Phan, Linh T.X.
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Publication An Empirical Analysis of Scheduling Techniques for Real-Time Cloud-Based Data Processing(2011-12-01) Phan, Linh T.X.; Loo, Boon Thau; Zhang, Zhuoyao; Lee, Insup; Zheng, QiIn this paper, we explore the challenges and needs of current cloud infrastructures, to better support cloud-based data-intensive applications that are not only latency-sensitive but also require strong timing guarantees. These applications have strict deadlines (e.g., to perform time-dependent mission critical tasks or to complete real-time control decisions using a human-in-the-loop), and deadline misses are undesirable. To highlight the challenges in this space, we provide a case study of the online scheduling of MapReduce jobs executed by Hadoop. Our evaluations on Amazon EC2 show that the existing Hadoop scheduler is ill-equipped to handle jobs with deadlines. However, by adapting existing multiprocessor scheduling techniques for the cloud environment, we observe significant performance improvements in minimizing missed deadlines and tardiness. Based on our case study, we discuss a range of challenges in this domain posed by virtualization and scale, and propose our research agenda centered around the application of advanced real-time scheduling techniques in the cloud environment.Publication MC-ADAPT: Adaptive Task Dropping in Mixed-Criticality Scheduling(2017-10-01) Lee, Jaewoo; Phan, Linh T.X.; Chwa, Hoon Sung; Lee, Insup; Shin, InsikRecent embedded systems are becoming integrated systems with components of different criticality. To tackle this, mixed-criticality systems aim to provide different levels of timing assurance to components of different criticality levels while achieving efficient resource utilization. Many approaches have been proposed to execute more lower-criticality tasks without affecting the timeliness of higher-criticality tasks. Those previous approaches however have at least one of the two limitations; i) they penalize all lower-criticality tasks at once upon a certain situation, or ii) they make the decision how to penalize lowercriticality tasks at design time. As a consequence, they underutilize resources by imposing an excessive penalty on lowcriticality tasks. Unlike those existing studies, we present a novel framework, called MC-ADAPT, that aims to minimally penalize lower-criticality tasks by fully reflecting the dynamically changing system behavior into adaptive decision making. Towards this, we propose a new scheduling algorithm and develop its runtime schedulability analysis capable of capturing the dynamic system state. Our proposed algorithm adaptively determines which task to drop based on the runtime analysis. To determine the quality of task dropping solution, we propose the speedup factor for task dropping while the conventional use of the speedup factor only evaluates MC scheduling algorithms in terms of the worst-case schedulability. We apply the speedup factor for a newly-defined task dropping problem that evaluates task dropping solution under different runtime scheduling scenarios. We derive that MC-ADAPT has a speedup factor of 1.619 for task drop. This implies that MC-ADAPT can behave the same as the optimal scheduling algorithm with optimal task dropping strategy does under any runtime scenario if the system is sped up by a factor of 1.619.Publication Multi-Mode Virtualization for Soft Real-Time Systems(2018-04-01) Xu, Meng; Li, Haoran; Phan, Linh T.X.; Li, Chong; Lee, Insup; Lu, Chenyang; Sokolsky, Oleg; Gill, ChristopherReal-time virtualization is an emerging technology for embedded systems integration and latency-sensitive cloud applications. Earlier real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads, but this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. Here, we present Multi-Mode-Xen (M2-Xen), a real-time virtualization platform for dynamic real-time systems where VMs can operate in modes with different CPU resource requirements at run-time. M2-Xen has three salient capabilities: (1) dynamic allocation of CPU resources among VMs in response to their mode changes, (2) overload avoidance at both the VM and host levels during mode transitions, and (3) fast mode transitions between different modes. M2-Xen has been implemented within Xen 4.8 using the real-time deferrable server (RTDS) scheduler. Experimental results show that M2-Xen maintains real-time performance in different modes, avoids overload during mode changes, and performs fast mode transitions.Publication Holistic resource allocation for multicore real-time systems(2019-04-01) Phan, Linh T.X.; Xu, Meng; Choi, Hyon-Young; Lin, Yuhan; Li, Haoran; Lee, Insup; Lu, ChenyangThis paper presents CaM, a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. CaM is designed for partitioned scheduling, where tasks are mapped onto cores, and the shared cache and memory bandwidth resources are partitioned among cores to reduce resource interferences due to concurrent accesses. Based on our extension of LITMUSRT with Intel’s Cache Allocation Technology and MemGuard, we present an experimental evaluation of the relationship between the allocation of cache and memory bandwidth resources and a task’s WCET. Our resource allocation strategy exploits this relationship to map tasks onto cores, and to compute the resource allocation for each core. By grouping tasks with similar characteristics (in terms of resource demands) to the same core, it enables tasks on each core to fully utilize the assigned resources. In addition, based on the tasks’ execution time behaviors with respect to their assigned resources, we can determine a desirable allocation that maximizes schedulability under resource constraints. Extensive evaluations using real-world benchmarks show that CaM offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms existing solutions.Publication Video Quality Driven Buffer Sizing via Frame Drops(2011-08-01) Gangadharan, Deepak; Phan, Linh T.X.; Chakraborty, Samarjit; Zimmermann, Roger; Lee, InsupWe study the impact of video frame drops in buffer constrained multiprocessor system-on-chip (MPSoC) platforms. Since on-chip buffer memory occupies a significant amount of silicon area, accurate buffer sizing has attracted