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 Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates.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.

A novel implementation of backpressure algorithm in wireless ad hoc network

In the wireless networks, the routing technique is the one of the highest concern and it is the important procedure in the ad hoc networks. To aid this effort, we proposed a new valuation of backpressure appliances for wireless networks. By this proposed system, we will address numerous preparation and routing difficulties and also recover the throughput and delay that are essentially produced by the packets at the node transmission. The Backpressure routing is a dense and enlarged throughput for wireless networks, but endures improved delays. In routing, the backpressure algorithm is known to afford throughput optimality with active traffic. The significant supposition in the backpressure algorithm is that all nodes are kind and detect the algorithm rules leading the information conversation and principal optimization necessities. In the proposed system, we validate that how the node is steady at the backpressure algorithm routing and also by together easing the virtual trust line and the real package queue. The backpressure algorithm not only achieves flexibility, but also stands the throughput performance under safety attacks. This scheme is mostly enhances the node performance at the time of announcement and also it recovers the node security at the time of numerous threats in the wireless requests.

Stability of multiclass queueing networks under longest-queue and longest-dominating-queue scheduling

We consider the stability of robust scheduling policies for multiclass queueing networks. These are open networks with arbitrary routeing matrix and several disjoint groups of queues in which at most one queue can be served at a time. The arrival and potential service processes and routeing decisions at the queues are independent, stationary, and ergodic. A scheduling policy is called robust if it does not depend on the arrival and service rates nor on the routeing probabilities. A policy is called throughput-optimal if it makes the system stable whenever the parameters are such that the system can be stable. We propose two robust policies: longest-queue scheduling and a new policy called longest-dominating-queue scheduling. We show that longest-queue scheduling is throughput-optimal for two groups of two queues. We also prove the throughput-optimality of longest-dominating-queue scheduling when the network topology is acyclic, for an arbitrary number of groups and queues.

A Two-Dimensional Multiarmed Bandit Approach to Secondary Users with Network Coding in Cognitive Radio Networks

We study how to utilize network coding to improve the throughput of secondary users (SUs) in cognitive radio networks (CRNs) when the channel quality is unavailable at SUs. We use a two-dimensional multiarmed bandit (MAB) approach to solve the problem of SUs with network coding under unknown channel quality in CRNs. We analytically prove the asymptotical-throughput optimality of the proposed two-dimensional MAB algorithm. Simulation results show that our proposed algorithm achieves comparable throughput performance, compared to both the theoretical upper bound and the scheme assuming known channel quality information.