Guaranteed Confidence Probability Research Articles

Energy Efficiency Optimization for Communication of Air-Based Information Network with Guaranteed Timing Constraints

In air-based information network of the Space-Air-Ground Integrated Network, it is critical to select an efficient communication link for each communication. However, some communication may not have fixed rules. In this article, we build a model of the air-based information network, and use the dynamic programming to select the most efficient communication link by minimizing the energy consumption in the communication model. In the communication model, energy consumption is associated with the distance between aircrafts and the relative velocity of the aircrafts. There is a given example used to describe the communication subnetworks of aircrafts in the air-based information network, and how each subnetwork containing multiple aircrafts communicates efficiently with the optimization by using the presented algorithms. The experiments show that the presented algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, there has been an achievement that an average reduction of 9.9 % per 20 time units in the 100-subnetwork model satisfying timing constraints based on the presented algorithm.

Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability

With the increasing capabilities of mobile phones, mobile users access data via wireless interfaces pervasively. Although WiFi has limited coverage and resulted in a bigger delay of data access, it is not uncommon that mobile users are willing to use WiFi to transmit data to decrease communication costs instead of 3G. Hence it is reasonable to use delay tolerance strategies to balance execution time, energy consumption, and communication cost. In this paper, we model mobile user experience as a combination of three random variables (energy consumption, execution time and communication cost). We present a wireless interface scheduling algorithm to select proper wireless interfaces for a set of data-dependent sporadic tasks to enhance user experience under the constraints of execution time, energy consumption, and communication cost with a guaranteed confidence probability in a delay-tolerant environment. The experimental results show that our approach can effectively enhance the user experience.

2011 ACM TODAES best paper award

In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves the heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0% on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using the worst-case scenario.

Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems

In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0% on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using worst-case scenario.

Energy minimization for heterogeneous wireless sensor networks

Lifetime is very important to wireless sensor networks since most sensors are equipped with non-rechargeable batteries. Therefore, energy and delay are critical issues for the research of sensor networks that have limited lifetime. Due to the uncertainties in execution time of some tasks, this paper models each varied execution time as a probabilistic random variable with the consideration of applications' performance requirements to solve the MAP (Mode Assignment with Probability) problem. Using probabilistic design, we propose an optimal algorithm to minimize the total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The experimental results show that our approach achieves significant energy saving than previous work. For example, our algorithm achieves an average improvement of 32.6% on total energy consumption.

Voltage Assignment with Guaranteed Probability Satisfying Timing Constraint for Real-time Multiproceesor DSP

Dynamic Voltage Scaling (DVS) is one of the techniques used to obtain energy-saving in real-time DSP systems. In many DSP systems, some tasks contain conditional instructions that have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable and solves the Voltage Assignment with Probability (VAP) Problem. VAP problem involves finding a voltage level to be used for each node of an date flow graph (DFG) in uniprocessor and multiprocessor DSP systems. This paper proposes two optimal algorithms, one for uniprocessor and one for multiprocessor DSP systems, to minimize the expected total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The experimental results show that our approach achieves significant energy saving than previous work. For example, our algorithm for multiprocessor achieves an average improvement of 56.1% on total energy-saving with 0.80 probability satisfying timing constraint.