SMLHADC: Security Model for Load Harmonization and Anomaly Detection in Cloud

A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker's idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.

Multi objective trust aware task scheduling algorithm in cloud computing using whale optimization

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Cloud computing is an inventive innovation that has acquired a progressive changes in computing administrations. It has empowered to drive the concentration from neighborhood/individual calculation to datacenter-driven algorithm by giving resources powerfully in a virtualized way by means of Internet. The aim of load adjusting is to clearly comprehend the client prerequisites, the information and data which can be sent and obtained without taking additional time. In this paper, we have focused on cloud computing, load balancing techniques, cloud resource scheduling process. Review on load balancing techniques and resource scheduling, sidechannel attack is also presented in this paper.KeywordsCloud computingLoad balancingSidechannel attacksResource scheduling

Good load balancing is the key to deriving maximal performance from multiprocessors. Several successful dynamic load balancing techniques on tightly-coupled multiprocessors have been developed. However, load balancing is more difficult on loosely-coupled multiprocessors because inter-processor communication overheads cost more. Dynamic load balancing techniques have been employed in a few programs on loosely-coupled multiprocessors, but they are tightly built into the particular programs and not much attention is paid to scalability. We have developed a dynamic load balancing scheme which is applicable to OR-parallel programs in general. Processors are grouped, and work loads of groups and processors are balanced hierarchically. Moreover, it is scalable to any number of processors because of this multi-level hierarchical structure. The scheme is tested for the all-solution exhaustive search Pentomino program on the mesh-connected loosely-coupled multiprocessor Multi-PSI, and speedups of 28.4 times with 32 processors and 50 times with 64 processors have been attained.

Good load balancing is the key to deriving maximal performance from multiprocessors. Several successful dynamic load balancing techniques on tightly-coupled multiprocessors have been developed. However, load balancing is more difficult on loosely-coupled multiprocessors because inter-processor communication overheads cost more. Dynamic load balancing techniques have been employed in a few programs on loosely-coupled multiprocessors, but they are tightly built into the particular programs and not much attention is paid to scalability. We have developed a dynamic load balancing scheme which is applicable to OR-parallel programs in general. Processors are grouped, and work loads of groups and processors are balanced hierarchically. Moreover, it is scalable to any number of processors because of this multi-level hierarchical structure. The scheme is tested for the all-solution exhaustive search Pentomino program on the mesh-connected loosely-coupled multiprocessor Multi-PSI, and speedups of 28.4 times with 32 processors and 50 times with 64 processors have been attained.

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.

Cloud service provider, infrastructure vendor and clients/Cloud user’s are main actors in any cloud enterprise like Amazon web service’s cloud or Google’s cloud. Now these enterprises take care in infrastructure deployment and cloud services management (IaaS/PaaS/SaaS). Cloud user ‘s need to provide correct amount of services needed and characteristic of workload in order to avoid over – provisioning of resources and it’s the important pricing factor. Cloud service provider need to manage the resources and as well as optimize the resources to maximize the profit. To manage the profit we consider the M/M/m queuing model which manages the queue of job and provide average execution time. Resource Scheduling is one of the main concerns in profit maximization for which we take HYBRID PSO-MOBA as it resolves the global convergence problem, faster convergence, less parameter to tune, easier searching in very large problem spaces and locating the right resource. In HYBRID PSO-MOBA we are combining the features of PSO and MOBA to achieve the benefits of both PSO and MOBA and have greater compatibility.

In distributed computing cloud computing is an emerging technology which provides pay per model as per user demand or requirement. Cloud has a collection of virtual machines which facilities both computational and storage requirement. Scheduling and Load balancing are the main challenges in the cloud computing on which we are emphasizing. Scheduling is the process to control the order of work going to be performed by computer system. Load balancing has an important role in the performance in cloud computing. Better load balancing will make cloud computing more efficient and will also increase user satisfaction. It provides a way to handle several inquiries residing inside cloud computing environment set. Complete balancing acquires two tasks, one is resource provisioning/resource allocation and task scheduling throughout the system. In the proposed research paper, we are presenting a hybrid algorithm created by FCFS and Round Robin algorithms. As the Round Robin is the easiest algorithm that's why it is frequently used and the first preference for implementing easy schedulers. The Round Robin algorithm only requires a list of nodes. In the proposed solution we have eliminated the drawbacks of simple Round Robin algorithm by introducing assignment of time slices to different processes depending upon priorities.

In recent years, the adoption of multi-cloud environments has increased due to its ability to provide better performance, cost-effectiveness, and higher availability. However, managing resources in such environments is a challenging task. Load balancing and virtual machine (VM) migration are two critical techniques for resource management in multi-cloud environments. Load balancing helps to distribute the workload evenly among the cloud resources, while VM migration enables the dynamic allocation of resources based on the changing demands. This systematic review paper presents a comprehensive survey of load balancing and VM migration strategies in multi-cloud environments. The paper first introduces the concept of multicloud environments and the challenges of resource management in such environments. Then, the paper discusses the different load balancing and VM migration techniques that have been proposed for multi-cloud environments, including their advantages and limitations.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

The notion of load balancing is necessary to maximise the use of cloud resources due to the daily increase in internet traffic. For the system's performance and stability, load balancing becomes essential. Consequently, an algorithmic strategy is needed to balance work among virtual machines (VMs) and improve system performance. Techniques: To provide load balancing and QoS, task scheduling methods are employed. Cloud computing is an entirely internet-based method in which all information and programs are stored on a cloud, which is made up of thousands of machines connected in a complex way. By guaranteeing optimal use of available resources, a load balancing algorithmic strategy seeks to improve the response time of user-submitted applications. The primary goal of load balancing techniques is to accelerate the execution of programs on resources whose workload fluctuates in unanticipated ways during runtime.