CLOUDFARM: An Elastic Cloud Platform with Flexible and Adaptive Resource Management

Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS architecture with an adaptive SLO-based resource management mechanism. It introduces new flexible SLAs backed with a respective development model and management interface for end-user services. According to their SLAs and the price tenants pay, services can be selectively downgraded to overcome short-term peaks, e.g. While preparing for scale-out. Providers can deploy services optimistically and thus maximize their data center utilization and revenue.

[1]  Massoud Pedram,et al.  Multi-dimensional SLA-Based Resource Allocation for Multi-tier Cloud Computing Systems , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[2]  Artur Andrzejak,et al.  Decision Model for Cloud Computing under SLA Constraints , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[3]  Karl-Erik Årzén,et al.  Resource Management on Multicore Systems: The ACTORS Approach , 2011, IEEE Micro.

[4]  Franz J. Hauck,et al.  ARTOS : System Model and Optimization Algorithm Technical Report , 2012 .

[5]  Tarek F. Abdelzaher,et al.  Web Content Adaptation to Improve Server Overload Behavior , 1999, Comput. Networks.

[6]  Seung-won Hwang,et al.  QACO: exploiting partial execution in web servers , 2013, CAC.

[7]  Karl-Erik Årzén,et al.  Brownout: building more robust cloud applications , 2014, ICSE.

[8]  Giorgio C. Buttazzo,et al.  Integrating multimedia applications in hard real-time systems , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[9]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[10]  Erik Elmroth,et al.  A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling , 2013, CAC.

[11]  Franz J. Hauck,et al.  Component-based scalability for cloud applications , 2013, CloudDP '13.

[12]  Franz J. Hauck,et al.  The COSCA PaaS platform: on the way to flexible and dependable cloud computing , 2012, EWDCC '12.

[13]  Mario Macías,et al.  Rule-based SLA management for revenue maximisation in Cloud Computing Markets , 2010, 2010 International Conference on Network and Service Management.

[14]  John A. Stankovic,et al.  Adding Robustness in Dynamic Preemptive Scheduling , 1995, Responsive Computer Systems.

[15]  Qian Zhu,et al.  Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments , 2010, IEEE Transactions on Services Computing.

[16]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[17]  Riccardo Bettati,et al.  Imprecise computations , 1994, Proc. IEEE.

[18]  Franz J. Hauck,et al.  COSCA: an easy-to-use component-based PaaS cloud system for common applications , 2011, CloudCP '11.

[19]  Lui Sha,et al.  Capacity sharing for overrun control , 2000, Proceedings 21st IEEE Real-Time Systems Symposium.

[20]  Artur Andrzejak,et al.  Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.