Qinghui Tang

Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach

High-performance computing data centers have been rapidly growing, both in number and size. Thermal management of data centers can address dominant problems associated with cooling such as the recirculation of hot air from the equipment outlets to their inlets and the appearance of hot spots. In this paper, we show through formalization that minimizing the peak inlet temperature allows for the lowest cooling power needs. Using a low-complexity linear heat recirculation model, we define the problem of minimizing the peak inlet temperature within a data center through task assignment (MPIT-TA), consequently leading to minimal cooling-requirement. We also provide two methods to solve the formulation: Xlnt-GA, which uses a genetic algorithm, and Xlnt-SQP, which uses sequential quadratic programming. Results from small-scale data center simulations show that solving the formulation leads to an inlet temperature distribution that, compared to other approaches, is 2 degC to 5 degC lower and achieves about 20 to 30 percent cooling energy savings at common data center utilization rates. Moreover, our algorithms consistently outperform the minimize heat recirculation algorithm, a recirculation-reducing task placement algorithm in the literature.

Sensor-Based Fast Thermal Evaluation Model For Energy Efficient High-Performance Datacenters

In this work, we propose an abstract heat flow model which uses temperature information from onboard and ambient sensors, characterizes hot air recirculation based on these information, and accelerates the thermal evaluation process for high performance datacenters. This is critical to minimize energy costs, optimize computing resources, and maximize computation capability of the datacenters. Given a workload and thermal profile, obtained from various distributed sensors, we predict the resulting temperature distribution in a fast and accurate manner taking into account the recirculation characterization of a datacenter topology. Simulation results confirm our hypothesis that heat recirculation can be characterized as cross interference in our abstract heat flow model. Moreover, fast thermal evaluation based on cross interference can be used in online thermal management to predict temperature distribution in real-time.

Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue

A network of biosensors can be implanted in a human body for health monitoring, diagnostics, or as a prosthetic device. Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the specific absorption rate (SAR) and temperature increase of implanted biosensors by using the finite-difference time-domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, temperature increase potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.

Thermal-aware task scheduling for data centers through minimizing heat recirculation

The thermal environment of data centers plays a significant role in affecting the energy efficiency and the reliability of data center operation. A dominant problem associated with cooling data centers is the recirculation of hot air from the equipment outlets to their inlets, causing the appearance of hot spots and an uneven inlet temperature distribution. Heat is generated due to the execution of tasks, and it varies according to the power profile of a task. We are looking into the prospect of assigning the incoming tasks around the data center in such a way so as to make the inlet temperatures as even as possible; this will allow for considerable cooling power savings. Based on our previous research work on characterizing the heat recirculation in terms of cross-interference coefficients, we propose a task scheduling algorithm for homogeneous data centers, called XInt, that minimizes the inlet temperatures, and leads to minimal heat recirculation and minimal cooling energy cost for data center operation. We verify, through both theoretical formalization and simulation, that minimizing heat recirculation will result in the best cooling energy efficiency. XInt leads to an inlet temperature distribution that is 2degC to 5degC lower than other approaches, and achieves about 20%-30% energy savings at moderate data center utilization rates. XInt also consistently achieves the best energy efficiency compared to another recirculation minimized algorithm, MinHR.

TARA: thermal-aware routing algorithm for implanted sensor networks

Implanted biological sensors are a special class of wireless sensor networks that are used in-vivo for various medical applications. One of the major challenges of continuous in-vivo sensing is the heat generated by the implanted sensors due to communication radiation and circuitry power consumption. This paper addresses the issues of routing in implanted sensor networks. We propose a thermal-aware routing protocol that routes the data away from high temperature areas (hot spots). With this protocol each node estimates temperature change of its neighbors and routes packets around the hot spot area by a withdraw strategy. The proposed protocol can achieve a better balance of temperature rise and only experience a modest increased delay compared with shortest hop, but thermal-awareness also indicates the capability of load balance, which leads to less packet loss in high load situations.

Thermal-Aware Task Scheduling to Minimize Energy Usage of Blade Server Based Datacenters

Blade severs are being increasingly deployed in modern datacenters due to their high performance/cost ratio and compact size. In this study, we document our work on blade server based datacenter thermal management. Our goal is to minimize the total energy costs (usage) of datacenter operation while providing a reasonable thermal environment for their reliable operation. Due to special characteristics of blade servers, we argue that previously proposed power-oriented schemes are ineffective for blade server-based datacenters and that task-oriented scheduling is a more practicable approach since the contribution to the total energy cost from cooling and computing systems varies according to the utilization rates. CFD simulations are used to evaluate scheduling results of three different task scheduling algorithms: uniform outlet profile (UOP), minimal computing energy (MCE), and uniform task (UT), under four different blade-server energy consumption models: discretenonoptimal (DNO), discreteoptimal (DO), analognonoptimal (ANO), and analogoptimal (AO). Simulation results show that the MCE algorithm, in most cases, results in a minimal total energy cost - a conclusion that differs from the findings of previous research. UOP performs better than UT at low datacenter utilization rates, whereas UT outperforms UOP at high utilization rates

Software Architecture for Dynamic Thermal Management in Datacenters

Minimizing the energy cost and improving thermal performance of power-limited datacenters, deploying large computing clusters, are the key issues towards optimizing their computing resources and maximally exploiting the computation capabilities. In this paper, we develop a unique merger between the physical infrastructure and resource management functions of a cluster management system to take a holistic view of datacenter management, and make global (at the level of a datacenter) thermal-aware job scheduling decisions. A software architecture is presented in this regard and implemented in a fully operational computational cluster in the ASU datacenter. The proposed architecture develops a feedback-control loop, by combining information from ambient and on-board sensors with the node allocation and job scheduling mechanisms, for managing the system load depending on the thermal distribution in the datacenter.

BER performance analysis of an on-off keying based minimum energy coding for energy constrained wireless sensor applications

An on-off keying based minimum-energy coding scheme with coherent receiver has been shown to provide better performance than BPSK. This paper presents a closed-form expression of the BER performance of that scheme over an AWGN channel with either a coherent receiver or a noncoherent receiver. In this paper, a more conservative result is obtained due to a better characterization of the average energy per source bit. Our results show that the performance is better than BPSK only when the codeword length is greater than 63. It is shown that hard-decision decoding outperforms BPSK only when the SNR is higher than a certain threshold and that soft-decision decoding outperforms BPSK regardless of the SNR value. Recommendations for practical codeword lengths are also provided.

A Unified Methodology for Scheduling in Distributed Cyber-Physical Systems

A distributed cyber-physical system (DCPS) may receive and induce energy-based interference to and from its environment. This article presents a model and an associated methodology that can be used to (i) schedule tasks in DCPSs to ensure that the thermal effects of the task execution are within acceptable levels, and (ii) verify that a given schedule meets the constraints. The model uses coarse discretization of space and linearity of interference. The methodology involves characterizing the interference of the task execution and fitting it into the model, then using the fitted model to verify a solution or explore the solution space.