Inderveer Chana

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.

QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review

As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions.

QRSF: QoS-aware resource scheduling framework in cloud computing

Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.

Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy

The increase in energy consumption is the most critical problem worldwide. The growth and development of complex data-intensive applications have promulgated the creation of huge data centers that have heightened the energy demand. In this article, the need for energy efficiency is emphasized by discussing the dual role of cloud computing as a major contributor to increasing energy consumption and as a method to reduce energy wastage. This article comprehensively and comparatively studies existing energy efficiency techniques in cloud computing and provides the taxonomies for the classification and evaluation of the existing studies. The article concludes with a summary providing valuable suggestions for future enhancements.

Bacterial foraging based hyper-heuristic for resource scheduling in grid computing

Grid computing is a form of distributed computing that co-ordinates and provides the facility of resource sharing over various geographical locations. Resource scheduling in Grid computing is a complex task due to the heterogeneous and dynamic nature of the resources. Bacterial foraging has recently emerged as a global optimization algorithm for distributed optimization and control. This paper proposes the use of the bacterial foraging optimization technique for Grid resource scheduling. A novel bacterial foraging based hyper-heuristic resource scheduling algorithm has been designed to effectively schedule the jobs on available resources in a Grid environment. The performance of the proposed algorithm has been evaluated with the existing common heuristics based scheduling algorithms through the GridSim toolkit. The experimental results show that the proposed algorithm outperforms the existing algorithms by minimizing cost and makespan of user applications submitted to the Grid.

A resource elasticity framework for QoS-aware execution of cloud applications

Abstract Cloud infrastructures consisting of heterogeneous resources are increasingly being utilized for hosting large-scale distributed applications from diverse users with discrete needs. The multifarious cloud applications impose varied demands for computational resources along with multitude of performance implications. Successful hosting of cloud applications necessitates service providers to take into account the heterogeneity existing in the behavior of users, applications and system resources while respecting the user’s agreed Quality of Service (QoS) criteria. In this work, we propose a QoS-Aware Resource Elasticity (QRE) framework that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components. Experimental results conducted on the Amazon EC2 cloud clearly demonstrate the effectiveness of our approach while complying with the agreed QoS attributes of users.

Q-aware

Graphical abstractDisplay Omitted Cloud workloads have been analyzed and clustered through workload patterns.QoS metrics of each workload have been identified.We have analyzed the effect of number of workloads and resources on execution time and cost.Proposed technique demonstrates the minimization of cost and time simultaneously while adhering to workload deadline. Provisioning of appropriate resources to cloud workloads depends on the Quality of Service (QoS) requirements of cloud workloads. Based on application requirements of cloud users, discovery and allocation of best workload - resource pair is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters based resource provisioning technique is therefore required for efficient provisioning of resources. In this paper, QoS metric based resource provisioning technique has been proposed. The proposed technique caters to provisioned resource distribution and scheduling of resources. The main aim of this research work is to analyze the workloads, categorize them on the basis of common patterns and then provision the cloud workloads before actual scheduling. The experimental results demonstrate that QoS metric based resource provisioning technique is efficient in reducing execution time and execution cost of cloud workloads along with other QoS parameters.

Cloud resource provisioning: survey, status and future research directions

Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery of best workload–resource pair based on application requirements of cloud users is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters-based resource provisioning technique is therefore required for efficient provisioning of resources. This research depicts a broad methodical literature analysis of cloud resource provisioning in general and cloud resource identification in specific. The existing research is categorized generally into various groups in the area of cloud resource provisioning. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource management, resource provisioning, resource provisioning evolution, different types of resource provisioning mechanisms and their comparisons, benefits and open issues are described. This research work also highlights the previous research, current status and future directions of resource provisioning and management in cloud computing.

Load Balancing and Job Migration Techniques in Grid: A Survey of Recent Trends

Grid computing has recently become one of the most important research topics in the field of computing. The Grid computing paradigm has gained popularity due to its capability to offer easier access to geographically distributed resources operating across multiple administrative domains. The grid environment is considered as a combination of dynamic, heterogeneous and shared resources in order to provide faster and reliable access to the Grid resources, the resource overloading must be prevented which can be obtained by proper load balancing and job migration mechanisms. This paper presents an extensive survey of the existing load balancing and job migration techniques proposed so far. A detailed classification has also been included based on different parameters which are depending on the analysis of the existing techniques, a new Load balancing technique, along with Job Migration approach has been proposed and discussed to fulfill the existing research gaps.