Yasushi Inoguchi

Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing

With energy shortages and global climate change leading our concerns these days, the power consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing. We employ the predictor to predict future load demand based on historical demand. According to the prediction, the algorithm turns off unused servers and restarts them to minimize the number of running servers, thus minimizing the energy use at the points of consumption to benefit all other levels. For evaluation, we perform simulations with two load traces. The results show that the PP20 mode can save up to 46.3% of power consumption with a drop rate of 0.03% on one load trace, and a drop rate of 0.12% with a power reduction rate of 46.7% on the other.

Secure Data Aggregation in Wireless Sensor Networks: A Survey

Data aggregation is a widely used technique in wireless sensor networks. The security issues, data confidentiality and integrity, in data aggregation become vital when the sensor network is deployed in a hostile environment. There has been many related work proposed to address these security issues. In this paper we survey these work and classify them into two cases: hop-by-hop encrypted data aggregation and end-to-end encrypted data aggregation. We also propose two general frameworks for the two cases respectively. The framework for end-to-end encrypted data aggregation has higher computation cost on the sensor nodes, but achieves stronger security, in comparison with the framework for hop-by-hop encrypted data aggregation

Predict task running time in grid environments based on CPU load predictions

A good running time prediction of tasks is very helpful and important for job scheduling and resource management in grid systems. In this paper, we present a running time prediction method for grid tasks based on our previous work, which is a novel CPU load prediction method. In order to eliminate the interference of other factors, such as memory accessing, network performance, and fluctuation of competing CPU load and so on, we produce a simulation to test and evaluate our prediction method. In this simulation we use more than 10,000 randomized test cases run on load traces sampled from 39 different machines. The simulation results are excellent and demonstrate that our running time prediction of grid tasks outperforms significantly that of a widely existing prediction method.

CPU Load Predictions on the Computational Grid

The ability to accurately predict future resource capabilities is of great importance for applications and scheduling algorithms which need to determine how to use time-shared resources in a dynamic grid environment. In this paper we present and evaluate a new and innovative method to predict the one-stepahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results demonstrate that this new prediction strategy achieves average prediction errors that are between 37% and 86% lower than those incurred by the previously best tendency-based method.

Predicting Running Time of Grid Tasks based on CPU Load Predictions

The ability to accurately predict task running time is of great importance for interactive applications and scheduling algorithms which need to determine how to use time-shared resources in a dynamic grid environment. In this paper we present and evaluate a new method to predict the running time of tasks in a grid. The prediction of task running time is based on a novel CPU load prediction method and is calculated from predictions of CPU load. We conducted evaluations using more than 10,000 randomized testcases run on load traces sampled from 39 heterogeneous machines. Our experimental results demonstrate that both our CPU load prediction method and task running time prediction strategy outperform significantly the widely used AR(16) load prediction model and the task running-time prediction method based on this model

CPU Load Predictions on the Computational Grid*This research is conducted as a program for the 21st Century COE Program by Ministry of Education, Culture, Sports, Science and Technology, Japan.

The ability to accurately predict future resource capabilities is of great importance for applications and scheduling algorithms which need to determine how to use time-shared resources in a dynamic grid environment. In this paper we present and evaluate a new and innovative method to predict the one-stepahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results demonstrate that this new prediction strategy achieves average prediction errors that are between 37% and 86% lower than those incurred by the previously best tendency-based method.

On-the-fly detection of precise loop nests across procedures on a dynamic binary translation system

Loop structures in programs have been regarded as a primary source of finding parallelism from sequential codes. In this paper, we present a new technique that dynamically detects precise loop structures with their inter-procedural nests on a dynamic binary translation system. Using precompiled application binary code as an input, our mechanism generates the simple but precise markers when they are loaded from their binary code image, and at runtime monitors loop structures with inter-procedural nesting on the fly using Loop-Call Context Graph. We implement our mechanism and evaluate it using SPEC CPU benchmark suite. The results show that our mechanism reveals precise loop structures with interprocedural loop nesting successfully. The results also show that ours can reduce overheads for loop analysis compared with the existing ones. These indicate that our mechanism can be applied to runtime optimization and parallelization as well as hints for performance tuning.

Real-time Task Scheduling Using Extended Overloading Technique for Multiprocessor Systems

The scheduling of real-time tasks with fault-tolerant requirements has been an important problem in multiprocessor systems. Primary-backup (PB) approach is often used as a fault-tolerant technique to guarantee the deadlines of tasks despite the presence of faults. In this paper we propose a PB-based task scheduling approach, wherein an allocation parameter is used to search the available time slots for a newly arriving task, and the previously scheduled tasks can be rescheduled when there is no available time slot for the newly arriving task. In order to improve the schedulability we extend the existing PB-overloading and the Backup-backup (BB) overloading. Our proposed task scheduling algorithm is compared with some existing scheduling algorithms in the literature through simulation studies. The results have shown that the task rejection ratio of our real-time task scheduling algorithm is lower than the compared algorithms.

Improving accuracy of host load predictions on computational grids by artificial neural networks

The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This work attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe the feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves consistent performance improvement with surprisingly low overhead in most cases. Compared with the best previously proposed method, our typical 20:10:1 network reduces the mean of prediction errors by approximately up to 79%. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples, in order to make tens of thousands of accurate predictions within just a second.

Dynamic Task Flow Scheduling for Heterogeneous Distributed Computing: Algorithm and Strategy

Heterogeneous distributed computing environments are well suited to meet the fast increasing computational demands. Task scheduling is very important for a heterogeneous distributed system to satisfy the large computational demands of applications. The performance of a scheduler in a heterogeneous distributed system normally has something to do with the dynamic task flow, that is, the scheduler always suffers from the heterogeneity of task sizes and the variety of task arrivals. From the long-term viewpoint it is necessary and possible to improve the performance of the scheduler serving the dynamic task flow. In this paper we propose a task scheduling method including a scheduling strategy which adapts to the dynamic task flow and a genetic algorithm which can achieve the short completion time of a batch of tasks. The strategy and the genetic algorithm work with each other to enhance the schedulers efficiency and performance. We simulated a task flow with enough tasks, the scheduler with our strategy and algorithm, and the schedulers with other strategies and algorithms. We also simulated a complex scenario including the variant arrival rate of tasks and the heterogeneous computational nodes. The simulation results show that our scheduler achieves much better scheduling results than the others, in terms of the average waiting time, the average response time, and the finish time of all tasks.