- Pasadena, California, United States
Niangjun Chen
California Institute of Technology, Computer Science, Graduate Student
- I am a PhD student in Computer Science at Caltech, and a member of Netlab and the Rigorous Systems Research Group (RS... moreI am a PhD student in Computer Science at Caltech, and a member of Netlab and the Rigorous Systems Research Group (RSRG), working with Professor Adam Wierman and Professor Steven Low. My research interests lie in online algorithms, optimization and mechanism design and their applications to the future energy systems.edit
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Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small... more
Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation. In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptim...
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Making use of predictions is a crucial, but under-explored, area of sequential decision problems with limited information. While in practice most online algorithms rely on predictions to make real time decisions, in theory their... more
Making use of predictions is a crucial, but under-explored, area of sequential decision problems with limited information. While in practice most online algorithms rely on predictions to make real time decisions, in theory their performance is only analyzed in simplified models of prediction noise, either adversarial or i.i.d. The goal of this thesis is to bridge this divide between theory and practice: to study online algorithm under more practical predictions models, gain better understanding about the value of prediction, and design online algorithms that make the best use of predictions. This thesis makes three main contributions. First, we propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. Using this general prediction model, we prove that Averaging Fixed Horizon Control (AFHC) can simultaneou...
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The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in... more
The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively. [1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019). [2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Ma...
We study Smoothed Online Convex Optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a $\Omega(\sqrt{d})$ lower bound on the competitive ratio of any... more
We study Smoothed Online Convex Optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a $\Omega(\sqrt{d})$ lower bound on the competitive ratio of any online algorithm, where $d$ is the dimension of the action space, we ask under what conditions this bound can be beaten. We introduce a novel algorithmic framework for this problem, Online Balanced Descent (OBD), which works by iteratively projecting the previous point onto a carefully chosen level set of the current cost function so as to balance the switching costs and hitting costs. We demonstrate the generality of the OBD framework by showing how, with different choices of "balance," OBD can improve upon state-of-the-art performance guarantees for both competitive ratio and regret, in particular, OBD is the first algorithm to achieve a dimension-free competitive ratio, $3 + O(1/\alpha)$, for locally polyhedral costs, where $\alpha$ measures...
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Cloud computing is becoming one of the ubiquitous computing paradigms for enterprises and organizations in recent years. Due to the volatility of system states such as cloud resource price and workload demand, it is challenging to... more
Cloud computing is becoming one of the ubiquitous computing paradigms for enterprises and organizations in recent years. Due to the volatility of system states such as cloud resource price and workload demand, it is challenging to provision cloud resources efficiently. This paper studies online cloud resource provisioning problems under cost budget where no accurate or distributional future information is available. We develop an algorithmic framework and design online algorithms based on the framework. We prove the competitive ratio of the proposed algorithms. We further show the proposed algorithms have better performance than a prominent existing algorithm named CR-Pursuit. While prior works on the problem require the objective functions to be concave, the proposed algorithms work for non-convex and non-concave objective functions. We conduct real-world trace-driven simulations. Results highlight the proposed algorithms outperform baselines significantly over a wide range of settings.
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Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning... more
Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge [1].
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We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy... more
We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy predictions is largely open. To this point, two promising algorithms have been proposed: Receding Horizon Control (RHC) and Averaging Fixed Horizon Control (AFHC). The comparison of these policies is largely open. AFHC has been shown to provide better worst-case performance, while RHC outperforms AFHC in many realistic settings. In this paper, we introduce a new class of policies, Committed Horizon Control (CHC), that generalizes both RHC and AFHC. We provide average-case analysis and concentration results for CHC policies, yielding the first analysis of RHC for OCO problems with noisy predictions. Further, we provide explicit results characterizing the optimal CHC policy as a function of properties of the prediction noise, e.g., variance and corre...
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We propose a new approach for distributed optimization based on an emerging area of theoretical computer science -- local computation algorithms. The approach is fundamentally different from existing methodologies and provides a number of... more
We propose a new approach for distributed optimization based on an emerging area of theoretical computer science -- local computation algorithms. The approach is fundamentally different from existing methodologies and provides a number of benefits, such as robustness to link failure and adaptivity in dynamic settings. Specifically, we develop an algorithm, LOCO, that given a convex optimization problem P with n variables and a "sparse" linear constraint matrix with m constraints, provably finds a solution as good as that of the best online algorithm for P using only O(log(n+m)) messages with high probability. The approach is not iterative and communication is restricted to a localized neighborhood. In addition to analytic results, we show numerically that the performance improvements over classical approaches for distributed optimization are significant, e.g., it uses orders of magnitude less communication than ADMM.
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Aggregators are playing an increasingly crucial role for integrating renewable generation into power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators di cult to monitor and... more
Aggregators are playing an increasingly crucial role for integrating renewable generation into power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators di cult to monitor and regulate, raising concerns about potential market manipulations. In this paper, we address this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, e cient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit can be obtained through strategic curtailment in practical settings.
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We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy... more
We consider online convex optimization (OCO) problems with switching costs and noisy predictions. While the design of online algorithms for OCO problems has received considerable attention, the design of algorithms in the context of noisy predictions is largely open. To this point, two promising algorithms have been proposed: Receding Horizon Control (RHC) and Averaging Fixed Horizon Control (AFHC). The comparison of these policies is largely open. AFHC has been shown to provide better worst-case performance, while RHC outperforms AFHC in many realistic settings. In this paper, we introduce a new class of policies, Committed Horizon Control (CHC), that generalizes both RHC and AFHC. We provide average-case analysis and concentration results for CHC policies, yielding the first analysis of RHC for OCO problems with noisy predictions. Further, we provide explicit results characterizing the optimal CHC policy as a function of properties of the prediction noise, e.g., variance and correlation structure. Our results provide a characterization of when AFHC outperforms RHC and vice versa, as well as when other CHC policies outperform both RHC and AFHC.
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ABSTRACT Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data... more
ABSTRACT Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on &quot;greening&quot; demand response in multi-tenant data centers, i.e., colocation data centers, by designing a pricing mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods to fulfill energy reduction requirement for EDR. In particular, we propose a pricing mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the pricing mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).
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ABSTRACT Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers’ participation in... more
ABSTRACT Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers’ participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center’s peak loads and energy expenditure: workload shifting and the use of local power generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with local generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.
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Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of on- line optimization problems where we have external noisy predictions available. We propose a stochastic prediction... more
Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of on- line optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predic- tions improve as time passes. We prove that achieving sub- linear regret and constant competitive ratio for online algo- rithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochas- tic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expecta- tion using only a constant-sized prediction window. Fur- thermore, we show that the performance of AFHC is tightly concentrated around its mean.