Cloud resource management .1 Comparison with grid systems.1Comparison with grid systems

This section aims to compare the difference of resource management be-tween grids and clouds. The first one is the business model for cloud system regards to the consumption! Like electricity, water or gas, the customer pays to the resource owner according to the amount consumed. On the contrary, grids have the project-oriented business model. The proposal represents the users who have a certain number of service units they can spend.

Future of grids resources management 135 On the perspective of the computing model, the grids always use a local resource manager to manage the computing resources for a grid site, while the users submit jobs to request some resources for some time [Yu and Magoul`es, 2009 ]. On the contrary, clouds share all the resources by all the users at the same time; that is to say, some low-latency applications can easily operate on clouds, which is not the case on grids.

Finally, the combination of the computing and data resource management is important. It is more efficient to schedule computational tasks close to the data, and to understand the costs of moving the work as opposed to moving the data [Chervenak et al., 2000 ]. Data-aware schedulers and dispersing data close to processors is critical in achieving good scalability and performance.

5.5.2 Resource model

The resource model is related to the question: “How to describe and manage resources in the system?” In the original resource model, the operation and data usually have two kinds of relations. One approach is related to data that comprise a resource and which are described in a specific description language along with some integrity constraints. One other approach treats operation on the resources as a part of the resource model. However in the cloud computing, because of the high virtualization of the resource and interacting operation of data, the operation and data must be considered as a whole part. Thus, a new resource model is proposed as represented in Figure 5.4.

FIGURE 5.4: Resource model in clouds.

136 Fundamentals of Grid Computing

All the clouds users share resources to support the interactive applications.

These applications are limited in grids because of the expensive scheduling decisions, data moving and potentially long queue times [Foster et al., 2008 ]. That is to say, computing will be centralized, when storage, operation and other kinds of resources are provisioned by clouds.

At the same time, local computing coexists with the cloud computing, and can be communicated and converted to each other if necessary. There are three reasons why we can’t ignore the local operation. The first one is the Internet limitation. Even network technology is extremely developed today;

some users can’t access to the Internet anytime, or even won’t suffer on the line all the time. So we must consider this situation in which the users prefer to finish their work when clouds are down. The second is related to the security consideration for some users, especially commercial enterprises, who don’t want to run their security, critical tasks on the common clouds and send their sensitive data to the cloud storage. The third is related to some large-size companies well equipped with software and hardware, able to handle their data internally, which will be more effective than computing on clouds.

Virtual data in the center storage can be requested without regard to data location which considers data transparent to the users. The center data can either be computed in the clouds, or transferred to the special request. How-ever, frequently staging data in and out to distant computers will slow down the computing speed. Besides, the speed difference of I/O and local disc to network storage can affect application performance. So we apply the local data which is most closed to the computing jobs as the solution to overcome the transmission problem. In summary, the resource model combines the data control and computing operation to minimize the amount of data movement and improve the end-application ability.

5.5.3 Economy-oriented model

Compared with other computing paradigm, cloud computing is more com-mercial and promises to deliver services on subscription-basis in a pay-as-you-go model. The traditional resource management always focuses on maximizing the throughput and minimizing the mean waiting time, but seldom includes important factors in the market such as the fair access to the resources [Yeo and Buyya, 2006 ], [Calheiros et al., 2009 ]. So we must orient economic mod-els to enable on-demand trading of services and support customers buying the computing service like other utilities. The economy-oriented computing model is shown in Figure 5.5.

At the provider end, the lowest level implies numerous physical machines including all kinds of servers. The second layer contains virtual machines which utilize the physical machines to meet the customer’s service request dramatically. Each virtual machine is isolated from each other on the same physical machine. Cloud services are abstracted as the actual applications operate on the highest level of the provider.

Future of grids resources management 137

FIGURE 5.5: Economy-oriented model.

At the user-end, enterprise or individual users submit their service request and quality of service parameters from anywhere. It only waits for the re-ply of process from cloud provider, rather than directly deals with multiple heterogeneous providers.

Between provider and user, a broker bridges the two entities. The broker is equipped with a negotiation module that is informed by the current condi-tions of resource and current demands of user, so that it can help the users find a resource which meet its quality of service, and choose the user whose application can provide it maximum utility. So the broker will gain by the price difference between the user and provider. Furthermore, the broker will aggregate more jobs such as reserving resource slots, scheduling services and performing admission control to avoid overload.