This paper investigates a critical access control issue in the Internet of Things (IoT). In particular, we propose a smart contract-based framework, which consists of multiple access control contracts (ACCs), one judge contract (JC) and one register contract (RC), to achieve distributed and trustworthy access control for IoT systems. Each ACC provides one access control method for a subject-object pair, and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject. The JC implements a misbehavior-judging method to facilitate the dynamic validation of the ACCs by receiving misbehavior reports from the ACCs, judging the misbehavior and returning the corresponding penalty. The RC registers the information of the access control and misbehavior-judging methods as well as their smart contracts, and also provides functions (e.g., register, update and delete) to manage these methods. To demonstrate the application of the framework, we provide a case study in an IoT system with one desktop computer, one laptop and two Raspberry Pi single-board computers, where the ACCs, JC and RC are implemented based on the Ethereum smart contract platform to achieve the access control.

Recently, the rapid growth in both data center power density and scale poses great challenges to the cooling system. On one hand, the data center operators try to over provision cooling resources for fear of server failures induced by accumulated heat. On the other hand, they also want to reduce the energy cost as the cooling system takes up a significant portion of overall energy consumption. Among all available cooling solutions, air cooling dominates the data center industry due to its simpleness. However, its cooling efficiency has been questioned due to the low air density and specific heat. In this paper, we provide an overview for current endeavors to improve the air cooling efficiency. We group existing researches according to the locations where they can be applied from the perspective of air flow cycle. We also discuss the thermal measurement issues. We hope this paper can help researchers and engineers to design and control their data center air cooling systems.

In this paper, we try to address the issue of holistic energy minimization in data centers by designing a cross-layer optimization framework. We formulate a mixed integer nonLinear programming problem named data center holistic energy minimization problem, and develop an efficient JOINT algorithm to solve it. JOINT cuts energy consumption via collaboration of system components across different control layers, and dynamically configures system parameters according to the current workload. To evaluate the performance of the proposed framework, we conduct extensive simulations against synthetic and real-world workload traces. Experimental results suggest that our cross-layer framework is effective in data center holistic energy reduction.

Data centers worldwide consume a large amount of energy and pose serious threats to the environment. Combined Cooling, Heating, and Power (CCHP) and Waste Heat Reuse (WHR) systems are two effective approaches to improve the data center energy efficiency. However, whether and to what extent the combination of them can bring further performance improvement have not been explored yet. Moreover, it is unclear how to manage the energy flow in such a hybrid system. In this work, we try to answer these questions by proposing a data center energy architecture that includes both CCHP and WHR technologies. We formulate the energy management problem as two optimization problems with different objectives. By analytically solving the optimization problems, two energy management strategies, namely, Following the Electrical Load with Waste Heat Reuse (FEL-WHR) and Operating Cost-Aware Energy Management with Waste Heat Reuse (OCM-WHR), are developed. We evaluate the performance of proposed solutions by extensive simulations using a real-world workload trace from a production data center. Numerical results suggest that the CCHP andWHR technologies generally reduce the operating cost and are more environmental friendly. If the power generation efficiency of CCHP system is high, the impact of WHR will be more conspicuous (up to 8% improvement) and FEL-WHR is recommended because it achieves better sustainability at an operating cost comparable to OCM-WHR.

A framework for traffic congestion prediction and visualization based on machine learning and Fuzzy Comprehensive Evaluation named MF-TCPV is proposed in this paper. The framework uses DataX and DataV to implement the integration of multi-source heterogeneous traffic data and the visualization of congestion prediction results. A deep prediction model named LSTM-SPRVM based on deep learning algorithms, machine learning algorithms, and Spark parallelization technology for the prediction of traffic congestion features in the future is proposed. In MF-TCPV, traffic congestion is divided into six levels based on Fuzzy Comprehensive Evaluation and traffic congestion features such as average speed, road occupancy rate, and traffic flow density. MF-TCPV is validated based on the real data of Whitemud Drive in Canada. The experimental results demonstrate that MF-TCPV is capable of predicting the traffic congestion accurately and displaying prediction results visually. LSTM-SPRVM is better than other existing deep learning models in terms of prediction accuracy, and MF-TCPV can intuitively visualize the prediction results of traffic congestion.

Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose SafeCool , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. SafeCool incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, SafeCool achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that SafeCool saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.