With a growing concern on the considerable energy consumed by data centers, research efforts are targeting toward green data centers with higher energy efficiency. In particular, server virtualization is emerging as the prominent approach to consolidate applications from multiple applications to one server, with an objective to save energy usage. However, little understanding has been obtained about the potential overhead in energy consumption and the throughput reduction for virtualized servers in data centers. In this research, we take the initiative to characterize the energy usage on virtualized servers. An empirical approach is adopted to investigate how server virtualization affects the energy usage in physical servers. Through intensive data collection and analysis, we identify a fundamental trade-off between the energy saving from server consolidation and the detrimental effects (e.g., energy overhead and throughput reduction) from server virtualization. This characterization lays a mathematical foundation for server consolidation in green data center architecture.
Aiming at the problem of local path planning for structured roads, this paper proposes a framework of local path-speed planning for autonomous driving, which takes safety as the premise and improves driving efficiency. The framework simulates human driving thinking and divides the local path planning of autonomous driving into two parts: lane decision and path-speed planning. In the part of the lane decision, a lane decision algorithm based on driving risk field and safe distance is proposed, which can ensure driving efficient and ensure that the planning vehicle is always in a low-risk driving environment. In the part of the lane change path-speed planning, a candidate path generation algorithm based on uniform sampling of lane change time and a cost function considering lane change timeliness, driving safety, speed smoothness, and path continuity are proposed to achieve optimal path selection and speed planning. In the experiment part, there are six different driving tasks. In six scenes, the local path-speed planning framework proposed in this paper can plan a safe, efficient, and smooth driving path and a safe planning speed. Taking the scenario of detouring low-speed obstacles as an example, the path-speed planning algorithm proposed is compared with the path-speed planning algorithm based on discrete optimization in Hu et al. It has been verified that the algorithm proposed can ensure that planner is always at low environmental risks and drive with high driving efficiency.
The emergence of multi-screen cloud social TV has the potential to transform TV experience, providing a unified media experience across a diverse set of devices at an affordable cost. One key technology to support unified media experience across multiple screens is to instantiate a virtual machine (VM) as a cloud clone of the user, to manage all his/her media outlets (e.g., TV and smartphone), as implemented in our Cloud-Centric Media Network (CCMN). In this case, as the user shifts his attention from one device to another, the cloud clone can migrate to another location for better quality of experience. In this paper, we investigates the problem of cloud-clone migration for the multi-screen social TV application, minimizing its monetary cost. This problem can be cast into the Markov Decision Process (MDP) framework, to balance a trade-off between the migration cost and the transmission cost. Under this framework, we first derive an upper and lower bound for the optimal monetary cost, by considering a fixed placement policy and an offline policy. We then follow up with an online policy using a dynamic programming approach. Our numerical results indicate, up to 10% monetary cost can be saved, by optimally migrating the cloud clone. Moreover, the cost reduction depends on the length of content-delivery path, the data size associated with VM migration, and the user behavior pattern. These insights would offer operational guidelines to deliver cost effective multi-screen social TV services over CCMN, potentially easing its adoption.
This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based Software Defined Wireless Networking (cloud-based SDWN). Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking
Customer Relationship Management (CRM) system improves companies' profitability by helping companies focus on the relationships with customers, colleagues or suppliers. By having strong initiative to move applications to cloud, enterprises are hindered by cloud security and reliability issues [1], especially when it comes to financial industries. To provide a practical and secure solution to these enterprises, this project aims to build a cloud CRM system that enables fully homomorphic encryption. In order to explore the potential of this, the project integrates three key components: Open source CRM system Sugar CRM, partial homomorphic database system Crypt DB and fully homomorphic encryption library HElib. By leveraging the structure based on our previous work [2], Stealthy CRM successfully integrates fully homomorphic encryption support on top of Crypt DB database encryption environment. Besides that, Stealthy CRM enables a transparent and seamless integration to any CRM system by using a modified My SQL proxy to listen to, encrypt the queries and interact with Crypt DB and HElib subsystems. An evaluation of TPC-C and TPC-H queries is conducted on Stealthy CRM system. The result shows Stealthy CRM has 14%-28% throughput overhead for most of the CRM queries, compared with unmodified My SQL server. For complex TPC-H queries involving multiplication and composition of computation, Stealthy CRM is able to execute the query between 1.75 min to 11.7 min. Although the time takes to complete a fully homomorphic query in CRM system is still long, Stealthy CRM provided a prototype for researchers and other business application developers to explore the potential.
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