a lot of research interest lately. However, all previous work studied this problem with the underlying assumption that no video frame drops can be tolerated. In reality, multimedia applications can often tolerate some frame drops without significantly deteriorating their output quality. Although system simulations can be used to perform video quality driven buffer sizing, they are time consuming. In this paper, we first demonstrate a dual-buffer management scheme to drop only the less significant frames. Based on this scheme, we then propose a formal framework to evaluate the buffer size vs. video quality trade-offs, which in turn will help a system designer to perform quality driven buffer sizing. In particular, we mathematically characterize the maximum numbers of frame drops for various buffer sizes and evaluate how they affect the worst-case PSNR value of the decoded video. We evaluate our proposed framework with an MPEG-2 decoder and compare the obtained results with that of a cycle-accurate simulator. Our evaluations show that for an acceptable quality of 30 dB, it is possible to reduce the buffer size by upto 28.6% which amounts to 25.88 megabits.Publication Mixed-Criticality Scheduling on Multiprocessors using Task Grouping(2015-07-01) Ren, Jiankang; Phan, Linh T.X.Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor real-time performance for the low-criticality tasks.Publication On the Feasibility of Dynamic Rescheduling on the Intel Distributed Computing Platform(2010-11-01) Phan, Linh T.X.; Zhang, Zhuoyao; Tan, Godfrey; Jain, Saumya; Duong, Harrison; Lee, Insup; Loo, Boon ThauThis paper examines the feasibility of dynamic rescheduling techniques for effectively utilizing compute resources within a data center. Our work is motivated by practical concerns of Intel’s NetBatch system, an Internet-scale data center based distributed computing platform developed by Intel Corporation for massively parallel chip simulations within the company. NetBatch has been operational for many years, and currently is deployed live on tens of thousands of machines that are globally distributed at various data centers. We perform an analysis of job execution traces obtained over a one year period collected from tens of thousands of NetBatch machines from 20 different pools. Our analysis show that we observe that the NetBatch currently does not make full use of all the resources. Specifically, the job completion time can be severely impacted due to job suspension when higher priority jobs preempt lower priority jobs. We then develop dynamic job rescheduling strategies that adaptively restart jobs to available resources elsewhere, which better utilize system resources and improve completion times. Our trace-driven evaluation results show that dynamic rescheduling enables NetBatch to significantly reduce system waste and completion time of suspended jobs.Publication Cloud-Based Secure Logger for Medical Devices(2016-06-01) Nguyen, Hung; Ivanov, Radoslav; Haeberlen, Andreas; Phan, Linh T.X.; Sokolsky, Oleg; Weimer, James; Hanson III, C. William; Acharya, Bipeen; Lee, Insup; Walker, JesseA logger in the cloud capable of keeping a secure, time-synchronized and tamper-evident log of medical device and patient information allows efficient forensic analysis in cases of adverse events or attacks on interoperable medical devices. A secure logger as such must meet requirements of confidentiality and integrity of message logs and provide tamper-detection and tamper-evidence. In this paper, we propose a design for such a cloud-based secure logger using the Intel Software Guard Extensions (SGX) and the Trusted Platform Module (TPM). The proposed logger receives medical device information from a dongle attached to a medical device. The logger relies on SGX, TPM and standard encryption to maintain a secure communication channel even on an untrusted network and operating system. We also show that the logger is resilient against different kinds of attacks such as Replay attacks, Injection attacks and Eavesdropping attacks.Publication Compositional Analysis of Multi-Mode Systems(2009-01-01) Phan, Linh T.X.; Lee, Insup; Sokolsky, OlegThe paper presents a model for multi-mode realtime applications and develops new techniques for the compositional analysis of systems that contain multiple such applications. An algorithm for constructing an interface for a single multimode application is presented. Then, a method for computing an interface of a composite application is presented, which uses only the interfaces of constituent applications. A case study of an adaptive streaming system demonstrates that multi-mode analysis offers more precise results compared to a unimodal worst-case analysis.Publication RT-OpenStack: CPU Resource Management for Real-Time Cloud Computing(2015-06-01) Xi, Sisu; Xu, Meng; Li, Chong; Phan, Linh T.X.; Lu, Chenyang; Lee, Insup; Gill, Christopher D; Sokolsky, OlegClouds have become appealing platforms for not only general-purpose applications, but also real-time ones. However, current clouds cannot provide real-time performance to virtual machines (VMs). We observe the demand and the advantage of co-hosting real-time (RT) VMs with non-real-time (regular) VMs in a same cloud. RT VMs can benefit from the easily deployed, elastic resource provisioning provided by the cloud, while regular VMs effectively utilize remaining resources without affecting the performance of RT VMs through pro per resource management at both the cloud and the hypervisor levels. This paper presents RT-OpenStack, a cloud CPU resource management system for co-hosting real-time and regular VMs. RT-OpenStack entails three main contributions: (1) integration of a real-time hypervisor (RT-Xen) and a cloud management system (OpenStack) through a real-time resource interface; (2) a realtime VM scheduler to allow regular VMs to share hosts with RT VMs without interfering the real-time performance of RT VMs; and (3) a VM-to-host mapping strategy that provisions real-time performance to RT VMs while allowing effective resource sharing with regular VMs. Experimental results demonstrate that RTOpenStack can effectively improve the real-time performance of RT VMs while allowing regular VMs to fully utilize the remaining CPU resources.