Qinru Qiu

Distributed task migration for thermal management in many-core systems

In the deep submicron era, thermal hot spots and large temperature gradients significantly impact system reliability, performance, cost and leakage power. As the system complexity increases, it is more and more difficult to perform thermal management in a centralized manner because of state explosion and the overhead of monitoring the entire chip. In this paper, we propose a framework for distributed thermal management for many-core systems where balanced thermal profile can be achieved by proactive task migration among neighboring cores. The framework has a low cost agent residing in each core that observes the local workload and temperature and communicates with its nearest neighbor for task migration/exchange. By choosing only those migration requests that will result balanced workload without generating thermal emergency, the proposed framework maintains workload balance across the system and avoids unnecessary migration. Experimental results show that, compared with existing proactive task migration technique, our approach generates less hotspots and smoother thermal gradient with less migration overhead and higher processing throughput.

Dynamic power management of complex systems using generalized stochastic Petri nets

In this paper, we introduce a new technique for modeling and solving the dynamic power management (DPM) problem for systems with complex behavioral characteristics such as concurrency, synchronization, mutual exclusion and conflict. We model a power-managed distributed computing system as a controllable Generalized Stochastic Petri Net (GSPN) with cost. The obtained GSPN model is automatically converted to an equivalent continuous-time Markov decision process. Given the delay constraints, the optimal power management policy for system components as well as the optimal dispatch policy for requests are calculated by solving a linear programming problem based on the Markov decision process. Experimental results show that the proposed technique can achieve more than 20% power saving compared to other existing DPM techniques.

Adaptive power management using reinforcement learning

System level power management must consider the uncertainty and variability that comes from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past history and improve itself as the environment changes. This paper presents a novel online power management technique based on model-free constrained reinforcement learning (RL). It learns the best power management policy that gives the minimum power consumption for a given performance constraint without any prior information of workload. Compared with existing machine learning based power management techniques, the RL based learning is capable of exploring the trade-off in the power-performance design space and converging to a better power management policy. Experimental results show that the proposed RL based power management achieves 24% and 3% reduction in power and latency respectively comparing to the existing expert based power management.

Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting

In this paper, an energy aware dynamic voltage and frequency selection (EA-DVFS) algorithm is proposed. The EA-DVFS algorithm adjusts the processors behavior depending on the summation of the stored energy and the harvested energy in a future duration. Specifically, if the system has sufficient energy, tasks are executed at full speed; otherwise, the processor slows down task execution to save energy. Simulation results show that when the utilization is low, the EA-DVFS algorithm gives a deadline miss rate that is at least 50% lower than the one given by the lazy scheduling policy. Similarly, when the workload is low, the minimum storage size is reduced by at least 25%.

Harvesting-Aware Power Management for Real-Time Systems With Renewable Energy

In this paper, we propose a harvesting-aware power management algorithm that targets at achieving good energy efficiency and system performance in energy harvesting real-time systems. The proposed algorithm utilizes static and adaptive scheduling techniques combined with dynamic voltage and frequency selection to achieve good system performance under timing and energy constraints. In our approach, we simplify the scheduling and optimization problem by separating constraints in timing and energy domains. The proposed algorithm achieves improved system performance by exploiting task slack with dynamic voltage and frequency selection and minimizing the waste on harvested energy. Experimental results show that the proposed algorithm improves the system performance in deadline miss rate and the minimum storage capacity requirement for zero deadline miss rate. Comparing to the existing algorithms, the proposed algorithm achieves better performance in terms of the deadline miss rate and the minimum storage capacity under various settings of workloads and harvested energy profiles.

An adaptive scheduling and voltage/frequency selection algorithm for real-time energy harvesting systems

In this paper we propose an adaptive scheduling and voltage/frequency selection algorithm which targets at energy harvesting systems. The proposed algorithm adjusts the processor operating frequency under the timing and energy constraints based on workload information so that the system-wide energy efficiency is achieved. In this approach, we decouple the timing and energy constraints and simplify the original scheduling problem by separating constraints in timing and energy domains. The proposed algorithm utilizes maximum task slack for energy saving. Experimental results show that the proposed method improves the system performance in remaining energy, deadline miss rate and the minimum storage capacity requirement for zero deadline miss rate. Comparing to the existing algorithms, the new algorithm decreases the deadline miss rate by at least 23%, and the minimum storage capacity by at least 20% under various processor utilizations.

A game theoretic resource allocation for overall energy minimization in mobile cloud computing system

Cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute code remotely. When the cloud infrastructure consists of heterogeneous servers, the mapping between mobile devices and servers plays an important role in determining the energy dissipation on both sides. From an environmental impact perspective, any energy dissipation related to computation should be counted. To achieve energy sustainability, it is important reducing the overall energy consumption of the mobile systems and the cloud infrastructure. Furthermore, reducing cloud energy consumption can potentially reduce the cost of mobile cloud users because the pricing model of cloud services is pay-by-usage. In this paper, we propose a game-theoretic approach to optimize the overall energy in a mobile cloud computing system. We formulate the energy minimization problem as a congestion game, where each mobile device is a player and his strategy is to select one of the servers to offload the computation while minimizing the overall energy consumption. We prove that the Nash equilibrium always exists in this game and propose an efficient algorithm that could achieve the Nash equilibrium in polynomial time. Experimental results show that our approach is able to reduce the total energy of mobile devices and servers compared to a random approach and an approach which only tries to reduce mobile devices alone.

Dynamic power management in a mobile multimedia system with guaranteed quality-of-service

In this paper we address the problem of dynamic power management in a distributed multimedia system with a required quality of service (QoS). Using a generalized stochastic Petri net model where the non-exponential inter-arrival time distribution of the incoming requests is captured by the “stage method”, we provide a detailed model of the power-managed multimedia system under general QoS constraints. Based on this mathematical model, the power-optimal policy is obtained by solving a linear programming problem. We compare the new problem formulation and solution technique to previous dynamic power management techniques that can only optimize power under delay constraints. We then demonstrate that these other techniques yield policies with higher power dissipation by over-constraining the delay target in an attempt to indirectly satisfy the QoS constraints. In contrast, our new method correctly formulates the power management problem under QoS constraints and obtains the optimal solution.

An interleaved dual-battery power supply for battery-operated electronics

After a detailed analysis and discussion of two important characteristics of todays battery cells (i.e., their current-capacity and current-voltage curves), this paper describes the design principles and architecture of a dual-battery power supply system for portable electronics. The key idea is to integrate two battery types with different energy capacity and current rate curves into the power supply system, and then use them in an interleaved manner in response to varying current requirement of the VLSI circuit that is powered by this dual-battery system. Analytical and empirical results demonstrate the effectiveness of the new battery architecture in maximizing the service life of a battery system with fixed volume (or weight).

Accurate modeling and prediction of energy availability in energy harvesting real-time embedded systems

Energy availability is the primary subject that drives the research innovations in energy harvesting systems. In this paper, we first propose a novel concept of effective energy dissipation that defines a unique quantity to accurately quantify the energy dissipation of the system by including not only the energy demand by the electronic circuit, but also the energy overhead incurred by energy flows amongst system components. This work also addresses the techniques in run-time prediction of future harvested energy. These two contributions significantly improve the accuracy of energy availability computation for the proposed Model-Accurate Predictive DVFS algorithm, which aims at achieving best system performance under energy harvesting constraints. Experimental results show the improvements achieved by the MAP-DVFS algorithm in deadline miss rate. In addition, we illustrate the trend of system performance variation under different conditions and system design parameters.