WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

Recently, the development of cloud computing has received considerable attention. For cloud service providers, packing VMs onto a small number of servers is an effective way to reduce energy costs, so as to improve the efficiency of the data center. However allocating too many VMs on a physical machine may cause some hot spots which violate the SLA of applications. Load balancing of the entire system is hence needed to guarantee the SLA. In this paper, we present a simulated-annealing load balancing algorithm for solving the resource allocation and scheduling problem in a cloud computing environment. Experimental results show that this method is able to achieve load balancing, and performs better than the round robin and basic simulated-annealing algorithms.

The rapid development of cloud computing expands the scale of modern cloud data centers, resulting in growing challenges of energy consumption. Load balancing of virtual machines(VMs), for the purposes of improving the utilization of physical resources and reducing energy consumption, has become a research focus in recent years. However, the existing researches mostly focus on how to maximize resource utilization and reduce energy consumption. Security issues in the context of load balancing of VMs were rarely addressed. In this paper, we research the key procedures of load balancing, VM selection and VM placement, we find that the existing schemes introduced several security problems. In consideration of some conclusions of recent researches and inevitable live migrations during load balancing, common tenants have sufficient reasons to worry about their VMs' security when they are migrated to strange hosts and/or co-reside with the VMs owned by strange tenants. In short, VMs' mobility introduced by load balance expands the attack surface. In this work, we classify and analyze related security threats and create an information leakage model for load balancing. We present a new security policy, SeLance, to secure the load balancing via avoiding above threats as far as possible. We develop exact implementations in CloudSim and OpenStack. We show that SeLance can effectively alleviate the threats introduced by load balancing, the security score can improve 46.90%-81.15%, while keeping the load balancer's original function to a great extent (± 2.5%).

Cloud computing provides us various services over the Internet and services have various problems includes security issues, energy efficiency, big data problem, load balancing etc. Proper Load balancing is used to accomplish proper resource utilization, optimal throughput achievement, reduce response time. Various research papers focused on various technique used to optimize response time, effectively lessens the system load, low computation and other overhead. Resources scheduling in virtual machine in cloud computing environment have also scheduling strategy constructed on genetic algorithm to reduce or avoids dynamic migration. The review paper specially focused on comparison of two algorithms PSO and SJF-MMBF on parameters like total flow time, completion time variance, RPD makespan, RPD total flow time etc.

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.

In this paper, a research work is carried out to improve workload management by minimize energy consumption of node, localizing traffic in a network and reduce communication delay between virtual machines in cloud environment. Cloud computing consists numerous virtual machine to store huge volume of data. The usage of virtual machines is to provide separate guest operating system which is popular now-a-days. The workload management across different virtual machines in cloud is really a difficult process. Decentralized approaches were proposed in the literature for energy efficient management of virtual machines. But still it has some issues like failed to consider the communication between virtual machines and it leads to traffic in a network. Thus it reduces the performance of work load management.

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.

Cloud computing has transformed IT infrastructure by offering scalable, pay-as-you-go resources. However, efficiently allocating cloud resources under dynamic workloads while minimizing operational costs and maintaining service-level agreements (SLAs) remains a critical challenge. This paper presents a hybrid AI-driven framework for cost-efficient resource allocation in cloud environments. The proposed model integrates Long Short-Term Memory (LSTM) networks for workload forecasting, reinforcement learning (RL) for dynamic decision-making, heuristic scheduling (inspired by ACO and GA) for optimal task assignments, and economic pricing using the ERA model. A real-world data set from Google Cluster Trace is used to validate the framework. Simulation results show a forecasted CPU utilization of 10.79% with an estimated cost of ₹1.08 and one SLA violation, demonstrating the potential of hybrid AI models for real-time and cost-aware resource provisioning.

