Jens Mache

A Comparative Study of Real Workload Traces and Synthetic Workload Models for Parallel Job Scheduling

Two basic approaches are taken when modeling workloads in simulation-based performance evaluation of parallel job scheduling algorithms: (1) a carefully reconstructed trace from a real supercomputer can provide a very realistic job stream, or (2) a flexible synthetic model that attempts to capture the behavior of observed workloads can be devised. Both approaches require that accurate statistical observations be made and that the researcher be aware of the applicability of a given trace for his or her experimental goals.

Communication patterns and allocation strategies

Summary form only given. Motivated by observations about job runtimes on the CPlant system, we use a trace-driven microsimulator to begin characterizing the performance of different classes of allocation algorithms on jobs with different communication patterns in space-shared parallel systems with mesh topology. We show that relative performance varies considerably with communication pattern. The paging strategy using the Hilbert space-filling curve and the best fit heuristic performed best across several communication patterns.

Job scheduling for prime time vs. non-prime time

Current job scheduling systems for massively parallel machines and Beowulf-class compute clusters support batch scheduling involving two classes of queues: prime time vs. non-prime time. Jobs running in these queue classes must satisfy different criteria with respect to job-size, runtime, or other resource needs. These constraints are designed to delay big jobs to non-prime time in order to provide better quality service during the prime time workday hours. This paper surveys existing prime time/non-prime time scheduling policies and investigates the sensitivity of scheduling performance to changes in the jobsize and runtime limits allowed during prime time vs. non-prime time. Our simulation study, using real workload traces from the NASA NAS IBM SP/2 cluster gives strong evidence for the use of specific prime time limits and sheds light oil the performance trade-offs regarding response times, utilization, short term scheduling algorithm (FCFS vs. EASY backfilling), and success and overflow rates.

Cluster computing in the classroom: topics, guidelines, and experiences

With the progress of research on cluster computing, more and more universities have begun to offer various courses covering cluster computing. A wide variety of content can be taught in these courses. Because of this, a difficulty that arises is the selection of appropriate course material. The selection is complicated by the fact that some content in cluster computing is also covered by other courses such as operating systems, networking, or computer architecture. In addition, the background of students enrolled in cluster computing courses varies. These aspects of cluster computing make the development of good course material difficult. Combining our experiences in teaching cluster computing in several universities in the USA and Australia and conducting tutorials at many international conferences all over the world, we present prospective topics in cluster computing along with a wide variety of information sources (books, software, and materials on the Web) from which instructors can choose. The course material described includes system architecture, parallel programming, algorithms, and applications. We share our experiences in teaching cluster computing and the topics we have chosen depending on course objectives.

Cluster computing in the classroom and integration with computing curricula 2001

With the progress of research on cluster computing, many universities have begun to offer various courses covering cluster computing. A wide variety of content can be taught in these courses. Because of this variation, a difficulty that arises is the selection of appropriate course material. The selection is complicated because some content in cluster computing may also be covered by other courses in the undergraduate curriculum, and the background of students enrolled in cluster computing courses varies. These aspects of cluster computing make the development of good course material difficult. Combining experiences in teaching cluster computing at universities in the United States and Australia, this paper presents prospective topics in cluster computing and a wide variety of information sources from which instructors can choose. The course material is described in relation to the knowledge units of the Joint IEEE Computer Society and the Association for Computing Machinery (ACM) Computing Curricula 2001 and includes system architecture, parallel programming, algorithms, and applications. Instructors can select units in each of the topical areas and develop their own syllabi to meet course objectives. The authors share their experiences in teaching cluster computing and the topics chosen, depending on course objectives.

Job scheduling that minimizes network contention due to both communication and I/O

As communication and I/O traffic increase on the interconnection network of high-performance systems, network contention becomes a critical problem drastically reducing performance. Whereas earlier allocation strategies were either sensitive to communication alone or sensitive to I/O alone, we present a new strategy that is sensitive to both communication and I/O. Our new strategy MC-Elongated, strives to achieve (1) the compactness needed to minimize communication-based contention as well as (2) the balance and orientation relative to I/O nodes needed to minimize I/O-based contention. We tested our new strategy using synthetic workloads and a real workload trace of 6087 jobs captured from a 400 node Intel Paragon. Our results show that with respect to system throughput and average job turnaround time, in environments with varying degree of communication and I/O traffic, MC-Elongated outperforms previous allocation strategies that are in use today. Regarding the tension between communication and I/O, our results show that spatial layout is more critical for I/O intensive jobs at lower utilization levels and more critical for communication-intensive jobs at higher utilization levels; and that in general, the impact of I/O traffic is dominant.

