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The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

Today, cloud computing technology has attracted the attention of many researchers. According to the needs of users to quickly execute requests and provide quality services, optimal allocation of resources and timing of task execution between virtual machines in cloud computing are of great importance. One of the important challenges that cloud service providers face is the effective management of resources by physical infrastructure. Therefore, in this paper, an autonomous system based on the Clipped Double Deep Q-Learning (CDDQL) Algorithm and the meta-heuristic Particle Swarm Optimization (PSO) for resource allocation is proposed in the Fog-cloud computing infrastructure. The PSO algorithm is used to prioritize the tasks and CDDQL is used as the core of the autonomous system (Auto-CDDQL) to allocate the desired VM resources to the tasks. The proposed Auto-CDDQL is implemented in the Fog and performs this process autonomously. By evaluating the results, it was observed that the amount of Make Span, response time, task completion, resource utilization, and energy consumption rate in the proposed AutoCDDQL on the c-hilo dataset, compared to the FCFS, RR, and PBTS methods, are significantly improved. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced Marine Predator Algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into Marine Predator Algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ datasets.The experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization. [ABSTRACT FROM AUTHOR]

Due to the users' mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

With the present situation in the world, proper monitoring of health has become one of the important and critical parts of our day-to-day life. With increasing work pressure and improper eating habit in day-to-day life, diabetes has become a common disease among the people. The early prediction of diabetes is a major tool to diagnose diabetes in its early stage. With the development of cloud computing and fog computing, a nature-based algorithm can be used to create a framework which can predict the diabetes in its early phase. The early prediction of disease will allow immediate treatment and hence patient's life can be saved. Cloud computing and fog computing have provided mobility and has facilitated that the patients' data can be collected and is monitored properly by the healthcare professionals at any time. In this paper, a fog-based diabetes prediction model is proposed where the patient data are acquired from the sensors that are present in remote. Fog computing is used for gathering and processing the data at the device end and communicating immediately. The processed data is transferred to the cloud layer for the analysis. In the cloud layer, the proposed hybrid ANFIS-PSO-WOA algorithm is used for the detection of diabetes level. The collected data is stored in fog layer and is used for further analysis by the healthcare professionals. Based on the analysis, the healthcare professional will treat the patient. The nature-based algorithm helps to provide good accuracy. The proposed framework has experimented with diabetes data obtained from the UCI repository and the result obtained with high accuracy of 92% by comparing with other existing algorithms such as SVM, ANN, and ANFIS. The proposed algorithm-based framework can be used to detect diabetes more efficiently and accurately as compared to other nature-based frameworks and will help healthcare professionals to diagnose the disease in its early phase. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth. [ABSTRACT FROM AUTHOR]

Nowadays, heterogeneous cloud resources are charged by cloud providers according to the pay-as-you-go pricing model. To execute workflow applications in clouds under deadline constraints, cloud resources have to be utilized appropriately and judiciously, challenging traditional workflow scheduling algorithms, which are either inapplicable to the cloud environment or fail to fully exploit the features of scheduling problem for cost optimization. In this paper, we propose a heuristic algorithm CSDW and a meta-heuristic algorithm N-WOA to minimize the execution cost of the given workflow subject to the deadline constraint in clouds. CSDW first assigns the sub-deadline to each task based on the modified probabilistic upward rank, and then tasks are sorted and mapped to appropriate instances, finally instance-type upgrading and downgrading method is adopted to further accelerate workflow execution and reduce the total cost, respectively. N-WOA employs whale optimization algorithm for deadline-constrained cost optimization by refining the task ordering step in CSDW. By simulation experiments on scientific workflows with existing algorithms, the results demonstrate the capability of the proposed algorithms in meeting the deadlines and reducing the execution costs, CSDW is highly competitive and N-WOA achieves the best performance in all cases. [ABSTRACT FROM AUTHOR]

