Energy-aware optimization of scalable load balancing strategiesQueuing systems are one of the most prominent technological evolution in today's digital world. Data centers and cloud networks operated by large companies like Microsoft, Google and Amazon rely on large-scale queuing systems for their operations. The goal of minimizing energy consumption while optimizing performance has been the focus of recent literature around the subject. In this thesis, we consider the TABS scheme and investigate through simulations and a theoretical proof the implications of various parameters of the scheme on a variant of the decentralized parallel queuing system. Simulation results explore how changing the assumption of exponential service time distributions affects various performance metrics. Our theoretical result builds on previous results in order to identify patterns in fluctuations around mean behaviour of the system over long time intervals.Chandak, RajitacreatorRamanan, Kavitathesis advisorHarrison, MatthewreaderBrown University. Applied Mathematicssponsor2019textborn digitalEnglishSenior thesis (ScB)--Brown University, 2019Applied Mathematics and EconomicsthesesQueuing theoryQueuing theory--Simulation methodsMartingales (Mathematics)Limit theorems (Probability theory)10.26300/jjv6-az91In CopyrightCollection is open for research.