Approximation Algorithms for Demand-Response Contract Execution and Coflow Scheduling

Edición de la obra Approximation Algorithms for Demand-Response Contract Execution and Coflow Scheduling

Solving operations research problems with approximation algorithms has been an important topic since approximation algorithm can provide near-optimal solutions to NP-hard problems while achieving computational efficiency. In this thesis, we consider two different problems in the field of optimal control and scheduling theory respectively and develop efficient approximation algorithms for those problems with performance guarantee. Chapter 2 presents approximation algorithms for solving the optimal execution problem for demand-response contract in electricity markets. Demand side participation is essential for achieving real-time energy balance in today's electricity grid. Demand-response contracts, where an electric utility company buys options from consumers to reduce their load in the future, are an important tool to increase demand-side participation. In this chapter, we consider the operational problem of optimally exercising the available contracts over the planning horizon such that the total cost to satisfy the demand is minimized. In particular, we consider the objective of minimizing the sum of the expected ℓ_β-norm of the load deviations from given thresholds and the contract execution costs over the planning horizon. For β=∞, this reduces to minimizing the expected peak load. The peak load provides a good proxy to the total cost of the utility as spikes in electricity prices are observed only in peak load periods. We present a data driven near-optimal algorithm for the contract execution problem. Our algorithm is a sample average approximation (SAA) based dynamic program over a multi-period planning horizon. We provide a sample complexity bound on the number of demand samples required to compute a (1+ε)-approximate policy for any ε>0. Our SAA algorithm is quite general and we show that it can be adapted to quite general demand models including Markovian demands and objective functions. For the special case where the demand in each period is i.i.d., we show that a static solution is optimal for the dynamic problem. We also conduct a numerical study to compare the performance of our SAA based DP algorithm. Our numerical experiments show that we can achieve a (1+ε)-approximation in significantly smaller numbers of samples than what is implied by the theoretical bounds. Moreover, the structure of the approximate policy also shows that it can be well approximated by a simple affine function of the state. In Chapter 3, we study the NP-hard coflow scheduling problem and develop a polynomial-time approximation algorithm for the problem with constant approximation ratio. Communications in datacenter jobs (such as the shuffle operations in MapReduce applications) often involve many parallel flows, which may be processed simultaneously. This highly parallel structure presents new scheduling challenges in optimizing job-level performance objectives in data centers. Chowdhury and Stoica [13] introduced the coflow abstraction to capture these communication patterns, and recently Chowdhury et al. [15] developed effective heuristics to schedule coflows. In this chapter, we consider the problem of efficiently scheduling coflows so as to minimize the total weighted completion time, which has been shown to be strongly NP-hard [15]. Our main result is the first polynomial-time deterministic approximation algorithm for this problem, with an approximation ratio of $64/3$, and a randomized version of the algorithm, with a ratio of 8+16sqrt{2}/3. Our results use techniques from both combinatorial scheduling and matching theory, and rely on a clever grouping of coflows. In Chapter 4, we carry out a comprehensive experimental analysis on a Facebook trace and extensive simulated instances to evaluate the practical performance of several algorithms for coflow scheduling, including our approximation algorithms developed in Chapter 3. Our experiments suggest that simple algorithms provide effective approximations of the optimal, and that the performance of the approximation algorithm of Chapter 3 is relatively robust, near optimal, and always among the best compared with the other algorithms, in both the offline and online settings.