The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.

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Today, cloud-based services and applications are ubiquitous in many systems. The cloud provides undeniable potential benefits to the users by offering lower costs and simpler deployment. The users significantly reduce their system management responsibilities by outsourcing services to the cloud service providers. However, the management shift has posed significant security challenges to the cloud service providers. Security concerns are the main reasons that delay organizations from moving to the cloud. The security and efficiency of user identity management and access control in the cloud needs to be well addressed to realize the power of the cloud. In this chapter, the authors identify the key challenges and provide solutions to the authentication and identity management for secure cloud business and services. The authors first identify and discuss the challenges and requirements of the authentication and identity management system in the cloud. Several prevailing industry standards and protocols for authentication and access control in cloud environments are provided and discussed. The authors then present and discuss the latest advances in authentication and identity management in cloud, especially for mobile cloud computing and identity as a service. They further discuss how proximity-based access control can be applied for an effective and fine-grained data access control in the cloud.

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One important factor restricting the capacity of IPTV service over vehicular-to-infrastructure (V2I) networks is the limited radio resource reserved on the road-side-units (RSUs). This paper elaborates a hybrid video transmission scheme to improve the channel availability in vehicular IPTV systems. The advanced scalable video coding (SVC) technique is applied to encode TV channels. SVC layers are transmitted in different modulation and coding schemes (MCSs), so as to provide differentiated robustness and resource utility efficiency. The hybrid transmission scheme intelligently delivers SVC layers to vehicles via either pure V2I or inter-vehicle relay connections. Comprehensive simulation experiments are conducted and show that, compared to the legacy V2I transmission scheme, the proposed hybrid scheme can effectively enhance user quality of experience (QoE) by significantly increasing channel availability, with only slightly deteriorating the transmission delay for the enhancement layers.