Service level agreement (SLA) is a contract between cloud computing customer and provider. Improving the customers' trust with cloud computing requires monitoring and ensuring the quality of the provided services continuously. Therefore, this paper provides a model for monitoring the SLA which belongs to the same customer and a specific provider of multiple virtual machines (VMs) in the same environment. The core foundations for this model are: SLA, quality of service (QoS) parameters, multiple VMs and violations' penalty assessment. The proposed model monitors the QoS parameters within multiple VMs environment dynamically and checks for any violation. Moreover; the penalty for each violation will be calculated and informed to the customer and provider at the same time. The proposed model depends on the time factor while running the VMs in sequentially manner with a short period of time as a gap between each VM. This model prevents any interception between VMs and can maintain synchronisation.

Cloud computing(CC) delivers multiple services to users by processing complex tasks through internet connections to serve as a dynamic, dependable, and flexible computing solution. With the increased Internet speed, the user demands to perform many tasks through computing provided by cloud virtual machines (VMs) created in heterogeneous servers. The complexity of cloud computing(CC) increases through the diversity of servers and the increased demand for resources to perform more tasks, which makes the cloud data center(CDC) unbalanced in load. Therefore, future cloud systems will require more effective resource management(RM) methods to balance load and improve Quality of Service (QoS). This paper proposes KMPPVM-DRM, a dynamic clustering and scheduling approach for VM resource allocation(RA) based on the KMeans++ algorithm. VMs are clustered into three groups (high, medium, low) according to their real-time normalized CPU and RAM weights, using weighting parameters α = 0.6 and β = 0.4. Tasks are categorized into three levels based on length and mapped to appropriate VM clusters. Within each cluster, a Utilization-Level Comparator (ULC) dynamically selects the optimal VM for task execution. The experimental results were conducted using CloudSim, with 5 and 10 VMs and task counts ranging from 1000 to 10000. The proposed model was compared with PSO, ACO, and DBSCAN algorithms using execution time, average start time, and average finish time. Results show that with 10 VMs, the proposed model achieved a harmonic mean execution time of 886.72 ms, compared to 4102.99 ms (PSO), 4672.25 ms (ACO), and 3071.51 ms (DBSCAN). It also attained the lowest harmonic mean start and finish times of 166.41 ms and 167.19 ms, respectively. Relative reduction in execution time against DBSCAN ranged from 69.90% to 71.52%, with start and finish time improvements between 52.65% and 81.51%.To ensure statistical reliability, a paired t-test confirmed that the performance improvements were statistically significant (p < 0.05) across all task sizes. The findings confirm that KMPPVM-DRM enhances resource allocation(RA) efficiency and scheduling effectiveness, maintaining balanced loads even with limited resources (e.g., only 5 VMs) and outperforming PSO, ACO, and DBSCAN in all tested scenarios.

This paper presents ALARM, an autonomic load-aware resource management algorithm that can be used to manage physical machines or virtual machines in cloud, which participate in a P2P key-value store. A lot of existing key-value stores claim that they are elastic enough to scale up or down with no downtime or interruption to applications. However, the question that when the scaling up or down should take place has still not been resolved. The situation may get worse if the data store consists of hundreds of machines, for it's unrealistic for a system administrator to monitor the system and add/remove a machine manually. Fortunately, cloud computing and virtualization technology have enabled the real-time provision of virtual machines and a way of managing virtual machines without human interference. By supervising the utilization of multiple resources (CPU, memory, network IO, etc.) in virtual machines hosting the data store, our ALARM algorithm will take effect when some of the machines become overloaded or under loaded. The experiment result shows that ALARM helps the Open Chord data store, an open-source implementation of the Chord protocol, scale up and down according to the resource usage in the virtual machines.

Recently, cloud computing has been the most popular method among all other promising technologies that provides the service for many users at the same time. all users can reach and use the resource that is provided by the Virtual Machines (VMs). Hence users process, data must arrive to the resources to perform as a disturbed system, which provides dynamical scale services and virtualized resources. As a result of limitation of resources, the deadlock status may occur in such type of systems. In this paper, we have designed an algorithm that could increase the efficiency of deadlock avoidance. Our technique will use the attribute of execution time in the process to get better resource allocation in additional to the checking system to stay in safe state and free of deadlock.

Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment