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Supply Monitoring System

By russelcabil Jun 13, 2013 2742 Words
System monitoring
From Wikipedia, the free encyclopedia
A system monitor (SM) in systems engineering is a process within a distributed system for collecting and storing state data. This is a fundamental principle supporting Application Performance Management. Contents  [hide]  * 1 Overview * 2 System monitor basics * 2.1 Protocol * 2.2 Data access * 2.3 Mode * 3 References| -------------------------------------------------

Overview [edit]
The argument that system monitoring is just a nice to have, and not really a core requirement for operational readiness, dissipates quickly when a critical application goes down with no warning.[1] The configuration for the system monitor takes two forms: 1. configuration data for the monitor application itself, and 2. configuration data for the system being monitored. See: System configuration The monitoring application needs information such as log file path and number of threads to run with. Once the application is running, it needs to know what to monitor, and deduce how to monitor. Because the configuration data for what to monitor is needed in other areas of the system, such as deployment[disambiguation needed], the configuration data should not be tailored specifically for use by the system monitor, but should be a generalized system configuration model. The performance of the monitoring system has two aspects:

* Impact on system domain or impact on domain functionality: Any element of the monitoring system that prevents the main domain functionality from working is in-appropriate. Ideally the monitoring is a tiny fraction of each applications footprint, requiring simplicity. The monitoring function must be highly tunable to allow for such issues as network performance, improvements to applications in the development life-cycle, appropriate levels of detail, etc. Impact on the systems' primary function must be considered. * Efficient monitoring or ability to monitor efficiently: Monitoring must be efficient, able to handle all monitoring goals in a timely manner, within the desired period. This is most related to scalability. Various monitoring modes are discussed below. There are many issues involved with designing and implementing a system monitor. Here are a few issues to be dealt with: * configuration

* protocol
* performance
* data access
System monitor basics [edit]
Protocol [edit]
There are many tools for collecting system data from hosts and devices using the SNMP (Simple Network Management Protocol).[2]Most computers and networked devices will have some form of SNMP access. Interpretation of the SNMP data from a host or device requires either a specialized tool (typically extra software [3] from the vendor) or a Management information base (MIB), a mapping of commands/data references to the various data elements the host or device provides. The advantage of SNMP for monitoring is its low bandwidth requirements and universal usage in the industries. Unless an application itself provides a MIB and output via SNMP, then SNMP is not suitable for collecting application data. Other protocols are suitable for monitoring applications, such as CORBA (language/OS-independent), JMX (Java-specific management and monitoring protocol), or proprietary TCP/IP or UDP protocols (language/OS independent for the most part). Data access [edit]

Data access refers to the interface by which the monitor data can be utilized by other processes. For example, if the system monitor is a CORBA server, clients can connect and make calls on the monitor for current state of an element, or historical states for an element for some time period. The system monitor may be writing data directly into a database, allowing other processes to access the database outside the context of the system monitor. This is dangerous however, as the table design for the database will dictate the potential for data-sharing. Ideally the system monitor is a wrapper for whatever persistence mechanism is used, providing a consistent and 'safe' access interface for others to access the data. Mode [edit]

The data collection mode of the system monitor is critical. The modes are: monitor poll, agent push, and a hybrid scheme. Monitor poll
In this mode, one or more processes in the monitoring system actually poll the system elements in some thread. During the loop, devices are polled via SNMP calls, hosts can be accessed via Telnet/SSH to execute scripts or dump files or execute other OS-specific commands, applications can be polled for state data, or their state-output-files can be dumped. The advantage of this mode is that there is little impact on the host/device being polled. The host's CPU is loaded only during the poll. The rest of the time the monitoring function plays no part in CPU loading. The main disadvantage of this mode is that the monitoring process can only do so much in its time. If polling takes too long, the intended poll-period gets elongated. Agent push

In agent-push mode, the monitored host is simply pushing data from itself to the system monitoring application. This can be done periodically, or by request from the system monitor asynchronously. The advantage of this mode is that the monitoring system's load can be reduced to simply accepting and storing data. It doesn't have to worry about timeouts for SSH calls, parsing OS-specific call results, etc. The disadvantage of this mode is that the logic for the polling cycle/options are not centralized at the system monitor, but distributed to each remote node. Thus changes to the monitoring logic must be pushed out to each node. Also, in agent-based monitoring, a host cannot inform that it is completely "down" or powered off, or if an intermediary system (such as a router) is preventing access to the system. Hybrid mode

The median mode between 'monitor-poll' and 'agent-push' is a hybrid approach, where the system configuration determines where monitoring occurs, either in the system monitor or agent. Thus when applications come up, they can determine for themselves what system elements they are responsible for polling. Everything however must post its monitored-data ultimately to the system monitorprocess. This is especially useful when setting up a monitoring infrastructure for the first time and not all monitoring mechanisms have been implemented. The system monitor can do all the polling in whatever simple means are available. As the agents become smarter, they can take on more of the load.

