shorten the resource retrieval time. [ABSTRACT FROM AUTHOR]

The purpose of designing embedded Internet of things and studying the ad hoc technology of gateway is to meet the monitoring requirements brought by the continuous expansion of data center. Among them, in the aspect of dynamic data monitoring, this paper designs a dynamic acquisition algorithm, that is, by analyzing the characteristics of data center resources and the number of information acquisition methods, so as to adjust the data acquisition cycle and to reduce the monitoring workload and improve the monitoring flexibility. At the same time, this paper also studies the automatic psychological evaluation system of students. It is found that with the intensification and efficient operation of social competition, some special groups of college students are facing increasing pressure, and events caused by psychological problems emerge one after another. Even though there are many psychological evaluation systems on the network, a large number of psychological knowledge and mental health management on the Internet cannot effectively manage and improve the psychological status of college students. Therefore, the university needs to build a psychological evaluation system for college students in time and combine the automatic psychological evaluation system for college students with embedded Internet of things and cloud service technology to improve the accuracy of psychological evaluation. The application of the system, college students can independently evaluate their own mental state, if there are psychological problems can be timely counseling, not only can improve the level of mental health education in colleges and universities, but also can promote the development of physical and mental health. [ABSTRACT FROM AUTHOR]

Cloud Computing is playing a huge role in future technology. Further, with the explosive growth of the Internet and cloud computing, several service providers, such as Amazon, Microsoft, IBM, and Google, have expanded their data centers and rapidly deployed data centers in different places around the world to deliver various cloud computing services. However, several challenges are raised with the wide spread use of cloud environment such as power consumption, load balance, reliability, scalability, and security. This paper tackles the power consumption problem and presents an efficient algorithm, called Task Consolidation based Power Minimization (TCPM), to efficiently schedule tasks onto available resources of the cloud environment so as to minimize power consumption. In proposed TCPM algorithm, several benefits of the existing algorithms are enhanced and incorporated into the TCPM algorithm, where the best-fit procedure is used to achieve the best possible resource utilization and avoid wasting energy. The results of the proposed TCPM algorithm are compared with other recent algorithms such as FCFS, WWO, and MCT algorithms using the CloudSim toolkit. [ABSTRACT FROM AUTHOR]

As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research. [ABSTRACT FROM AUTHOR]

Resource management is an important issue in the cloud computing paradigm. The maximum Utilization of resources leads the service providers to get maximum profit. The suitable resources in the cloud data centre are needed to satisfy the user needs. The selection of the right resource is very essential in the data centre. Metaheuristic algorithms are used to perform the task allocation in the cloud computing environment. In this paper, a novel task allocation algorithm is proposed using Cat Swarm Optimization and BAT algorithm. BAT algorithm helps CSO algorithm to escape from a pre convergence issue. The proposed HGCSBAT algorithm results are evaluated and compared with the popular algorithms CSO and BAT algorithms. The HGCSBAT results are better than Genetic algorithm, Cat Swarm Optimization, and BAT algorithms in terms of availability and throughput. [ABSTRACT FROM AUTHOR]

Cloud computing is a distributed environment for providing services over the Internet. Load balancing of computing resources has emerged as a crucial element in this industry as a result of the expanding use of cloud computing and the expectations of customers to receive more services and better outcomes. The workload and system behavior of cloud computing are quite dynamic. And this can cause the resources in the data center to be overloaded. Ultimately, a load imbalance in some data center resources could result in increased energy use, decreased performance, and resource waste. Response time, expense, throughput, performance, and resource usage are among the quality of service indicators that load balancing can enhance. In this article, we analyze and evaluate scheduling and resource allocation methods with a view to load balancing, review the most recent approaches, and give a classification of these algorithms. Also, several significant problems and difficulties with cloud load balancing will be discussed in an upcoming study to create new algorithms. [ABSTRACT FROM AUTHOR]

Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers stringmatching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this study is to use a string-matching algorithm (i.e., Boyer Moore) to compare the contents of incoming workloads to those of documents that have already been processed in a data center. A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy. On the contrary, if no match can be discovered, the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices, the number of active servers, on-site green energy, and traces of incoming workload. The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques. [ABSTRACT FROM AUTHOR]

In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM. [ABSTRACT FROM AUTHOR]

Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs. [ABSTRACT FROM AUTHOR]

We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China's hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm's stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China's rice harvesting operations. [ABSTRACT FROM AUTHOR]

