The increase in network traffic in recent years has led to increased power consumption. Accordingly, many studies have tried to reduce the energy consumption of network devices. Various types of data have become available in large quantities via large high-speed computer networks. Time-constrained file transfer is receiving much attention as an advanced service. In this model, a request must be completed within a user-specified deadline or rejected if the requested deadline cannot be met. Some bandwidth assignment and routing methods to accept more requests have been proposed. However, these existing methods do not consider energy consumption. Herein, we propose a joint bandwidth assignment and routing method that reduces energy consumption for time-constrained large file transfer. The bandwidth assignment method reduces the power consumption of mediate node, typically router, by waiting for requests and transferring several requests at the same time. The routing method reduces the power consumption by selecting the path with the least predicted energy consumption. Finally, we evaluate the proposed method through simulation experiments.

In recent years, the number of requests to transfer large files via large high-speed computer networks has been increasing rapidly. Typically, these requests are handled in the “best effort” manner which results in unpredictable completion times. In this paper, we consider a model where a transfer request either must be completed by a user-specified deadline or must be rejected if its deadline cannot be satisfied. We propose a bandwidth scheduling method and a routing method for reducing the call-blocking probability in a bandwidth-guaranteed network. Finally, we show their excellent performance by simulation experiments.

The recent explosive growth in information networks has driven a huge increase in content. For efficient and flexible information retrieval over such large networks, agent technology has received much attention. We previously proposed an agent execution control method for time-constrained information retrieval that finds better results by terminating an agent that has already acquired results of high-enough quality or one that is unlikely to improve the quality of results with continued retrieval. However, this method assumed that all agents have identical time constraints. This leads to a disparity in the obtained score between users who give individual time constraints. In this paper, we propose a fair and efficient scheduling method based on the expected improvement of the highest score (EIS). The proposed method allocates all CPU resources to the agent that has the highest EIS to decrease the difference between users' scores and to increase the mean highest score of requested results.

Wireless sensor nodes are becoming more and more common in various settings and require a long battery life for better maintainability. Since most sensor nodes are powered by batteries, energy efficiency is a critical problem. In an experiment, we observed that when peak power consumption is high, battery voltage drops quickly, and the sensor stops working even though some useful charge remains in the battery. We propose three off-line algorithms that extend battery life by scheduling sensors' execution time that is able to reduce peak power consumption as much as possible under a deadline constraint. We also developed a simulator to evaluate the effectiveness of these algorithms. The simulation results showed that one of the three algorithms dramatically can extend battery life approximately three time as long as in simultaneous sensor activation.

Networks have gotten bigger recently, and users have a more difficult time finding the information that they want. The use of mobile agents to help users effectively retrieve information has garnered a lot of attention. In this paper, we propose an agent control method for time constrained information retrieval. We pay attention to the highest past score gained by the agents and control the agents with the expectation of achieving better scores. Using computer simulations, we confirmed that our control method gave the best improvement over the whole network while reducing the overall variance. From these results, we can say that our control method improves the quality of information retrieved by the agent.

As one of the technologies for the retrieval of desired contents over large-scale networks, multi-agent systems are receiving much attention. Since there are too many contents on the network to search them all exhaustively, some applications on multi-agent systems have time constraints, that is, they must obtain a result by a given deadline. To find better results for such applications, it is important for the agents to complete their tasks on as many nodes as possible by the deadline. However, most existing agent systems using round robin scheduling disciplines do not take time constraints into account. Therefore, agents are likely to miss their deadlines on many nodes. In this paper, we propose an efficient agent-dispatching method for time-constrained applications. This method decides creation and migration of a clone agent according to the estimated value of the number of agents that would have completed their tasks by the deadline. The results of our performance evaluation show that the proposed method increases the number of agents that complete their tasks.

This paper discusses clock skew due to manufacturing variability and environmental change. In clock tree design, transition time constraint is an important design parameter that controls clock skew and power dissipation. In this paper, we evaluate clock skew under several variability models, and demonstrate relationship among clock skew, transition time constraint and power dissipation. Experimental results show that constraint of small transition time reduces clock skew under manufacturing and supply voltage variabilities, whereas there is an optimum constraint value for temperature gradient. Our experiments in a 0.18 µm technology indicate that clock skew is minimized when clock buffer is sized such that the ratio of output and input capacitance is four.

This paper proposes a high-level energy-optimizing algorithm which can synthesize low energy system VLSIs. Given an initial system hardware obtained from an abstract behavioral description, the proposed algorithm applies to it the three energy reduction techniques, 1) reducing supply voltage, 2) selecting lower energy modules, and 3) applying gated clocks. By incorporating our area/delay/power estimation, the proposed algorithm can obtain low energy system VLSIs meeting the constraints of area, delay, and execution time. The proposed algorithm has been incorporated into a high-level synthesis system and experimental results demonstrate effectiveness and efficiency of the algorithm.