Cloud computing is the sharing of remote access resources over the Internet. But with this comes an extensive risk of unauthorized access. Hence, for the security and privacy of the data, intrusion detection system (IDS) is required. IDS has to process a huge number of data with dimensions in search of intrusions. The more data it has to scan through, the more time it takes to detect an intrusion. Thus to reduce the dataset, feature selection (FS) is used where redundant data dimensions are discarded. In this paper, authors have proposed a novel Sage Grouse Mating algorithm which is to be implemented for FS in IDS. The proposed model was tested on NSL-KDD and Kyoto2006+ datasets. The proposed model increases the average accuracy of IDS up to 81.729% and reduces the number of features from 41 to 14 on NSL-KDD dataset. So, the experimental outcomes show that the proposed model enhanced the performance of IDS and outperforms all other metaheuristic algorithms compared in this paper. Therefore, it constitutes a robust IDS for Cloud Environment. [ABSTRACT FROM AUTHOR]

Data centres have seen significant growth recently as a result of the phenomenal rise of cloud computing. These data centres typically use more energy, which significantly raises operational costs. The management of server consolidation involves moving all Virtual Machines (VMs) to idle servers. However, performance suffers as a result of migration as migration volume and time increase. The Cloud computing model generates computational cooperative of huge computing services and systems. Recently, resource sharing, task scheduling and resource management between users are familiar research areas. In this paper, Fractional Improved Whale Social Optimization Algorithm (Fractional IWSOA) is developed for load balancing in the cloud model. The developed Fractional IWSOA is newly devised by incorporating Social Optimization Algorithm (SOA) and Improved Whale Optimization Algorithm (IWOA) along with Fractional Calculus (FC). Moreover, the categorization of VM is performed based on Deep Embedded Clustering (DEC) which is categorized into two types, underloaded VMs and overloaded VMs. Additionally, the tasks in underloaded VM is assigned based on various factors. As a result, the developed Fractional IWSOA performed better than other existing techniques in terms of load, capacity, and resource usage, which were respectively 0.1160, 0.5898, and 0.7168. [ABSTRACT FROM AUTHOR]

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation. [ABSTRACT FROM AUTHOR]

Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customers want to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with Bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master. [ABSTRACT FROM AUTHOR]

Cloud computing provides infinite resources and various services for the execution of variety of applications to end users, but still it has various challenges that need to be addressed. Objective of cloud users is to select the optimal resource that meets the demand of end users in reasonable cost and time, but sometimes users pay more for short time. Most of the proposed state-of-the-art algorithms try to optimize only one parameter at a time. Therefore, a novel compromise solution is needed to make the balance between conflicting objectives. The main goal of this research paper is to design and develop a task processing framework that has the decision-making capability to select the optimal resource at runtime to process the applications (diverse and complex nature) at virtual machines using modified particle swarm optimization (PSO) algorithm within a user-defined deadline. Proposed algorithm gives non-dominance set of optimal solutions and improves various influential parameters (time, cost, throughput, task acceptance ratio) by series of experiments over various synthetic datasets using Cloudsim tool. Computational results show that proposed algorithm well and substantially outperforms the baseline heuristic and meta-heuristic such as PSO, adaptive PSO, artificial bee colony, BAT algorithm, and improved min–min load-balancing algorithm. [ABSTRACT FROM AUTHOR]

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm. [ABSTRACT FROM AUTHOR]

The security of cloud services and underlying resources is a major concern due to vulnerabilities existing in current implementation of the virtualization. Thus, there is a need of detecting system-level attacks like viruses, worms, malware, etc. In this paper, we extend our previous work on vulnerability assessment and patching by integrating in-VM-assisted agent-based malware detection (AMD) framework for securing high-risk virtual machines (VMs) in cloud. The proposed framework has two components, viz. agent at VM and anomaly detection at hypervisor. An agent continuously looks for the new deployment of the executable in-VM and applies the signature-based detection to detect known malware. For detecting unknown attacks, it generates the profile with optimal static features for new executable. The optimal features are derived using an extended binary bat algorithm with two new fitness functions. The profile is transferred to hypervisor where anomaly detection using random forest classifier is applied. It classifies the executable to either normal or malware and generates an alert to VM user. The functionality of the proposed AMD framework is validated over cloud testbed at NIT Goa, as well as with the latest malware datasets. In addition, we analyze the VM security requirements fulfilled by the proposed framework. [ABSTRACT FROM AUTHOR]

