Measuring robustness of computing systems

System builders are becoming increasingly interested in robust design. We believe that a methodology for generating robustness metrics will help the robust design research efforts and, in general, is an important step in the efforts to create robust computing systems. The purpose of the research in this paper is to quantify the robustness of a resource allocation, with the eventual objective of setting a standard that could easily be instantiated for a particular computing system to generate a robustness metric. The paper exposes how not considering the uncertainties can result in gross overestimation of the system capacity, and shows a method for reducing the impact of uncertainty, and even use it to our advantage if the uncertainties are correlated. We present our theoretical foundation for a robustness metric and give its instantiation for a particular system.     

Dynamic Channel Allocation in Wireless Networks Using Adaptive Learning Automata

Single-channel based wireless networks have limited bandwidth and throughput and the bandwidth utilization decreases with increased number of users. To mitigate this problem, simultaneous transmission on multiple channels is considered as an option. In this paper, we propose a distributed dynamic channel allocation scheme using adaptive learning automata for wireless networks whose nodes are equipped with single-radio interfaces. The proposed scheme, Adaptive Pursuit learning automata runs periodically on the nodes, and adaptively finds the suitable channel allocation in order to attain a desired performance. A novel performance index, which takes into account the throughput and the energy consumption, is considered. The proposed learning scheme adapts the probabilities of selecting each channel as a function of the error in the performance index at each step. The extensive simulation results in static and mobile environments provide that the proposed channel allocation schemes in the multiple channel wireless networks significantly improves the throughput, drop rate, energy consumption per packet and fairness index—compared to the 802.11 single-channel, and 802.11 with randomly allocated multiple channels. Also, it was demonstrated that the Adaptive Pursuit Reward-Only (PRO) scheme guarantees updating the probability of the channel selection for all the links—even the links whose current channel allocations do not provide a satisfactory performance—thereby reducing the frequent channel switching of the links that cannot achieve the desired performance.     

Zone-based load balancing in two-tier heterogeneous cellular networks: a game theoretic approach

Telecommunication Systems - In this paper, load balancing in two-tier cellular networks is investigated. The network under-study is divided into several zones. The first tier of each zone includes...     

A case for on-machine load balancing

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional “across-the-machines” load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times.We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.