Control of a tandem queue with a startup cost for the second server

Various systems across a broad range of applications contain tandem queues. Strong dependence between the servers has proven to make such networks complicated and difficult to study. Exact analysis is rarely computationally tractable and sometimes not even possible. Nevertheless, as it is most often the case in reality, there are costs associated with running such systems, and therefore, optimizing the control of tandem queues is of main interest from both a theoretical and a practical point of view. Motivated by this, the present paper considers a tandem queueing network with linear holding costs and a startup cost for the second server. In our work, we present a rather intuitive, easy to understand, and at the same time very accurate technique to approximate the optimal decision policy. Extensive numerical experimentation shows that the approximation works extremely well for a wide range of parameter combinations

Flow termination signaling in the centralized pre-congestion notification architecture

Pre-congestion notification (PCN) protects inelastic traffic by using feedback on network link loads on and acting upon this accordingly. These actions comprise to admission control and termination of flows. Two PCN architectures have been defined by IETF: the centralized and decentralized PCN architecture. The decentralized PCN architecture has received much attention in the literature whereas the centralized PCN architecture has not. In the decentralized architecture, feedback is sent from the egress nodes to ingress nodes, which then take and apply decisions regarding admission of new flows and/or termination of ongoing flows. Signaling occurs only between ingress and egress nodes. In the centralized architecture these decisions are made at a central node, which requires proper signaling for action and information exchange between the central node and the egress and ingress nodes. This signaling has been suggested by other authors, but is not fully defined yet. Our contribution is twofold. We define signaling in the centralized PCN architecture focussing on flow termination, which completes the definition of the signaling in the centralized PCN architecture. Secondly, we run extensive simulations showing that the proposed signaling works well and that the performances of the centralized PCN and the decentralized PCN architectures are similar. Hence, it is expected that results from existing research on the effectiveness of decentralized PCN are also valid when the centralized PCN architecture is used

Changes in Optical Conductivity due to Readjustments in Electronic Density of States

Within the model of elastic impurity scattering, we study how changes in the energy dependence of the electronic density of states (EDOS) $N(\epsilon)$ around the Fermi energy $\epsilon_F$ are reflected in the frequency-dependent optical conductivity $\sigma(\omega)$. While conserving the total number of states in $N(\epsilon)$ we compute the induced changes in $\sigma(\omega)$ as a function of $\omega$ and in the corresponding optical scattering rate $1/\tau_{\rm op}(\omega)$. These quantities mirror some aspects of the EDOS changes but the relationship is not direct. Conservation of optical oscillator strength is found not to hold, and there is no sum rule on the optical scattering rate although one does hold for the quasiparticle scattering. Temperature as well as increases in impurity scattering lead to additional changes in optical properties not seen in the constant EDOS case. These effects have their origin in an averaging of the EDOS around the Fermi energy $\epsilon_F$ on an energy scale set by the impurity scattering.Comment: 13 pages, 7 figure

Improving mobile video quality through predictive channel quality based buffering

Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources