Twisted two-dimensional bilayer materials exhibit many exotic physical phenomena. Manipulating the twist angle between the two layers enables fine control of the physical structure, resulting in development of many novel physics, such as the magic-an
gle flat-band superconductivity, the formation of moire exciton and interlayer magnetism. Here, combined with analogous principles, we study theoretically the near-field radiative heat transfer (NFRHT) between two twisted hyperbolic systems. This two twisted hyperbolic systems are mirror images of each other. Each twisted hyperbolic system is composed of two graphene gratings, where there is an angle {phi} between this two graphene gratings. By analyzing the photonic transmission coefficient as well as the plasmon dispersion relation of twisted hyperbolic system, we prove that the topological transitions of the surface state at a special angle (from open (hyperbolic) to closed (elliptical) contours) can modulate efficiently the radiative heat transfer. Meanwhile the role of the thickness of dielectric spacer and vacuum gap on the manipulating the topological transitions of the surface state and the NFRHT are also discussed. We predict the hysteresis effect of topological transitions at a larger vacuum gap, and demonstrate that as thickness of dielectric spacer increase, the transition from the enhancement effect of heat transfer caused by the twisted hyperbolic system to a suppression. This technology could novel mechanism and control method for NFRHT, and may open a promising pathway for highly efficient thermal management, energy harvesting, and subwavelength thermal imaging.
Many cloud service providers (CSPs) provide on-demand service at a price with a small delay. We propose a QoS-differentiated model where multiple SLAs deliver both on-demand service for latency-critical users and delayed services for delay-tolerant u
sers at lower prices. Two architectures are considered to fulfill SLAs. The first is based on priority queues. The second simply separates servers into multiple modules, each for one SLA. As an ecosystem, we show that the proposed framework is dominant-strategy incentive compatible. Although the first architecture appears more prevalent in the literature, we prove the superiority of the second architecture, under which we further leverage queueing theory to determine the optimal SLA delays and prices. Finally, the viability of the proposed framework is validated through numerical comparison with the on-demand service and it exhibits a revenue improvement in excess of 200%. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.
Cloud computing delivers value to users by facilitating their access to computing capacity in periods when their need arises. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on d
emand at a fixed price and users occupy different periods of servers. The latter allows users to bid for the remaining unoccupied periods via dynamic pricing; however, without appropriate design, such periods may be arbitrarily small since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this paper, we provide the first integral framework for sharing the time of servers between on-demand and spot services while optimally pricing spot instances. It guarantees that on-demand users can get served quickly while spot users can stably utilize servers for a properly long period once accepted, which is a key feature to make both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 464.7%. The framework is designed under assumptions which are met in real environments. It is a new tool that cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, eventually making the case for operating such service model in their own infrastructures.
A fundamental goal in the design of IaaS service is to enable both user-friendly and cost-effective service access, while attaining high resource efficiency for revenue maximization. QoS differentiation is an important lens to achieve this design goa
l. In this paper, we propose the first analytical QoS-differentiated resource management and pricing architecture in the cloud computing context; here, a cloud service provider (CSP) offers a portfolio of SLAs. In order to maximize the CSPs revenue, we address two technical questions: (1) how to set the SLA prices so as to direct users to the SLAs best fitting their needs, and, (2) determining how many servers should be assigned to each SLA, and which users and how many of their jobs are admitted to be served. We propose optimal schemes to jointly determine SLA-based prices and perform capacity planning in polynomial time. Our pricing model retains high usability at the customers end. Compared with standard usage-based pricing schemes, numerical results show that the proposed scheme can improve the revenue by up to a five-fold increase.
