Response Time Analysis for Sporadic Server Based Budget Scheduling in Real Time Virtualization Environments

Beckert, Matthias; Ernst, Rolf

doi:10.1145/3126559

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…In other fields such as CPU scheduling discussed in the previous sections, in the current Hadoop schedulers, when straggler tasks exist, the container handling the task will be blocked so it can be used for other tasks. In other fields such as CPU scheduling [34], the blocking problem is handled by removing the blocking tasks from the main queue and sending them into a temporary place (a callback). However, the method has not been implemented in Hadoop, especially in Reduce task scheduling.…”

Section: E Proposed Methodsmentioning

confidence: 99%

Asynchronous Non-Blocking Algorithm to Handle Straggler Reduce Tasks in Hadoop System

Khoiruddin¹,

Zakaria²,

Alhussian³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Hadoop is widely adopted as a big data processing application as it can run on commercial hardware at a reasonable time. Hadoop uses asynchronous blocking concurrency using Thread and Future class. Therefore, in some cases such as network link or hardware failure, a running task may block other tasks from running (the task becomes straggler). Hadoop releases are equipped with algorithms to handle straggler tasks problem. However, the algorithms manage Map and Reduce task similarly, while the straggler root cause might be different for both tasks. In this paper, the Asynchronous Non-Blocking (ANB) method is proposed to improve the performance and avoid the blocking of Reduce task in Hadoop. Instead of using the single queue, our approach uses two queues, i.e. task queue and callback queue. When a task is not ready or detected as a straggler, it is removed from the main task queue and temporarily sent to the callback queue. When the task is ready to run, it will be sent back to the main task queue for running. The performance of the algorithm is compared with rTuner, the latest paper found on handling straggler task in Reduce task. From the comparison, it is shown that ANB consistently gives faster time to complete because any unready tasks will be directly put into the callback queue without blocking other tasks. Furthermore, the overhead time in rTuner is high as it needs to check the straggler status and to find the reason for a task to become straggler.

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Section: E Proposed Methodsmentioning

confidence: 99%

Asynchronous Non-Blocking Algorithm to Handle Straggler Reduce Tasks in Hadoop System

Khoiruddin¹,

Zakaria²,

Alhussian³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Table 1 summarizes some of the most relevant works that have performed a timing analysis of an OS or a virtualized environment in embedded systems, including a brief description of the works, the hardware platform, the SW environment where the analysis was carried out, and the tool used for the analysis. Some make a timing analysis of virtualized environments [8,24,25], others analyze the interrupt latency on an OS [26,27], and others analyze the interrupt latency on virtualized environments [28][29][30][31][32][33]. Finally, some propose new tools for analyzing latencies in embedded systems [34][35][36][37][38].…”

Section: Related Workmentioning

confidence: 99%

Interrupt Latency Accurate Measurement in Multiprocessing Embedded Systems by Means of a Dedicated Circuit

Alonso,

Muguira,

Garate

et al. 2024

Electronics

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Modern multiprocessing embedded applications require, in many cases, two different environments on the same platform: one that meets real-time requirements and another one with a general purpose operating system. Although several technologies can be used, two of the most popular are virtualization based on hypervisors and asymmetric multiprocessing software. However, using these tools introduces latency, which must be measured to verify compliance with real-time requirements. With the aim of facilitating these measurements, this work provides a hardware tool that is more precise and easier to use than other existing software solutions. The paper also studies the interrupt latency generated by different hypervisors and asymmetric multiprocessing frameworks in a Zynq UltraScale+ platform. This research work facilitates the accurate study of the temporal response of multiprocessor embedded systems, which allows for evaluating their suitability for applications with real-time requirements.

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“…This paper focuses on analysis of a single typed DAG task, but our results are also meaningful to general system setting with multiple recurrent typed DAG tasks. On one hand, the results of this paper are directly applicable to multiple tasks under scheduling algorithms where a subset of cores are assigned to each individual parallel task (e.g., federated scheduling [3], [5], [6], [29], [30]). On the other hand, the analysis of intra-task interference addressed in this paper is a necessary step towards the analysis for scheduling algorithms where different tasks interfere with each other (e.g., global scheduling [2], [8], [32]).…”

Section: Introductionmentioning

confidence: 99%

“…Proof. By the same idea as the proof of Theorem 3.1 but using the new bound (6) for Y s instead of (3), we can get…”

mentioning

confidence: 99%

Response Time Bounds for Typed DAG Parallel Tasks on Heterogeneous Multi-Cores

Han

Guan

Sun

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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Heterogenerous multi-cores utilize the strength of different architectures for executing particular types of workload, and usually offer higher performance and energy efficiency. In this paper, we study the worst-case response time (WCRT) analysis of typed scheduling of parallel DAG tasks on heterogeneous multi-cores, where the workload of each vertex in the DAG is only allowed to execute on a particular type of cores. The only known WCRT bound for this problem is grossly pessimistic and suffers the non-self-sustainability problem. In this paper, we propose two new WCRT bounds. The first new bound has the same time complexity as the existing bound, but is more precise and solves its non-self-sustainability problem. The second new bound explores more detailed task graph structure information to greatly improve the precision, but is computationally more expensive. We prove that the problem of computing the second bound is strongly NP-hard if the number of types in the system is a variable, and develop an efficient algorithm which has polynomial time complexity if the number of types is a constant. Experiments with randomly generated workload show that our proposed new methods are significantly more precise than the existing bound while having good scalability.

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Response Time Analysis for Sporadic Server Based Budget Scheduling in Real Time Virtualization Environments

Cited by 8 publications

References 16 publications

Asynchronous Non-Blocking Algorithm to Handle Straggler Reduce Tasks in Hadoop System

Asynchronous Non-Blocking Algorithm to Handle Straggler Reduce Tasks in Hadoop System

Interrupt Latency Accurate Measurement in Multiprocessing Embedded Systems by Means of a Dedicated Circuit

Response Time Bounds for Typed DAG Parallel Tasks on Heterogeneous Multi-Cores

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