Multi-center heterogeneous data are a hot topic in federated learning. The data of clients and centers do not follow a normal distribution, posing significant challenges to learning. Based on the assumption that the client data have a multivariate skewed normal distribution, we improve the DP-Fed-mv-PPCA model. We use a Bayesian framework to construct prior distributions of local parameters and use expectation maximization and pseudo-Newton algorithms to obtain robust parameter estimates. Then, the clipping algorithm and differential privacy algorithm are used to solve the problem in which the model parameters do not have a display solution and achieve privacy guarantee. Furthermore, we verified the effectiveness of our model using synthetic and actual data from the Internet of vehicles. [ABSTRACT FROM AUTHOR]

The emerging technologies such as high-performance computing, cloud computing and large data centre networks demand very high injection rate, high scalability and very low switching latency (SL) that can manage high network traffic, and buffering and also be cost-effective, so that they can be a viable solution for future manufacturing industry. To overcome the biggest challenge of optical buffering and big quantity of data switching, the Data Vortex (DV) all optical switch innovations and its various enhancements such as Augmented Data Vortex (ADV) seem to be the possible solution and can bring the revolution in the industrial world. In this paper, a new generalized algorithm has been used to simulate the equivalent planar virtual model of 3-D ADV and is named as equivalent planar algorithm (EPA). The algorithm is more versatile and provides more flexible design for multiple level networks. To verify the proposed EPA of ADV (or EPA ADV), the simulations have been performed for various input traffic loads and switch size keeping active angle 'A' constant. Also the ADV switch is not studied under asymmetric mode (AM) of operation till now which has been done here. Lastly the paper also provides the performance comparison study of original DV and EPA ADV under symmetric and asymmetric I/O mode. The results obtained from simulation have shown that under modest degree of asymmetric I/O operation of the switch EPA ADV shows performance improvement in injection rate (throughput) and average switch latency (SL) under different traffic pattern as compared to symmetric mode (SM) of operation in ADV. [ABSTRACT FROM AUTHOR]

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment. • We propose a novel multi-objective cloud federation algorithm considering to minimize three objectives. • We have identified a new problem in VM migration called Migration lock-in status. • We employ new heuristics to make a tradeoff between servers' power consumption and their resource utilization simultaneously. • We conduct a comprehensive simulation under an IaaS cloud infrastructure using a real-world datasets. [ABSTRACT FROM AUTHOR]

Cloud Computing is a widely adopted computing model that offloads the in-house processing workloads to remote servers. In recent years, the adoption of cloud computing and related models have increased multifold. The cloud data center consumes an enormous amount of electricity and becomes a major issue for emitting greenhouse gases. The most important power conservation strategy used in IaaS cloud is scheduling the virtual machine appropriately into the physical servers to minimize the number active servers. As the number of active servers decreases, the power consumption of a data center will also decrease. The fundamental aim of the proposed work is to schedule the virtual machine as dense as possible in a minimal number of servers using the proposed modified discrete firefly algorithm for power consumption. The proposed algorithm will effectively explore the large search space to find a placement that uses minimal power consumption in the data centers. The proposed algorithm is executed to place virtual machines of various configurations in IaaS cloud and the results are compared with Genetic Algorithm and Particle Swarm Optimization shows its superiority. [ABSTRACT FROM AUTHOR]

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value. [ABSTRACT FROM AUTHOR]

How to improve resource utilization of cloud data centers (CDCs) and ensure users' quality of service (QoS) through efficient virtual machine (VM) scheduling is an urgent problem. Especially when service reliability is taken into consideration, the problem becomes more challenging. However, existing related researches mostly ignore the influence of reliability factors, such as failures and recoveries of computing nodes (CNs), which cannot reflect the realistic situations of real-life CDCs. Therefore, this paper investigates the problem of fault tolerance-aware VM scheduling and formulates it as a multi-objective optimization model with multiple QoS constraints. The proposed model tries to minimize users' total expenditure and, at the same time, maximize the successful execution rate of their businesses. To solve the proposed optimization model, a greedy-based best fit decreasing (GBFD) algorithm is then developed. The GBFD algorithm adopts a cost efficiency factor whose definition is according to the characteristics of CNs, to select a suitable CN for each VM request. Finally, extensive experiments are conducted to verify the feasibility of the proposed models and algorithm based on both the real-world CDC cluster data sets and the simulation ones. The results show that, first, as expected, fault tolerance significantly influences the performance criteria of VM scheduling and second, in most cases, the developed algorithm can decrease users' expenditure, increase success rate for executing their business and improve their overall satisfactions. Specifically, under real-world CDC cluster scenario, GBFD algorithm can increase the overall satisfaction of all cloud users by 38.3%, 20.9% and 14.6%, respectively, compared with the other three ones. Thus, the developed algorithm can perform better under fault tolerance-aware cloud environments. [ABSTRACT FROM AUTHOR]