Teaching Grid Computing: Topics, Exercises, and Experiences

Grid protocols and technologies are being adopted in a wide variety of academic, government, and industrial environments, and a growing body of research-oriented literature in grid computing is being compiled. However, there is a need for educational material that is suitable for classroom use. This paper describes topics, exercises, and experiences of teaching grid computing at two different universities. Course material in grid computing can be grouped into several knowledge areas. The focus in this paper is on grid programming, i.e., developing grid-enabled services using the Globus toolkit. Assessment data shows that with preparatory material and hands-on exercises, undergraduate computer science students can master grid programming. Topics and exercises for security, network programming, Web services, and grid programming are described. Recommendations include using stand-alone containers on individual student computers and following a first grid programming exercise that builds confidence with at least one more elaborate grid programming exercise

Teaching Cybersecurity Analysis Skills in the Cloud

This paper reports on the experience of using the EDURange framework, a cloud-based resource for hosting on-demand interactive cybersecurity scenarios. Our framework is designed especially for the needs of teaching faculty. The scenarios we have implemented each are designed specifically to nurture the development of analysis skills in students as a complement to both theoretical security concepts and specific software tools. Our infrastructure has two features that make it unique compared to other cybersecurity educational frameworks. First, EDURange is scalable because it is hosted on a commercial, large-scale cloud environment. Second, EDURange supplies instructors with the ability to dynamically change the parameters and characteristics of exercises so they can be replayed and adapted to multiple classes. Our framework has been used successfully in classes and workshops for students and faculty. We present our experiences building the system, testing it, and using feedback from surveys to improve the system and boost user interest.

Exploiting Heterogeneity for Sensor Network Security

Many sensor network deployments are heterogeneous: a large number of regular nodes perform sensing, while some nodes have better energy resources and/or more computational capacity. Whereas the effect of heterogeneous nodes on network reliability and lifetime has been studied [3], we here focus on exploiting heterogeneity for security. For security and privacy, the identity of nodes must be authenticated and keys have to be distributed. In our lightweight end-middle-end security framework, every node has a public/private key pair, but only resource-rich gateway nodes use public key cryptography to compute digital signatures. Since gateway nodes vouch for regular nodes, regular nodes can use symmetric cryptography until a gateway is reached. On regular nodes, this reduces energy consumption and processing delays by more than a factor of 3000. Re-encryption on the gateways is typically not the bottleneck, since gateways are often line-powered and typically more than 100 times faster than regular nodes (e.g. Stargate vs. Mica). An additional advantage of our end-middle-end architecture is that only gateways are affected by changes in user privileges.

Practical error correction for resource-constrained wireless networks: unlocking the full power of the CRC

Bit errors are common in wireless networks, and techniques for overcoming them traditionally consist of expensive retransmission (e.g. Automatic Repeat reQuest (ARQ)) or expensive Forward Error Correction (FEC), both of which are undesirable in resource-constrained wireless networks such as wireless sensor networks (WSNs). In this paper, we present TVA (Transmit-Verify-Acknowledge), a protocol that can correct errors without adding additional redundancy to data packets. Instead, TVA corrects errors using the redundancy inherent in Cyclic Redundancy Checks (CRCs). The ubiquity of CRCs has the advantage of allowing TVA to be both backwards-compatible and backwards-efficient with link-layer protocols such as IEEE 802.15.4. We present a novel method of CRC error correction, which is compact and computationally efficient, and is designed to correct the most common error patterns observed in WSNs. We demonstrate that TVA provides reliability effectively equivalent to that of ARQ. We perform trace-driven simulations using data from sensor network deployments in different environments and analyze TVAs performance at different message lengths. To demonstrate the practicality of TVA, we implement it in TinyOS, and perform experiments on MicaZ motes to evaluate TVA in the presence of 802.11 interference. We find that TVA improves over ARQ and FEC-based protocols, using 31% less redundant communication and 30% less additional time to recover errored packets compared to ARQ.