Asymptotically optimal load balancing in large-scale heterogeneous systems with multiple dispatchers

We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information

The health queuing game

This paper studies agent-to-agent games in competition for a free public resource. The resource is not evidently scarce, scarcity may, however, be the equilibrium outcome. The agents' attributes may di er as well as quality parameters between di erent public resources. The examples are taken from the health sector. Based on simple two-player simultaneous games of complete information, results regarding agent equilibrium quality choices are derived. Most notably, Nash equilibria of the type: very ill patients choose low quality hospitals (denoted adverse patient allocation in the paper) are demonstrated. Furthermore, it is argued that a situation characterized by patients with relatively mild diseases but large patient variability (big di erences between patients regarding the given disease) and a health system with medium competition are prime candidates for Nash equilibria characterized by such Adverse patient allocation e ects

Partially shared buffers with full or mixed priority

This paper studies a finite-sized discrete-time two-class priority queue. Packets of both classes arrive according to a two-class discrete batch Markovian arrival process (2-DBMAP), taking into account the correlated nature of arrivals in heterogeneous telecommunication networks. The model incorporates time and space priority to provide different types of service to each class. One of both classes receives absolute time priority in order to minimize its delay. Space priority is implemented by the partial buffer sharing acceptance policy and can be provided to the class receiving time priority or to the other class. This choice gives rise to two different queueing models and this paper analyses both these models in a unified manner. Furthermore, the buffer finiteness and the use of space priority raise some issues on the order of arrivals in a slot. This paper does not assume that all arrivals from one class enter the queue before those of the other class. Instead, a string representation for sequences of arriving packets and a probability measure on the set of such strings are introduced. This naturally gives rise to the notion of intra-slot space priority. Performance of these queueing systems is then determined using matrix-analytic techniques. The numerical examples explore the range of service differentiation covered by both models

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity $c$ as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to $c = M + \alpha V$, where directly relates to the performance requirement (as agreed upon in a service level agreement) and $V$ reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.\ud As estimating $M$ is relatively straightforward, the remaining open issue concerns the estimation of $V$. We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of $V$ is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Energy-Delay Tradeoff and Dynamic Sleep Switching for Bluetooth-Like Body-Area Sensor Networks

Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people's health. This paper considers a system scenario relevant to such applications, where a smart-phone acts as a data-collecting hub, gathering data from a number of wireless-capable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors' energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless body-area sensor network at some desired point close to the optimal energy-delay tradeoff curve. This tradeoff curve is a function of the employed physical-layer protocol: in particular, it depends on the multiple-access scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widely-used Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energy-delay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control "NULL" packets necessary to maintain the connection alive, have a non-zero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.Comment: Extended version (with proofs details in the Appendix) of a paper accepted for publication on the IEEE Transactions on Communication

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship between the traffic offered (in terms of the mean offered load M, but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulae that estimate the required capacity C as a function of the input traffic and the performance target. For the special case of Gaussian input traffic these formulae reduce to C = M+V , where directly relates to the performance requirement (as agreed upon in a service level agreement) and V reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level the Gaussianity assumption is justified.\ud As estimating M is relatively straightforward, the remaining open issue concerns the estimation of V . We argue that, particularly if V corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of V is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Achieving Quality of Service in Group Scheduling in Cellular Networks

Title from PDF of title page, viewed on July 17, 2014Thesis advisor: Cory BeardVitaIncludes bibliographic references (pages 51-55)Thesis (M. S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014Cellular 3G/4G networks provide a wonderfully rich set of applications and social networking capabilities. From a QoS perspective, the traffic is basically divided into Real time and Non real time traffic which helps in scheduling priorities to the packets. With increase in need for QoS in the commercial networks, scheduling schemes such as MLWDF (Modified Largest Weighted Delay First) are playing a prominent role in deciding factors of packet selection. But most of these features are not available to public safety and emergency organizations where these organizations must use dedicated systems to obtain the reliability and protected performance that is needed. This research work provides a brief survey of the regulatory and commercial issues involved. The research work then provides solutions to give real-time and non-real-time traffic scheduling priorities to balance different requirements. We introduce the concept of a queue indicator that uses queue awareness to decide which traffic type to transmit. Then we introduce the concept of group scheduling that adds together scheduling metrics of different users within groups to decide which groups should transmit. These metrics are both opportunistic to take advantage of changing channel conditions and they are queue aware to adapt to traffic conditions. But the metrics are very simple so that scheduling mechanisms are practical and scalable for implementations. These are all evaluated through a detailed simulator (MATLAB-Simulator) that models long-term and short-term fading impacts. We find the best queue indicator values and then assess different cases where groups have various delay requirements. With the ever increasing number of users and the usage of data in cellular networks, meeting the expectations is a very difficult challenge. To add to the difficulties, the available resources are very limited, so proper management of these resources is very much needed. Scheduling is a key component and having a scheduling scheme which can meet the QoS requirements such as Throughput, Fairness and Delay is importantAbstract -- List of illustrations -- List of tables -- Acknowledgements -- Introduction -- Background -- Scheduler design and related work -- Matlab -- Results and analysis - Reference