P-Tracer: Path-Based Performance Profiling in Cloud Computing Systems

Mi, Haibo; Wang, Huaimin; Cai, Hubo; Zhou, Yangfan; Lyu, Michael R.; Chen, Zhenbang

doi:10.1109/compsac.2012.69

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…In cloud system it is possible that some nodes to be overloaded and some of them may be under loaded [9]. This scenario results in low performance.…”

Section: Load Balancingmentioning

confidence: 99%

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An Efficient Method for Resource Monitoring in Cloud Nodes using Dynamic Load Balancing Algorithm

Ansari¹,

Patil²

2015

IJERT

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This Cloud computing is the next invention of computation. Almost all the data required to human being is available on the cloud. Cloud computing is used to provide the resources to client on demand. The resources can be either software or hardware resources. Cloud computing architectures are distributed, parallel and supply the needs of multiple clients in different situation. This distributed architecture deploys resources distributive to deliver services effectively to users in different geographical channels. Clients in a distributed environment generate request at random in any processor. So the major drawback of this randomness is related with task assignment. The imbalanced task assignment to the processor creates inequality i.e., few of the processors are overloaded and few of them are under loaded. The objective of load balancing is to shift the load from overloaded process to under loaded process clearly. Load balancing is one of the vital issues in cloud computing. To achieve high performance, minimum response time and high resource utilization ratio it is required to transfer the tasks between nodes in cloud network. This paper provides information about cloud computing, load balancing methods and the proposed load balancing system for above defined problem.

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“…In cloud system it is possible that some nodes to be overloaded and some of them may be under loaded [9]. This scenario results in low performance.…”

Section: Load Balancingmentioning

confidence: 99%

“…client would be mobile devices. Second component is data center which is nothing but collection of servers hosting multiple applications [9]. Recently virtualization [6] [7] is used to install software that allows different instances of virtual server applications.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Resource Monitoring in Cloud Nodes using Dynamic Load Balancing Algorithm

Ansari¹,

Patil²

2015

IJERT

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“…For evaluating the performance of a cloud computing system, one of the key performance requirements is to assure that it is a SLA-driven system performance [16]. To determine the cloud performance, the major challenges in large-scale cloud computing, are massive scalability, dynamic configuration, and complexity of component interactions.…”

Section: Performance Analysis and Issues In Cloud Computingmentioning

confidence: 99%

A Features-based Comparative Study of the State-of-the-art Cloud Computing Simulators and Future Directions

Waqas¹,

Rehman²,

Gilal³

et al. 2017

ijacsa

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Abstract-Cloud computing has emerged during the last decade and turned out to be an essential component for today's business. Therefore, many solutions are being proposed to optimize and secure the cloud computing environment. To test and validate the proposed solutions before deploying in real cloud infrastructure, a cloud computing simulator is the key requirement. There are several cloud computing simulators that have been used by research community for this purpose. In this paper, we have discussed modern cloud simulators and presented comprehensive comparison based on their features.

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“…Spectroscope [2] introduces to find primary causes of performance changes between two time intervals. P-Tracer [7] can be able to identify anomalies available in the call trees and once the anomalies are detected these can be removed from the data.…”

Section: I) Statistical Anomaly Detectionmentioning

confidence: 99%

Black Box Anomaly Detection in Multi-Cloud Environment

Ahirwar¹,

Ahirwar²,

Chourasia³

2016

IJCA

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Automatic identification of anomalies for performance diagnosis in the cloud computing is a fundamental and challenging issue. TPA is interested to identifies these anomalies and remove them so that the performance of the cloud systems increased. In this paper we are proposing an Automatic Black Box Anomaly Detector which can find anomalies automatically with minimum human intervention. Using this detector we can find old and even new anomalies created in the cloud computing systems even if we don't have knowledge of source code (i.e. black box testing). Automatic black box anomaly detection is a two step process in which first of all data from different sources is collected and transform it into a common form that is act as input for black box anomaly detector and secondly anomaly detection is performed. General TermsAnomaly detection in cloud computing. KeywordsBlack box anomaly detector, cloud service provider, performance diagnosis, cloud systems.

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P-Tracer: Path-Based Performance Profiling in Cloud Computing Systems

Cited by 14 publications

References 14 publications

An Efficient Method for Resource Monitoring in Cloud Nodes using Dynamic Load Balancing Algorithm

An Efficient Method for Resource Monitoring in Cloud Nodes using Dynamic Load Balancing Algorithm

A Features-based Comparative Study of the State-of-the-art Cloud Computing Simulators and Future Directions

Black Box Anomaly Detection in Multi-Cloud Environment

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