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment. [ABSTRACT FROM AUTHOR]

Recently, cloud computing infrastructure (CCI) models have received much attention for their exceptional scalability, dependability, Data Information Sharing (DIS), and low cost rate. There are many hardware and software elements that are accessed over the internet by cloud data centers. Modern data centers utilize Virtualization Techniques (VT) to offer a dispersed CI that employs Virtual Machines (VM) based on Physical Hosts (PH). With the increasing number of centers, optimizing energy consumption has become vital to saving costs due to DCC's high energy consumption. In our CPS algorithm, we combine the Cuckoo algorithm and the particle swarm optimization (PSO). It is determined which virtual machine can be assigned to each host, thus choosing the best virtual machine. As a result, if the selected host is overloaded, it is determined which virtual machines are generating high loads and migrated to another host, which is determined based on the cuckoo algorithm and PSO. In testing each algorithm separately, the combination method proved to consume less energy and execute faster than the other methods in the CloudSim simulation environment. Fault tolerance for our network and evaluation of VMs have also been emphasized in vSphereTM. [ABSTRACT FROM AUTHOR]

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

In the private cloud data center, security participated a fundamental position amid the storage of a voluminous amount of information that is intended to share among various nodes. On the other hand, the challenges in moving object detection and movement-based sub-sequences are significant segments of numerous PC apparition functions that incorporate acknowledgment of objects, assessment of interchange, and manufacturing mechanization. In this paper, we propose to actualize hearty moving object detection and following calculation that can recognize quick-paced moving objects in an assortment of testing constant quick-moving applications like traffic reconnaissance, etc. For the detection of moving objects, we utilize a Gaussian Mixture Design Background Subtraction Methodology. To remove noise, morphological processes are concerned with the resultant forefront pretence. Kalman Filter is utilized for movement-based monitoring and the detected object functions carry out movement segmentation using a foreground detector. Ultimately, blob analysis recognizes clusters of associated picture elements that are known to be moving objects, and the values are stored in a private cloud data center. [ABSTRACT FROM AUTHOR]

Connectivity in large-scale data center networks is a critical indicator to evaluate network state. A feasible and performance-guaranteed algorithm enables us to find disjoint paths between network vertices to ensure effective data transfer and to maintain the normal operation of network in case of faulty nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first propose a vertex disjoint path algorithm with the time complexity of O (n k) in BCCC, where n denotes a switch connected to n servers and k denotes dimension. Then, we prove that the restricted connectivity of B C C C (n , k) . Finally, we present an O (k n κ 1 (G)) fault-free algorithm in BCCC, where κ 1 (G) is the restricted connectivity. This algorithm can obtain a fault-free path between any two distinct fault-free vertices under the condition that each vertex has at least one fault-free neighbor in the BCCC and a set of faulty vertices F with | F | < κ 1 (G) . [ABSTRACT FROM AUTHOR]

The evaluation of the fault diagnosis capability of a data center network (DCN) is important research in measuring network reliability. The g-extra diagnosability is defined under the condition that every component except the fault vertex set contains at least g+1 vertices. The t/k diagnosis strategy is that the number of fault nodes does not exceed t, and all fault nodes can be isolated into a set containing up to k fault-free nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first determine that the g-extra connectivity of B C n , k for 0 ≤ g ≤ n − 1 . Based on this, we establish the g-extra conditional diagnosability of B C n , k under the MM* model for 1 ≤ g ≤ n − 1 . Next, based on the conclusion of the largest connected component in g-extra connectivity, we prove that the t/k-diagnosability of B C n , k under the MM* model for 1 ≤ k ≤ n − 1 . Finally, we present a t/k diagnosis algorithm on BCCC under the MM* model. The algorithm can correctly identify all nodes at most k nodes undiagnosed. So far, t/k-diagnosability and diagnosis algorithms for most networks in the MM* model have not been studied. [ABSTRACT FROM AUTHOR]