Heterogeneous Multi-cloud Environment Research Articles

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the perfo}rmance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the othertwo algorithms

Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

Heterogeneous multi-cloud environments make use of a collection of diverse performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications in the multi-cloud environment require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Scheduling tasks to distributed heterogeneous clouds is termed NP-complete which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, three heuristicsPair-Task Threshold Limit (PTL), PTMax-Min, and PTMin-Max are proposed. Firstly, proposedheuristics calculate tasks threshold valuebased on the tasks attributes to determine the tasks scheduling order and then tasks are sorted in descending order of threshold value. Group 1 comprises ([n/2]) tasks ordered in descending value of threshold. Group 2 comprises remaining tasks ([n/2] − 1) ordered in ascending value of threshold. Secondly, tasks form group 1 are scheduled first based on minimum completion time, and then tasks in group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. The proposed approaches PTL, PTMax-Min, and PTMin-Max explicitly shows the better results in terms of reduced makespan, completion time, response time and more resource utilization compared to MCT, MET, and Min-min.

Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.

Energy-Aware Cloud Task Scheduling algorithm in heterogeneous multi-cloud environment

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

Replica-aware task scheduling and load balanced cache placement for delay reduction in multi-cloud environment

With the development of content-sharing and collaborative computing services such as online social networks, scientific workflow, there are huge amounts of data generated. To process this tremendous amount of data, multi-cloud system that integrates multiple clouds together to provide a unified service in a collaborative manner has been introduced. However, task scheduling in such heterogeneous multi-cloud environment is very challenging. To reduce response delay caused by cross-data centers file access, we proposed a replica-aware task scheduling algorithm based on data replication. For speeding up data access in multi-cloud cooperative caches, we presented a load balanced cache placement algorithm based on Bayesian networks. In our scheduling algorithm, combined transferring computation with transferring data, resource matching is accomplished according to node locality. Only non-local unassigned and failed map tasks’ input data are replicated and transferred in advance to target nodes to expedite task execution. In our cache placement method, based on Bayesian networks the next execute task is predicted. In accordance with caching profit and recycling cost, cache prefetching files are selected. For each prefetching file, according to load balancing, target placement node is determined. Extensive experimental results show that the performance of our proposed replica-aware task scheduling algorithm is better than benchmark scheduling algorithms in terms of node locality ratio and job response time, and our load balanced cache placement algorithm outperforms the baseline caching algorithms in performance of prefetching hit ratio and execution time saving ratio.

Task Partitioning Scheduling Algorithms for Heterogeneous Multi-Cloud Environment

Cloud computing is now an emerging trend for cost-effective, universal access, reliability, availability, recovery and flexible IT resources. Although cloud computing has a tremendous growth, there is a wide scope of research in different dimensions. For instance, one of the challenging topics is task scheduling problem, which is shown to be NP-Hard. Recent studies report that the tasks are assigned to clouds based on their current load, without considering the partition of a task into pre-processing and processing time. Here, pre-processing time is the time needed for initialization, linking and loading of a task, whereas processing time is the time needed for the execution of a task. In this paper, we present three task partitioning scheduling algorithms, namely cloud task partitioning scheduling (CTPS), cloud min–min task partitioning scheduling and cloud max–min task partitioning scheduling, for heterogeneous multi-cloud environment. The proposed CTPS is an online scheduling algorithm, whereas others are offline scheduling algorithm. Basically, these proposed algorithms partition the tasks into two different phases, pre-processing and processing, to schedule a task in two different clouds. We compare the proposed algorithms with four task scheduling algorithms as per their applicability. All the algorithms are extensively simulated and compared using various benchmark and synthetic datasets. The simulation results show the benefit of the proposed algorithms in terms of two performance metrics, makespan and average cloud resource utilization. Moreover, we evaluate the simulation results using analysis of variance statistical test and confidence interval.

SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

Service-level agreement (SLA) is a major issue in cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-cloud environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-cloud environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms.

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks (i.e., makespan). However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

Normalization-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment

Cloud computing is one of the most successful technologies that offer on-demand services through the Internet. However, datacenters of the clouds may not have unlimited capacity which can fulfill the demanded services in peak hours. Therefore, scheduling workloads across multiple clouds in a federated manner has gained a significant attention in the recent years. In this paper, we present four task scheduling algorithms, called CZSN, CDSN, CDN and CNRSN for heterogeneous multi-cloud environment. The first two algorithms are based on traditional normalization techniques, namely z-score and decimal scaling respectively which are hired from data mining. The next two algorithms are based on two newly proposed normalization techniques, called distribution scaling and nearest radix scaling respectively. All the proposed algorithms are shown to work on-line. We perform rigorous experiments on the proposed algorithms using various synthetic as well as benchmark datasets. Their performances are evaluated through simulation run by measuring two performance metrics, namely makespan and average cloud utilization. The experimental results are compared with that of existing algorithms to show the efficacy of the proposed algorithms.

Uncertainty-Based QoS Min–Min Algorithm for Heterogeneous Multi-cloud Environment

With the advances in virtualization technology, cloud has become the most powerful and promising platform for business, academia, public and government organizations. The cloud users do not require to maintain any IT infrastructure such as hardware, software and network resources in their premises. They can rent the services on demand from anywhere in the world just by paying for that service. In cloud computing, task allocation is a well-known problem. Many algorithms have been developed for the same. However, task allocation in a heterogeneous multi-cloud environment is much more challenging due to the dynamic nature of the cloud resources. In this paper, we present an algorithm, called uncertainty-based quality of service (QoS) Min–Min (UQMM) algorithm which considers QoS based on uncertainty parameters in heterogeneous multi-cloud environment. To the best of our knowledge, this is the first paper which deals with the task allocation problem with uncertainty-based QoS in a heterogeneous multi-cloud systems. We perform extensive simulations on the proposed algorithm using benchmark as well as synthetic datasets and measure performance in terms of various metrics. The results are compared with the popular cloud min–min scheduling, cloud min–max normalization and smoothing-based task scheduling algorithm to show the effectiveness of the proposed algorithm. Moreover, we evaluate the results using two statistical tests, namely t test and ANOVA.