A Framework For User Feedback Based Cloud Service Monitoring Authors: Zia ur Rehman Omar K Hussain Sazia Parvin Farookh K. Hussain Presenter: Sajala Rajendran

Abstract Goal: Assist users in choosing appropriate cloud services that offer optimal performance at lowest cost. A multi-criteria optimization or decision-making problem Major issues: (1) Choice of criteria set (2) Assessing cloud services against each criterion

Contd… Existing cloud service monitoring mechanisms depend on benchmark tests which is inaccurate. Proposes a user-feedback-based approach by monitoring cloud performance more reliably and accurately compared to existing mechanisms.

Outline Introduction Motivation Related Work Problem Formalization Proposed Framework Conclusion Future Work

Introduction Cloud services have different service characteristics, levels of abstraction, quality of service and pricing policies.

All these classification make it more complex for optimal service selection. Proposed mechanism depends on QoS history collected by capturing changes in performance and quality of provided service over a time interval. Continuous monitoring is required.

Cloud providers offer tools to check cloud status Third party cloud monitoring services available Current cloud status data and past performance is vital for accurate cloud service selection Else, cloud users need to deploy applications on different clouds to determine the relative performance of each – a costly, cumbersome and an inefficient process

Motivation Cloud users should have quality of service and performance related information of other cloud service offerings as well. Understanding will ensure efficient cloud service selection Migration from one service to another Difficulty in migration from one service to another due to incompatibilities – hypervisors( Xen, KVM or Vmware ) Interoperable and federated clouds achieve compatibility using open cloud software and inter-cloud protocols. (Open Nebula, Nimbus Project)

QoS data collected through cloud monitoring Currently, information regarding cloud service selection comes in the form of SLA’s and dashboard services. A third party cloud service monitoring is essential to gather unbiased QoS information. Third parties use benchmarks that cannot reflect performance of an actual application in the cloud Proposed approach enables sharing of usage experience

Related Work – Cloud Service Selection Goscinski stresses the need for research on developing methodologies for service selection in cloud computing. Li discussed the importance of having a service provider comparison framework. Presented a tool called CloudCmp that relies on several benchmark tools to compare services. Garg provided a standard set of attributes for cloud comparison.

Cloud Monitoring

CloudHarmony Provides vital information on the performance of clouds using benchmark tests Checks performance of services Data collected is provided to users Based on the data, users make a decision about migration

Applications differ in resource usage leading to different performance. Differences between actual and predicted cost Cloud profiling techniques have been developed to track resource usage of user applications. Provides vital information to predict the performance and cost of these applications in a cloud environment. These mechanisms use complex benchmarks.

Cloudle Aimed at determining resource usage pattern User’s application is run in a simulated environment Resource usage pattern recorded predicts expected cloud resource requirements of the application Drawbacks – (1) Does not have its own cloud monitoring mechanism (2) Depends on existing cloud monitoring services

Problem Formalization Problem domain defined using three sets C = { C1, C2, …. Cn } – available cloud services U = { u1, u2, ….un } – Set of current users Assumption : All the services in C are IaaS using same virtualization tool VM migration across different services is feasible

Relationship between users and cloud services is represented by the following matrix Row – cloud user Column – cloud service User i using cloud service k – corresponding element is 1 else 0

Example… Five cloud services 7 cloud users C = { C1, C2, …. C5 } U = { u1, u2, ….u7 } Example…

Proposed Framework Cloud Status Checker – Check the status of the application running on the cloud generating cloud status report. Status checker functionality is installed in the VM by each participating user. Repository – Maintains all status reports generated by previous step. Determining resource usage pattern of cloud applications – status reports reflect performance of common application types on popular cloud services at any time.

Dashboard Interface – Mechanism for users to access the information. Testing of new cloud applications – Cloud status checker or temporary cloud environment to determine application’s resource usage patter Assumption – Cloud services offering satisfactory service to existing applications having similar usage pattern to that of new applications are the best services.

Advantages Better approach compared to existing cloud monitoring services User provided information is more reliable compared to third part benchmark data or dashboards provided by vendors. Since users obtain monitoring data at no cost, they participate in the system despite paying for resources consumed in running cloud status checker. Cost of hosting repositories can be shared between cloud vendors who can increase their number of customers and enhance customer trust in them.

Conclusion and Future Work Authors have proposed a cloud monitoring system based on reliable user data to assist in cloud service selection and migration. Information sharing mechanism exists between cloud users, proving to be more effective In future, the authors will be involved in development of a simulation and a working prototype. Also, investigate the security issues that can arise in this system.

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