Climatización con Materiales de Cambio de Fase (PCM)

There are several resource-allocation strategies and approaches in the MCC system that provide higher level of QoS and ensure SLAs. Still, there are some areas that need more focus.

6.4.1 Energy-Aware Memory Management

The processing and memory units are the heart and the soul of a cloud system. CPUs consume the highest amount of energy in a cloud, and memory is the second highest power-consuming unit. Day by day, the uses of cloud are increasing rapidly, and memory and CPUs are also increasing. So, this has resulted in huge power consumption. Multi-core processors are very energy efficient. On the other hand, memory technologies have not shown energy efficiency [9]. Data centers have the same problem in networking and disk storage.

6.4.2 Maintaining Strict Service-Level Agreements (SLAs)

SLAs play a vital role in maintaining the QoS of a cloud system. A strict SLA always tries to avoid performance degradation, which is a very difficult job. Many questions arise here [9]: How to predict performance pick? How to determine which VMs, when, and where should be migrated to prevent performance degradation if multiple system resources are consid- ered? How to develop fast and effective algorithms for the VM placement optimization across multiple resources for large-scale systems? These questions have to be answered.

6.4.3 Merging of Different Resource-Allocation Strategies

Many resource-allocation strategies have been developed. But every time they cannot opti- mize each and every field. For example, the activity-based resource-allocation strategy [6] performs task scheduling very well, but it does not consider the energy efficiency factor.

The modified best-fit decreasing approach does not consider the priority factor of tasks [9]. So by merging different approaches, a positive tradeoff between performance and energy efficiency of the system can be made.

6.5 Conclusion

Resource allocation is one of the main operational issues in an MCC environment. After the task is offloaded from the mobile device into the cloud, the cloud provider allocates the task with the desired resources. In this chapter, we discussed the significance, strategies, and future challenges of resource allocation with the comparison of different approaches. We wish to modify the difficulties of the present approaches and overcome all the chal- lenges of resource-allocation strategies to maintain proper QoS of the MCC system.

Questions

1. What is resource allocation in MCC?

2. What is the significance of resource allocation in MCC? 3. Describe the different resource-allocation strategies in MCC. 4. Discuss the SMDP-based resource allocation in MCC. 5. SMDP or greedy approach: which one is better and why?

6. How activity-based algorithm is used to make task scheduling optimal? 7. Describe different resource-allocation strategies using the middleware.

8. Explain resource allocation in MCC using the entropy-based FIFO method with a suit- able example.

9. Explain the resource-allocation strategy using the auction method.

10. What is a virtual machine? How VM allocation is done in energy-aware data center resource allocation?

References

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minimization in mobile cloud computing system, in Proceedings of the ACM/IEEE International

Symposium on Low Power Electronics and Design, Rome, Italy, pp. 279–284, 2012.

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4. R. Patel and S. Patel, Survey on resource allocation strategies in cloud computing, International

Journal of Engineering Research & Technology, 2(2), 1–5, 2013.

5. H. Liang, L. X. Cai, D. Huang, X. Shen, and D. Peng, An SMDP-based service model for interdo- main resource allocation in mobile cloud networks, IEEE Transactions on Vehicular Technology, 61(5), 2222–2232, 2012.

6. L. S. V. Sing and J. Ahmed, A greedy algorithm for task scheduling & resource allocation problems in cloud computing, International Journal of Research & Development in Technology and

7. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, The case for VM-based cloudlets in mobile computing, IEEE Pervasive Computing, 8(4), 14–23, 2009.

8. M. Ferber, T. Rauber, M. H. C. Torres, and T. Holvoet, Resource allocation for cloud-assisted mobile applications, in IEEE Fifth International Conference on Cloud Computing, Honolulu, HI, pp. 400–407, 2012.

9. A. Beloglazov, J. Abawajy, and R. Buyya, Energy-aware resource allocation heuristics for effi- cient management of data centers for cloud computing, Future Generation Computer Systems, 28(5), 755–768, 2012.

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7

Sensor Mobile Cloud Computing

Cloud computing layer End user layer Mobile devices layer

Wireless sensor network

1. Sensor sensing data from the environment

2. Data is sent to mobile devices via wireless connections

3. Mobile devices send the data to the cloud

4. End user accesses data from the cloud

ABSTRACT Sensor mobile cloud computing (SMCC) is an emerging research area today. It is an integration of wireless sensor network with mobile cloud computing (MCC). In this chapter, we study the architecture and applications of SMCC. A life cycle model of this architecture is developed. Different challenges of SMCC are also discussed.

KEY WORDS: wireless sensor network, mobile cloud computing, sensor cloud, urban sensing.

7.1 Introduction

Wireless sensor networks (WSNs) have been gaining much attention, from both commercial and technical points of view, because of their potential for providing attractive solutions in areas such as health care, industrial automation, asset management, environmental moni- toring, transportation business, and so on. Limited processing power, battery life, and communication speed are the main problems of WSN [1]. Cloud computing provides new opportunities in aggregating sensor data and exploiting the aggregates for greater cover- age and relevancy and provides scalable processing power. Cloud computing is becoming increasingly pervasive in our daily lives. Its increasing popularity in distributed comput- ing environment is influencing the trend of using cloud environment for storage and data processing. The rapid growth of sensor network and cloud computing technology has led to the emergence of a new platform called sensor clouds. It integrates WSN with the data center model of cloud computing [2]. The primary goal of a sensor cloud is to facilitate con- necting sensors and software objects to build community-centric sensing applications. To explore this sensor, data of all types will drive the need for an increasing capability to do analysis and mining on the cloud [3]. One of the applications of sensor cloud computing is doctors’ virtual community, where various sensors and cloud computing technologies are used for monitoring health of patients.

Cloud computing is a real paradigm that provides applications and services that are executed on distributed networks using virtualized resources accessed by the Internet protocol. Cloud provides software as a service (SaaS), where software is deployed in the cloud in such a way that users can access the software through the Internet. This elimi- nates the need to install the software. Cloud services is a layer of the cloud computing stack, which includes software components running in a distributed fashion across the commercial Internet [4].

To extend the services of a sensor cloud, mobile devices can be integrated with it and this infrastructure is known as sensor mobile cloud computing (SMCC) [5]. In this scheme, the sensor data are sent to the cloud through the mobile devices. Because of the incorporation of mobile devices, communication becomes more real time and pervasive than the basic sensor–cloud communication. One of such SMCC applications is mobile health monitor- ing, which is used for monitoring patient health remotely.