We found that combinations of placement controllers and periodic reallocations achieve the highest energy efficiency subject to predefined service levels. We compare VM allocation strategies for cloud environments experimentally. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.
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.
To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. We focus on dynamic environments where virtual machines need to be allocated and deallocated to servers over time.
This thesis made the following contributions. First, we precisely estimated capacity requirements of client virtual machines VMs while renting server space in cloud environment.
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.
This led to new decision and control strategies with significant managerial impact for IT service providers. However, these placement heuristics can lead to suboptimal server utilization, because they cannot consider virtual machines, which arrive in the future.
This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. Previous article in issue. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment.
Simple bin packing heuristics have been analyzed and used to place virtual machines upon arrival. 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.
The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging.
Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements SLAs and multiple resource dimensions. 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.
Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. We specifically addressed two crucial data center operations. 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.
Third, we presented an approach to optimal VM sizing by employing the performance models we created.
The benefits of accurate application performance modeling are multifold. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center.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.
Fuzzy logic functions are used to check each overbooking decisions and estimate it.
Changing the acceptable level of risk is depending on the current. — Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. Improving consolidation of virtual machines with risk-aware bandwidth oversubscription in compute clouds An Autonomic Approach to Risk-Aware Data Center Overbooking.
Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center. The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important.
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. Library of Congress Cataloging-in-Publication Data Lewis, Leslie.
Improving the Army planning, programming, budgeting, and execution system (PPBES): the programming phase / Leslie might improve its resource allocation process and better justify its balanced against its supply of available resources.
The Arroyo Center.Download