Xuefeng Piao

Predictability of earliest deadline zero laxity algorithm for multiprocessor real-time systems

Validation methods for hard real-time jobs are usually performed based on the maximum execution time. The actual execution time of jobs are assumed to be known only when the jobs arrive or not known until they finish. A predictable algorithm must guarantee that it can generate a schedule for any set of jobs such that the finish time for the actual execution time is no later than the finish time for the maximum execution time. It is known that any job-level fixed priority algorithm (such as earliest deadline first) is predictable. However, job-level dynamic priority algorithms (such as least laxity first) may or may not. In this paper, we investigate the predictability of a job-level dynamic priority algorithm EDZL (earliest deadline zero laxity). We show that EDZL is predictable on the domain of integers regardless of the knowledge of the actual execution times. Based on this result, furthermore, we also show that EDZL can successfully schedule any periodic task set if the total utilization is not greater than (m + 1)/2, where m is the number of processors

Power-Aware EDZL Scheduling upon Identical Multiprocessor Platforms

Upon multiprocessor platforms, global EDZL is known to be at least as effective as global EDF in scheduling task sets to meet deadlines, but there has been no research on power-aware EDZL scheduling. In this paper, we firstly address the problem of reducing energy consumption of real-time tasks on EDZL scheduling by lowering processor speed. An off-line algorithm and an on-line algorithm are proposed to reduce energy consumption while guaranteeing a hard real-time constraint. Then we show the effectiveness of our algorithms through extensive simulation.

Adaptive load balancing mechanism for server cluster

Server cluster provides high availability, scalability, and reliability by gathering server nodes into a group. Client requests need to be distributed to each server node fairly to maximize the performance of server cluster. In this paper, we propose an adaptive and efficient load balancing algorithm for the server cluster. The proposed algorithm computes the load of server nodes with the usages of computer resources and their weights. These weights are determined dynamically based on the statistics of the usages. The experimental result shows that the proposed algorithm can prevent the bottleneck of server cluster efficiently compared with existing algorithms. This guarantees its adaptability even though there are changes to the characteristic of service.

High-Responsive Scheduling with MapReduce Performance Prediction on Hadoop YARN

Hadoop is an open-source big data analysis platform that is widely used in both academia and industry. Decoupling of resource management and programming framework, the next generation of Hadoop, namely Hadoop YARN, is accommodated to various programming frameworks and capable of handling more kinds of workload, such as interactive analysis and stream processing. However, most existent schedulers in YARN are designed for batch processing and they do not value per-job response time, which results in low responsiveness of the Hadoop platform. This paper proposes a FSPY (Fair Sojourn Protocol in YARN) scheduler to improve responsiveness with guaranteeing fairness. FSPY relies on job sizes which are unknown a priori. Consequently, we also present a job size prediction mechanism for MapReduce. Experimental results show that our scheduler outperforms Fair scheduler by 10x with respect to responsiveness under heavy workloads. Meanwhile, our prediction mechanism reaches an R2 prediction accuracy of 0.97.

Energy Consumption Optimization of Real-Time Embedded Systems

Minimizing energy consumption with guaranteeing real-time constraints in low-power embedded systems is gaining more importance as real-time applications become more widely used in embedded systems. Dynamic voltage scaling is a technique to reduce energy consumption by lowering supply voltage. However, lowering supply voltage may interfere with scheduling algorithms, so that tasks may not be successfully scheduled. In this paper, we formulate the problem of minimizing energy consumption for Pre-scheduling as an optimization problem, and show that the problem is a nonlinear convex optimization with linear constraints which can be solved by sequential quadratic programming. By solving the problem, we can obtain the optimal supply voltage and successful scheduling of all tasks is guaranteed.

Malicious behavior pattern mining using control flow graph

Cyber hacking attacks based on malicious code are becoming diversified. Malicious code analysis is very important because static flow analysis can naturally be helpful as part of the detection process given that malicious codes can affect the data and control flow of a program. This paper introduces the representation method of the Control Flow Graph based on malicious codes. Our proposed method can detect well-known malicious codes and their variants. In addition, the proposed method shows a new response method through the conceptual approach method of source codes.

Enhanced utilization bound of Rate-Monotonic scheduling in Controller Area Networks

When priorities to message streams are assigned using Rate Monotonic (RM) for a Controller Area Network (CAN), the utilization bound is known to be about 25% for CAN 2.0A and 29% for CAN 2.0B. In this letter, we present a higher utilization bound than the existing ones with a reasonable constraint. The new utilization bounds are approximately 34% for CAN 2.0A and 41% for CAN 2.0B if no single message streams utilization exceeds 46% or 27% of the total utilization for CAN 2.0A or CAN 2.0B, respectively.

Improving responsiveness of soft aperiodic tasks using proportional slack time

In a real-time system with both hard real-time periodic jobs and soft real-time aperiodic jobs, it is important to guarantee that the deadline of each periodic job is met, as well as to provide a fast response time for each aperiodic job. We propose an algorithm, called Proportional Slack Reserve (PSR), that produces an efficient schedule for such an environment. For every execution unit of a periodic job, the PSR algorithm reserves time which can be used for execution of aperiodic jobs. If reserved time is not available, the algorithm assigns a deadline to an aperiodic job for achieving better responsiveness of aperiodic jobs. The proposed algorithm can fully utilize processing power while meeting all deadlines of periodic jobs. It can also easily reclaim the time unused by the periodic job. We analytically show that for each aperiodic job, the response time in a PSR schedule is no longer than that in a TBS schedule, which is known to be efficient for servicing aperiodic jobs. We also present simulation results in which the response time of PSR is significantly improved over that of TBS, and moreover the performance of PSR compares favorably with TB(N) considering scheduling overhead.

Finish Time Predictability of Earliest Deadline Zero Laxity Algorithm for Multiprocessor Real-Time Systems*This work is supported in part by Brain Korea 21 project and in part by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-041-D00636). The ICT at Seoul National University provides research facilities for this study.

This letter proves the finish time predictability of EDZL (Earliest Deadline Zero Laxity) scheduling algorithm for multiprocessor real-time systems, which is a variant of EDF. Based on the results, it also shows that EDZL can successfully schedule any periodic task set if its total utilization is not greater than (m + 1)/2, where m is the number of processors.

Impact on the writing granularity for incremental checkpointing

Incremental checkpointing is an cost-efficient fault tolerant technique for long running programs such as genetic algorithms. In this paper, we derive the equations for the writing granularity of incremental checkpointing and find factors associated with the time overhead and disk space for incremental checkpoint. We also verify the applicability of the derived equation and the acceptability of the factors through experiments.