Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

Performance Comparison of Hyper-V and KVM for Cryptographic Tasks in Cloud Computing

As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Enhancing Cybersecurity through Cloud Computing Solutions in the United States

This study investigates how cybersecurity can be enhanced through cloud computing solutions in the United States. The motive for this study is due to the rampant loss of data, breaches, and unauthorized access of internet criminals in the United States. The study adopted a survey research design, collecting data from 890 cloud professionals with relevant knowledge of cybersecurity and cloud computing. A machine learning approach was adopted, specifically a random forest classifier, an ensemble, and a decision tree model. Out of the features in the data, ten important features were selected using random forest feature importance, which helps to achieve the objective of the study. The study’s purpose is to enable organizations to develop suitable techniques to prevent cybercrime using random forest predictions as they relate to cloud services in the United States. The effectiveness of the models used is evaluated by utilizing validation matrices that include recall values, accuracy, and precision, in addition to F1 scores and confusion matrices. Based on evaluation scores (accuracy, precision, recall, and F1 scores) of 81.9%, 82.6%, and 82.1%, the results demonstrated the effectiveness of the random forest model. It showed the importance of machine learning algorithms in preventing cybercrime and boosting security in the cloud environment. It recommends that other machine learning models be adopted to see how to improve cybersecurity through cloud computing.

Secure and Efficient Outsourced Computation in Cloud Computing Environments

Secure and efficient outsourced computation in cloud computing environments is crucial for ensuring data confidentiality, integrity, and resource optimization. In this research, we propose novel algorithms and methodologies to address these challenges. Through a series of experiments, we evaluate the performance, security, and efficiency of the proposed algorithms in real-world cloud environments. Our results demonstrate the effectiveness of homomorphic encryption-based secure computation, secure multiparty computation, and trusted execution environment-based approaches in mitigating security threats while ensuring efficient resource utilization. Specifically, our homomorphic encryption-based algorithm exhibits encryption times ranging from 20 to 1000 milliseconds and decryption times ranging from 25 to 1250 milliseconds for payload sizes varying from 100 KB to 5000 KB. Furthermore, our comparative analysis against state-of-the-art solutions reveals the strengths of our proposed algorithms in terms of security guarantees, encryption overhead, and communication latency.

Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.

Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

Security Implications of Edge Computing in Cloud Networks

Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.

Delayed-measurement one-way quantum computing on cloud quantum computer

One-way quantum computation focuses on initially generating an entangled cluster state followed by a sequence of measurements with classical communication of their individual outcomes.Recently,a delayed-measurement approach has been applied to replace classical communication of individual measurement outcomes.In this work,by considering the delayed-measurement approach,we demonstrate a modified one-way CNOT gate using the on-cloud superconducting quantum computing platform:Quafu.The modified protocol for one-way quantum computing requires only three qubits rather than the four used in the standard protocol.Since this modified cluster state decreases the number of physical qubits required to implement one-way computation,both the scalability and complexity of the computing process are improved.Compared to previous work,this modified one-way CNOT gate is superior to the standard one in both fidelity and resource requirements.We have also numerically compared the behavior of standard and modified methods in large-scale one-way quantum computing.Our results suggest that in a noisy intermediate-scale quantum(NISQ)era,the modified method shows a significant advantage for one-way quantum computation.

A Novel Scheduling Framework for Multi-Programming Quantum Computing in Cloud Environment

As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.

A Study on Technology Application and Performance of Small and Medium-sized Enterprises in the Context of Cloud Computing Application-A Case Study of Hotel Industry in Henan,China

This paper examines how the adoption of cloud computing affects the relationship between the technical and environmental capabilities of small and medium-sized enterprises(SMEs)in the tourism industry in Henan Province,China,thereby promoting the stable and sustainable development of the tourism industry,combining the laws of tourism market development,vigorously constructing a smart tourism project,guiding tourism cloud service providers to strengthen the cooperation and contact with the market’s tourism enterprises,introducing and utilizing cloud computing technology,optimizing and improving the functions of various tourism services of the enterprises,and enhancing the processing and analysis of enterprise-related data to provide tourism information.Strengthen the processing and analysis of enterprise-related data to provide tourism information,and further study the adoption of cloud computing and its impact on small and medium-sized enterprises(SMEs)in terms of technology and business environment knowledge,so as to make the best enterprise management decisions and realize the overall enhancement of the enterprise’s tourism brand value.

QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

Multiframe-integrated, in-sensor computing using persistent photoconductivity

The utilization of processing capabilities within the detector holds significant promise in addressing energy consumption and latency challenges. Especially in the context of dynamic motion recognition tasks, where substantial data transfers are necessitated by the generation of extensive information and the need for frame-by-frame analysis. Herein, we present a novel approach for dynamic motion recognition, leveraging a spatial-temporal in-sensor computing system rooted in multiframe integration by employing photodetector. Our approach introduced a retinomorphic MoS_(2) photodetector device for motion detection and analysis. The device enables the generation of informative final states, nonlinearly embedding both past and present frames. Subsequent multiply-accumulate (MAC) calculations are efficiently performed as the classifier. When evaluating our devices for target detection and direction classification, we achieved an impressive recognition accuracy of 93.5%. By eliminating the need for frame-by-frame analysis, our system not only achieves high precision but also facilitates energy-efficient in-sensor computing.

Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

ATSSC:An Attack Tolerant System in Serverless Computing

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.