Request Dispatch, Resource Allocation, DVFS, and Geographically Load Balancing in Data Centers
The cloud computing paradigm is quickly gaining popularity because of its advantages in on-demand self-service, ubiquitous network access, location independent resource pooling, and transference of risk. The ever increasing demand for the cloud computing service is driving the expense of the data centers through the roof. In order to control the expense of a data center while satisfying the clients’ requests specified in the service level agreements (SLAs), one must find the appropriate design and management policy. We have proposed joint optimization of request dispatch, resource allocation, dynamic voltage and frequency scaling (DVFS), and geographically load balancing among different data centers in a cloud computing system, in order to enhance the cloud computing system’s net profit, which is the revenue it receives from processing service requests minus the overall energy cost.Related work:
- H. Goudarzi and M. Pedram. “Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems,” Proc. of the IEEE Cloud, Jul. 2011.
- K. Patel, M. Annavaram, and M. Pedram. “NFRA: Generalized Network Flow Based Resource Allocation for Hosting Centers,” IEEE Trans. on Computers, Vol. 62, No. 9, Sep. 2013, pp. 1772-1785.
- H. Goudarzi and M. Pedram. “Geographical Load Balancing for Online Service Applications in Distributed Datacenters,” Proc. of the IEEE Cloud, Jun. 2013.
- Y. Wang, S. Chen, H. Goudarzi, and M. Pedram. “Resource allocation and consolidation in a multi-core server cluster using a Markov decision process model,” Proc. of the Int’l Symposium on Quality Electronic Design, Mar. 2013.
Placement, capacity provisioning and request flow control for distributed cloud infrastructure
The cloud computing paradigm is quickly gaining popularity because of its advantages in on-demand self-service, ubiquitous network access, location independent resource pooling, and transference of risk. The ever increasing demand for the cloud computing service is driving the expense of the data centers through the roof. In order to control the expense of a data center while satisfying the clients’ requests, one must find the appropriate design and management policy. Being aware of the interdependency between the problem of placement and capacity provisioning when designing a data center and resource allocation when operating the data center, we propose a generalized concurrent placement, capacity provisioning, and request flow control optimization framework for a distributed cloud infrastructure. With the trend of dynamic utility pricing, we try to utilize energy storage devices such as battery cells to further lower the utility cost of a data center.Related work:
- S. Chen, Y. Wang, and M. Pedram. “Concurrent placement, capacity provisioning, and request flow control for a distributed cloud infrastructure,” Proc. of Design Automation and Test in Europe, Mar. 2014.
- S. Chen, Y. Wang, and M. Pedram. “Resource allocation optimization in a data center with energy storage devices,” To appear in Proc. of the 40th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2014.
Power-aware Control for a Mobile Device in a Cloud Computing System
Because of the enlarging gap between the rapidly increasing power demand of mobile devices (e.g. smartphones, tablet PCs, etc.) and the limited growth of the volumetric/gravimetric energy density in rechargeable batteries, the battery lifetime has become a major concern in the design of these mobile devices. Apart from some well-known techniques including DVFS that can balance between the processing power and the power consumption of some components in the mobile device, computation offloading, a technique that transfers some local tasks to a server in the cloud, can also be used to extend the battery life of a mobile device. Our work propose an optimization framework for a mobile device based on a semi-Markov decision process model to determine the DVFS policy, proportion of tasks to be offloaded to the cloud, as well as the transmission bit rate used for offloading.Related work:
- S. Chen, Y. Wang, and M. Pedram. “Optimal Offloading Control for a Mobile Device Based on a Realistic Battery Model and Semi-Markov Decision Process,” To appear in Proc. of the Int’l Conf. on Computer Aided Design, Nov. 2014.
Trace-based Workload Characterization for a Cloud Computing System
The prediction of the workload profile of the cloud service clients can help optimize the operational cost and improve the quality of service (QoS) for the cloud infrastructure provider. However, because of the complex dynamics in the users’ behavior, it is challenging to generate workload prediction results with high accuracy. By analyzing the cluster dataset released by Google, we identify the multi-fractal behavior of the workload profile, based on which we propose a prediction algorithm using fractional ordered derivatives and find that the alpha-stable distribution can be used to fit the distribution of a set of characteristics of the workload.Related work:
- S. Chen, M. Ghorbani, Y. Wang, P. Bogdan, and M. Pedram. “Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique,” Proc. of the IEEE Conference on Big Data, Jun. 2014.