Improving resource allocation for data center overbooking

Simple bin packing heuristics have been analyzed and used to place virtual machines upon arrival.

This thesis made the following contributions. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs.

Abstract Resource allocation strategies in virtualized data centers have received considerable attention recently as they can have substantial impact on the energy efficiency of a data center.

Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes.

First, we precisely estimated capacity requirements of client virtual machines VMs while renting server space in cloud environment.

Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements SLAs and multiple resource dimensions.

We focus on dynamic environments where virtual machines need to be allocated and deallocated to servers over time. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment.

Resource overbooking and application profiling in shared hosting platforms

However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future. We specifically addressed two crucial data center operations. Previous article in issue. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications.

Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. The benefits of accurate application performance modeling are multifold. We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels.

Third, we presented an approach to optimal VM sizing by employing the performance models we created. We compare VM allocation strategies for cloud environments experimentally.

On Cloud-based Oversubscription

This led to new decision and control strategies with significant managerial impact for IT service providers. We ran extensive lab experiments and simulations with different controllers and different workloads to understand which control strategies achieve high levels of energy efficiency in different workload environments.

This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools.

To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. While the type of placement heuristic had little impact on the average server demand, the type of virtual machine resource demand estimator used for the placement decisions had a significant impact on the overall energy efficiency.By overbooking cluster resources in a controlled fashion, our platform can provide performance guarantees to applications even when overbooked, and combine these techniques with commonly used QoS resource allocation mechanisms to provide application isolation and performance guarantees at run-time.

iOverbook: Managing Cloud-based Soft Real-time Applications in a Resource-Overbooked Data Center Faruk Caglar and Aniruddha Gokhale Department of Electrical Engineering and Computer Science. Airline Yield Management with Overbooking, Cancellations, and No-Shows. Janakiram Subramanian, Shaler Stidham, An Autonomic Approach to Risk-Aware Data Center Overbooking.

IEEE Transactions on Cloud Computing, Vol. 2, No. 3 High Performance Resource Allocation Strategies for Computational Economies.

Improving Resource Utilisation in the Cloud Environment Using Multivariate Probabilistic Models

percent of the total energy costs in a data center. Resource overbooking is one way to reduce the usage of active hosts and networks Our approach calculates resource allocation ratio based on the which can help improve Quality of Service (QoS) of the net-work intensive applications.

approximation to improve resource allocation strategies. This resource allocation mechanisms for data centers in the cloud to satisfy video application requirements with the minimum cost. The study gives information about a new type of service resource overbooking [21] in shared hosting platforms.

Overbooking-based Resource Allocation in Virtualized Data Center Tianyu Wo, Qian Sun, Bo Li, Chunrning Hu, approach can greatly improve the request acceptance rate and C. Resource Allocation in Data Centers Normally, VM placement is decided by various capacity.

Improving resource allocation for data center overbooking
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