As a critical research topic toward the new era of big data, how to develop a high-performance data analytics system has received significant research attention from different disciplines since the 2000s. In the literature, many recent works attempted to develop a high-performance data analytics system to handle the large amount of data (i.e., volume) from different information systems (i.e., variety) that typically will be created very quickly in a short time (i.e., velocity). In particular, several recent studies have shown that metaheuristic algorithms can be applied to many data mining optimization problems to provide a better way to find a high-quality result than traditional deterministic algorithms. A high-performance clustering algorithm for big data analytics system will be presented in this paper. The proposed algorithm is designed based on a new kind of metaheuristic algorithm, coral reef optimization with substrate layers (CRO-SL), to get a better cluster result. To improve the effectiveness and efficiency, the proposed CRO-SL scheme has been applied to a cloud computing platform as well to reduce the response time of a data analytics system. The simulation results show that the proposed algorithm is able to provide a better clustering result than the other clustering algorithms compared in this research, including k-means, genetic k-means algorithm, particle swarm optimization, and simple coral reef optimization algorithm in terms of the sum of squared errors. [ABSTRACT FROM AUTHOR]

Data clustering partitions the information into helpful classes or groups with no earlier learning. This is a fundamental method in the field of computer data mining and it has turned into an essential element in many other engineering areas including cloud computing. This paper purports a novel clustering technique based on the application of krill herd Efficient Stud Krill Herd—Clustering (ESKH-C) technique. It is an optimisation approach for data clustering problem in which a swarm of krill (candidate solutions) moves to converge to specific positions as final cluster centres by minimizing the fitness function. The accuracy of the purposed methodology is blazed on different well familiar bench mark data sets. Analysed with the common clustering methods such as k-means clustering algorithm, data clustering using particle swarm optimization algorithm, ant colony optimization based data clustering, and clustering method using bacterial foraging algorithm, MATLAB simulation results evidence that the proposed technique is an effectual data clustering method. The proposed data clustering method can be employed to manipulate vast data sets with different cluster sizes, multi dimensional and densities. [ABSTRACT FROM AUTHOR]

Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation) [ABSTRACT FROM AUTHOR]

Cloud computing is a technology wherein a network of remote servers is used to process large amount of data in real-time. The servers and data sources may be located in geographically distant regions. Scheduling of workflows is one of the major challenging issues in cloud computing. Workflows are used to express a wide variety of applications including scientific computing and multi-tier web applications. The Workflow scheduling problem is known to be NP-complete. No known traditional scheduling algorithm is able to provide an optimal solution in polynomial time for NP-complete problems. So, researchers rely on heuristics and meta-heuristics to achieve the most efficient solution. In this paper, a workflow scheduling algorithm is proposed to schedule large scientific workflows that are to be executed on IaaS clouds. The workflow scheduling algorithm generates a schedule with the task-to-resource mapping. The metaheuristic Catfish particle swarm optimization (C-PSO) technique is used to select the best schedule with the least makespan and execution cost. The performance of C-PSO is then compared with traditional PSO. The algorithm is simulated on the WorkFlowSim Simulator, an extension of CloudSim simulator. The solution is tested for different types of scientific workflows like Montage, Epigenome, CyberShake and Inspiral. It is observed from the experimental results that C-PSO gives better performance than traditional PSO in terms of execution cost and makespan. [ABSTRACT FROM AUTHOR]