Interpretación de los resultados de la editorial Santillana

In spite of the progress made in cloud computing, there remain a number of important open issues as follows:

1. Resource demand prediction. Since the process of cloud resource

allocation takes time to complete, it will be too late to prevent the QoS degradation if the resource reallocation is only carried out when

0.04 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 50 70 90 110 130 150

0.035 Optimal allocation scheme

Equal allocation scheme

Optimal allocation scheme Equal allocation scheme 0.03 0.025 0.02 Me an se rv ice resp onse time (s ec onds) Resource cost ( dollars) 0.015 0.01 0.005 50 70 (a) (b) 90 110

Arrival rate λ (requests/second) Arrival rate λ (requests/second)

130 150

FIGURE 2.5 Simulation results between the proposed optimal allocation scheme

and the equal allocation scheme in the priority service case: (a) Comparison of mean service response time and (b) comparison of resource cost.

resources become insufficient. Therefore, an accurate resource demand prediction model is required to forecast the resource demand in the near-term future based on the previous statistics.

2. Workload monitoring. The workload in cloud is changing in real

time. To allocate resources to satisfy the dynamic workload, espe- cially the burst of requests, a live workload monitoring is needed for cloud providers. In addition, it is a challenge to dynamically allocate the cloud resources to handle the time-varying workload.

3. Workload scheduling. There are two levels of scheduling in cloud

computing. The first level is the user-level scheduling, in which the requests for one application are distributed to different VMs according to the current workload. By balancing workload among the VMs, the user-level scheduling can effectively avoid episodic congestions in the cloud. Compared to the user-level scheduling, the task-level scheduling performs in a finer granularity. An appli- cation can be decomposed into a set of tasks, each of which requires different resources. The task-level scheduling is to assign different tasks to different VMs so that the performance can be maximized.

4. Resource migration. With current techniques, VMs and application

migrations have been implemented in the local area network (LAN) environment. In the future, cloud should be able to migrate VMs and services to other clouds, which can greatly improve the robustness of cloud data center.

5. Joint resource optimization. Currently, most of the resource opti-

mization methods focus only on the cloud side, while ignoring the transmission path and the user side. In fact, it is a challenging task to maximize or minimize an end-to-end QoS metric by jointly opti- mizing the resources in the cloud, at the client, and along the trans- mission path between the cloud and the client.

2.5 SUMMARY

Multimedia cloud computing, as a specific cloud computing model, focuses on how cloud can effectively provide multimedia services and guarantee QoS provisioning. Optimal resource allocation in multimedia cloud com- puting can greatly improve the performance for multimedia applications. In this chapter, we investigate the optimal resource allocation for multi- media cloud computing. We first provide a review of recent advances on

cloud-based multimedia services and resource allocation in cloud comput- ing. We then present a queuing model-based resource optimization scheme for multimedia cloud computing. Finally, the future research directions in the area of multimedia cloud computing have been discussed.

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