Comprehensive Analysis of Performance, Fault-Tolerance and Scalability in Grid Resource Management System

Kong, Xiangzhen; Huang, Jiwei; Chen, Lin

doi:10.1109/gcc.2009.30

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Even if one determines the latency, it can change over the period of time [6]. Node is a virtual and computation can be migrated from one virtual machine instance to another [19]. The user of real time cloud applications has lose control over the nodes and does not know where his application is going to be processed.…”

Section: Cloud Computing Fault Tolerancementioning

confidence: 99%

Cloud Computing Online Scheduling

Keshk¹

2014

IOSRJEN

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-Cloud computing has gained a lot of attention to be used as a computing model for a variety of application domains. Task scheduling is the fundamental issue in this environment. To utilize cloud efficiently, a good task scheduling algorithm is needed to assign tasks to resources in cloud. Cloud task can be divided into two categories such as on-line mode service and the batch mode service. In this paper, online cloud task scheduling based on virtual machine adaptive fault tolerance and load balancing using ant colony algorithm is proposed. The main contribution of this work is that load balancing factor is added and the system tolerates the faults by tacking the decision on the basis of reliability of the virtual machines in scheduling process. The proposed scheduling strategy was simulated using the Cloudsim toolkit package. Experimental results show that the proposed algorithm achieved the better load balance than Join-shortest-queue (JSQ) and Modified Ant Colony Optimization (MACO) algorithms.

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Section: Cloud Computing Fault Tolerancementioning

confidence: 99%

Cloud Computing Online Scheduling

Keshk¹

2014

IOSRJEN

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“…In cloud, the latency of nodes (virtual machines) is unknown. Even if one determines the latency, it can change over the period of time [5]. Generally, IP is not associated to a particular instance [2].…”

Section: Related Workmentioning

confidence: 99%

“…al. [1,5] presented a model for virtual infrastructure performance and fault tolerance. But it is not well suited for the fault tolerance of real time cloud applications.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Fault Tolerance in Real Time Cloud Computing

Malik

Huet

2011

2011 IEEE World Congress on Services

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--With the increasing demand and benefits of cloud computing infrastructure, real time computing can be performed on cloud infrastructure. A real time system can take advantage of intensive computing capabilities and scalable virtualized environment of cloud computing to execute real time tasks. In most of the real time cloud applications, processing is done on remote cloud computing nodes. So there are more chances of errors, due to the undetermined latency and loose control over computing node. On the other side, most of the real time systems are also safety critical and should be highly reliable. So there is an increased requirement for fault tolerance to achieve reliability for the real time computing on cloud infrastructure. In this paper, a fault tolerance model for real time cloud computing is proposed. In the proposed model, the system tolerates the faults and makes the decision on the basis of reliability of the processing nodes, i.e. virtual machines. The reliability of the virtual machines is adaptive, which changes after every computing cycle. If a virtual machine manages to produce a correct result within the time limit, its reliability increases. And if it fails to produce the result within time or correct result, its reliability decreases. A metric model is given for the reliability assessment. In the model, decrease in reliability is more than increase. If the node continues to fail, it is removed, and a new node is added. There is also a minimum reliability level. If any processing node does not achieve that level, the systems will perform backward recovery or safety measures. The proposed technique is based on the execution of design diverse variants on multiple virtual machines, and assigning reliability to the results produced by variants. The virtual machine instances can be of same type or of different types. The system provides both the forward and backward recovery mechanism, but main focus is on forward recovery. The main essence of the proposed technique is the adaptive behavior of the reliability weights assigned to each processing node and adding and removing of nodes on the basis of reliability.

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“…Between a centralized, hierarchical, and peer-to-peer logical topology, the authors conclude that a hierarchical topology is the most resilient, while a peer-to-peer topology is the most scalable. Adding the resource management system (RMS) into the logical topology for physical resource allocation, peerto-peer becomes the most resilient and scalable topology for cloud virtualization [201].…”

Section: F Measurement and Estimation Of Resiliency In Cloud Computingmentioning

confidence: 99%

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Colman-Meixner

Develder

Tornatore

et al. 2016

IEEE Commun. Surv. Tutorials

127

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Abstract-Today's businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency: disruptions due to failures (either accidental or because of disasters or attacks) may entail significant revenue losses (e.g., US$ 25.5 billion in 2010 for North America). Such failures may originate at any of the major components in a cloud architecture (and propagate to others): (i) the servers hosting the application, (ii) the network interconnecting them (on different scales, inside a data center, up to wide-area connections), or (iii) the application itself. We comprehensively survey a large body of work focusing on resilience of cloud computing, in each (or a combination) of the server, network, and application components.First, we present the cloud computing architecture and its key concepts. We highlight both the infrastructure (servers, network) and application components. A key concept is virtualization of infrastructure (i.e., partitioning into logically separate units), and thus we detail the components in both physical and virtual layers. Before moving to the detailed resilience aspects, we provide a qualitative overview of the types of failures that may occur (from the perspective of the layered cloud architecture), and their consequences.The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency. This ranges from designing and operating the facilities, servers, networks, to their integration and virtualization (e.g., also including resilience of the middleware infrastructure).The third part focuses on resilience in application design and development. We study how applications are designed, installed, and replicated to survive multiple physical failure scenarios as well as disaster failures.

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Comprehensive Analysis of Performance, Fault-Tolerance and Scalability in Grid Resource Management System

Cited by 8 publications

References 12 publications

Cloud Computing Online Scheduling

Cloud Computing Online Scheduling

Adaptive Fault Tolerance in Real Time Cloud Computing

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

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