Cloud Resource Management With Turnaround Time Driven Auto-Scaling

Liu, Xiaolong; Yuan, Shyan-Ming; Luo, Guo-Heng; Huang, Hsiang-Cheh; Bellavista, Paolo

doi:10.1109/access.2017.2706019

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…Interdependency is another key characteristic which means direct/indirect connection and relation in cloud resource and this can increase the level of complexity. [3] Xiaolong Liu et al discuss about AWS CloudWatch (CW) tools, AWS CloudWatch is cloud resource management frameworks of AWS and it is a monitoring tool used by AWS which uses metrics to monitor the AWS resources. This monitoring allows for doing some triggers in specific conditions.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Notification for Improper Use of AWS Resources

Nayak¹,

L.M.²

2019

IJRTE

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A proper management of resources is very important when they are on public cloud to ensure security, cost optimization etc. Monitoring resources and sending notification to admin or authorized person for all monitored activities helps the corporate institute/organization who is using public cloud like AWS to manage the resources properly, as AWS in an organization is used by different users though they have roles associated with them but they can do any mistake which costs for an organization. Automated notifications can help on this issue when resources are monitored and notified to admin/authenticated person and they can take appropriate actions. This paper focuses on how to make an environment and hardware setup of instance in AWS using AMIs, user data and Terraform, securing and managing S3 from unwanted objects and automated deletion of unwanted resources. It is also important for an organization to verify that developers, tester etc. are working according to their provided environment standard so that they can guarantee that there is not be any problem after or during project development or delivery so, in context of that AMIs can be created and EC2 monitored for AMI.

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Section: Literature Surveymentioning

confidence: 99%

Automated Notification for Improper Use of AWS Resources

Nayak¹,

L.M.²

2019

IJRTE

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“…Turnaround time driven auto-scaling: [10] In the dynamic scaling mechanism, a turnaround time monitor and an event alarm is used to trigger the relative scaling policy and execute it. In the schedule-based auto scaling module, the Pre-configuration Maker can automatically conduct serial testing with the help of elastic load tester.…”

Section: 4mentioning

confidence: 99%

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

Sridevi¹,

Katiravan²

2018

IJCA

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Efficient resource provisioning is the key challenge that brings the best quality of service which is beneficial both to the users and CSPs. Since cloud computing aims at providing adaptive provisioning as pay-per-use basis, dynamic resource provisioning is a critical research issue. The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. To ensure better performance, there is a need for auto-scaling. The problem of assigning resources to tasks and orchestrating their execution to preserve the dependencies of workflows is NP-complete. [2] Hence, no optimal solution can be found in polynomial time. NP-complete problems are often addressed by using heuristic or meta-heuristics approaches. This survey presents the existing auto-scaling techniques and metaheuristics approaches for VM placements of cloud workflows.

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“…In other words, under such circumstances, service provider has inaccurately predicted customer demand, which causes a high volume of payloads concurrently request to a few computing instances in the cloud environment. Elastic scaling is one of the mechanisms that can be used in IaaS, which enables service providers to maintain their resources and reduce wasted resources by increasing or reducing computing instances whenever required. The main idea of elastic scaling mechanism is to monitor system metrics, such as CPU utilization, memory utilization, disc I/O, and network I/O on the server side, for triggering system configurations .…”

Section: Introductionmentioning

confidence: 99%

Automatic cloud service testing and bottleneck detection system with scaling recommendation

Liu

Sheu

et al. 2019

Concurrency and Computation

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Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.

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Cloud Resource Management With Turnaround Time Driven Auto-Scaling

Cited by 8 publications

References 21 publications

Automated Notification for Improper Use of AWS Resources

Automated Notification for Improper Use of AWS Resources

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

Automatic cloud service testing and bottleneck detection system with scaling recommendation

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