Resource Management
2.6 Summary
Although the VM live migration within a data center has been extensively studied, inter-data center VM migration in a federated cloud needs more investigation due to the diversity in network bandwidth and data transfer prices. An accurate model to characterize the performance of migration is required. Moreover, most of the works consider migrating homogeneous VMs, which is not realistic.
2.6 Summary
In this chapter, we discussed the architecture of a federated cloud data center and the various components associated with it. We also provided a brief overview of VCC. We discussed the concepts of virtualization and the various migration techniques used. We discussed resource management in cloud data centers and presented a comprehensive taxonomy of resource management problems. Finally, we discussed the state-of-art literature review of the problems addressed in the thesis. In the next chapter, we describe the proposed hierarchical resource management framework and discuss the problem of placing the data-intensive applications in a federated cloud.
<<=8=;;
3
C h a p t e r
A Cost-aware Management Framework for Placement of Data-intensive Applications on Federated Cloud
A
s discussed in Chapter 1, the data-intensive applications are typically deployed in cloud data centers using two types of VCs−DBs as storage nodes and VMs as compute nodes.However, modern big data analysis and business intelligence applications come with high resource demands, along with a requirement for massive data transfer between storage and compute nodes. Due to the large volume of data to be analyzed, dealing with data-intensive applications is a major challenge for the cloud provider as the available resources of a single data center might not be sufficient to host the VCs of an application. Federated cloud systems offer a promising solution for handling such applications in a scalable manner, while avoiding the cost associated with having large infrastructure [30]. The objective of each cloud provider in a federation is to maximize its own profit and to minimize its TCO. The large resources of a data-intensive application consume high amount of energy resulting in large electricity bill. Further, distributing the VCs of an application across different data centers may lead to huge inter-data center network traffic and that increases the cost.
Typically, the workload assignment in cloud data centers is handled in a centralized manner, which is not scalable and compatible with the federation requirements [124].
Motivation: This work is motivated by the following observations. First, existing cloud management approaches do not consider the semi-autonomous requirements of the federated system. Second, data-intensive applications require a large amount of resources that could
span across multiple data centers, and in this case exploiting federation is a good choice for small cloud providers. Third, cloud providers are mainly interested in maximizing their profit by minimizing their TCO. Energy consumption cost contributes a significant fraction of TCO of the cloud provider. It could be reduced by taking advantage of the electricity price and PUE diversity across the data centers of the federation. In addition, data-intensive applications may trigger large data transfer over WAN, wherein the bandwidth is limited and expensive. Thus, inter-data center communication cost is another critical cost component in data-intensive applications where it could surpass the cost of the energy consumption. That requires maintaining data locality to minimize inter-data center traffic by carefully allocating highly-communicating virtual components together. In the literature, resource allocation usually considers the placement of homogeneous VMs in isolation, which is not suitable when heterogeneous VMs collaborate in an application. Further, most of the works either optimize the energy consumption or the network bandwidth while allocating the resources.
However, the cloud provider should minimize the energy consumption and bandwidth costs together while allocating the VCs of a data-intensive application to reduce its TCO.
To deal with these issues, we design a hierarchical framework for resource management and workload allocation considering the features of the federated cloud. Using this framework, we solve the problem of cost-aware deployment of data-intensive applications in federated clouds. We propose an optimization model with the objective of minimizing the TCO including energy consumption cost and bandwidth cost. We propose an efficient algorithm for placement of data-intensive applications in a federated cloud. The proposed algorithm considers the correlation of virtual components to reduce inter-data center communication and bandwidth cost, and it leverages the electricity price and PUE variations across data centers of the federation to minimize the cost of energy consumption. The proposed algorithm is evaluated with various scenarios to demonstrate the benefits of federation.
Organization of the Chapter: The remainder of this chapter is organized as follows. We first present the hierarchical framework for resource management and workload distribution for federated clouds in Section 3.1. The system model and the problem formulation are presented in Section 3.2. We describe the proposed algorithm to solve the problem, and discuss its complexity and running cost in Section 3.3. Simulation results are reported in Section 3.4 followed by the summary of the chapter in Section 3.5.
3. Placement of Data-intensive Applications on Federated Cloud