Monitoring of cloud computing environments

Rodrigues, Guilherme da Cunha; Calheiros, Rodrigo N.; Guimaraes, Vinicius Tavares; Santos, Glederson Lessa dos; Carvalho, Marcio Barbosa de; Granville, Lisandro Zambenedetti; Tarouco, Liane Margarida Rockenbach; Buyya, Rajkumar

doi:10.1145/2851613.2851619

Cited by 45 publications

(10 citation statements)

References 27 publications

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“…However, due to limitations affecting end-users that can only access the monitoring data from within the provided resource and are unaware of the issues caused by other users of the same infrastructure, it is much more complicated for them to reliably detect and properly locate such performance degradation. A study conducted in 2016 by Rodrigues and associates in the area of cloud computing environments identified monitoring of lower-level infrastructure as well as virtualization and multi-tenant monitoring as open issues in the context of IaaS monitoring [6]. Similar conclusions on the need for the advancement of future monitoring systems were echoed in the 2014 survey on cloud monitoring tools that examined 21 available tools for general and cloud monitoring [7].…”

Section: Introductionmentioning

confidence: 92%

A Novel Approach to Client-Side Monitoring of Shared Infrastructures

2020

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Modern computing is migrating towards more prevalent use of shared infrastructures and is transforming physical and fixed resources into a more dynamic and virtualized environment. This trend has proved to be a boon for advanced monitoring and profiling tools and approaches available for infrastructure operators and service providers. On the other end of the spectrum, the clients have been provided with previously unavailable flexibility and simple access to vast resources at the price of being unable to detect or correctly locate issues caused by lower layers of the new complex but abstracted infrastructure. This issue is most visible with performance degradation caused by other tenants of the same infrastructure, tenants formally invisible for other end-users. Although performance interference in shared infrastructures is a known problem that is covered by research aimed at detection and mitigation by infrastructure operators, there is a lack of research aimed at end-users and their ability to detect and locate such performance degradation. This paper covers the areas of shared networking, virtualized compute and grid compute infrastructures, gives an analysis of the specifics of each of the infrastructures, presents a novel approach for client-side monitoring and discusses the practical implementation issues. The system presented in the paper aims to provide end-users with valuable insight into underlying physical infrastructure without burdening them with complex and heterogeneous nature of monitored devices by reducing the shared infrastructure to a single representative generated node simply accessible as a generic SNMP (Simple Network Management Protocol) enabled networked device. The primary aim of the system is to enable end-users to detect and locate the domain of the issue causing the performance interference and not to replace or reimplement existing general-purpose monitoring systems. The system was tested on a regional e-Infrastructure within VI-SEEM and NI4OS-Europe projects where it performed as expected.

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Section: Introductionmentioning

confidence: 92%

A Novel Approach to Client-Side Monitoring of Shared Infrastructures

2020

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“…Different cloud environments depend upon on preferences of clients such as federated and nonfederated, static and dynamic, light-loaded and heavy-loaded, homogeneous, and heterogeneous as displayed in Table 1. The performance of the scheduling algorithm depends on cloud computing environments [27], [28], [29]. Therefore, this paper takes into consideration two environments, namely homogeneous and heterogeneous.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Hybrid Scheduling Algorithm for Task Scheduling in the Cloud Computing Environments

et al. 2021

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The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.

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“…Buyya [5] presents an overview on cloud monitoring and a comparison among relevant cloud monitoring solutions. Resource management is an important mean to improve clouds, and resources monitoring is the key to achieve it.…”

Section: Releted Workmentioning

confidence: 99%

Real Time Load Analysis Using Profiling as-a-Service in Cloud

2017

IJAERD

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Monitoring of cloud computing environments

Cited by 45 publications

References 27 publications

A Novel Approach to Client-Side Monitoring of Shared Infrastructures

A Novel Approach to Client-Side Monitoring of Shared Infrastructures

An Energy-Efficient Hybrid Scheduling Algorithm for Task Scheduling in the Cloud Computing Environments

Real Time Load Analysis Using Profiling as-a-Service in Cloud

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