This dissertation documents the extent to which middle school students engage in small-group sense-making discussion, and also identifies those factors which provide support (or not) for small-group sense-making discussion.

For the past few decades, a good deal of attention has been paid to how students construct conceptual and procedural understandings of science. As a result, a number of theories have become increasingly refined to explain how students construct and modify their knowledge structures. Constructivism, for instance, a modern theory of knowledge, holds that individuals actively construct subjective understandings of the physical world based on their personal experiences (von Glasersfeld, 1984). From this paradigm, conceptual development occurs when people become dissatisfied with their existing conceptions (a state known as "disequilibration"; see Piaget 1952, 1969) and feel the need to modify their understanding of the world. Another theory -- information processing , a theory of cognition -- uses the "mind-as-computer" metaphor to explain how cognition proceeds. From this perspective, learning is the process of perceiving sensory inputs (i.e., new information), performing mental operations on this new information in working memory, and, finally, modifying existing knowledge structures in long-term memory (Atkinson & Shiffrin, 1968).

Clearly, these two widely-held theories take a cognitive approach to the learning process -- meaning that, from these perspectives, the factors most2 relevant to learning are those factors which deal directly with knowledge and thought processes. Consequently, when considering the best ways to educate their students, teachers who fall into the "constructivist" or "information processing" paradigms are likely to focus on one or more of the following cognitive factors: the form and content of existing knowledge structures, the development of new knowledge structures, metacognition (i.e., awareness of one's own mental activity), and so forth. For example, the teacher who bases her teaching on the theory of constructivism focuses on structuring learning activities that a) help students become aware of their prior knowledge and the ways in which this knowledge develops over time (Hewson & Thorley, 1990), and b) surprise or perplex students in order to have them experience dissatisfaction with this knowledge (Dykstra, Boyle, & Monarch, 1992). Similarly, the instructor who bases his instruction on the "information processing" paradigm is primarily concerned with such matters as cognitive efficiency (Reif & Larkin, 1991) and the differences between the knowledge structures and thought processes of experts and novices (Chi, Feltovich, & Glaser, 1981; Larkin, McDermott, Simon, & Simon, 1980).

Recently, however, researchers have come to believe that these sorts of cognitive perspectives need to be modified to include other factors in the learning environment. Strike and Posner (1992), for instance, argue that their well-known outline of the conditions necessary for conceptual change (dissatisfaction with existing conceptions, followed by the introduction of a new theory which is plausible, intelligible and fruitful; see Posner, Strike, Hewson, & Hertzog, 1982) should be expanded to include institutional and social3 sources of motivation and goals. Pintrich, Marx, and Boyle (1993) use the word cold to describe current theories of student learning (i.e., those concerned solely with cognitive factors); their suggestion is that researchers should shift their sights and focus -- in their new, hot theories of cognition -- on the roles of context and motivation in the learning process. An important justification for expanding our focus to include social and contextual factors in the learning environment is that, in part, learning is just that: a social, contextually-ground process. From a sociocultural perspective, for example, the behaviors, thoughts, and actions of students are recognized as being influenced by the expectations, traditions, and values of the classroom community (i.e., the classroom community's norms and values; see Cobb, Wood, & Yackel, 1993) . For instance, students are often reticent to speak in classrooms where they cannot trust their teacher to show serious interest in their ideas and questions (Mitchell, 1992). Another key aspect of the sociocultural perspective on learning is Vygotsky's idea that learners develop by internalizing the guidance of others (Griffin & Cole, 1984; Vygotsky, 1986, 1987). Vygotsky argues that, through social interaction, people move from needing guidance to accomplish a given task to eventually (after internalizing this guidance) being able to accomplish the task themselves. As applied to the classroom, the idea is that classroom discussions and joint problem-solving sessions help students internalize peerand adult-modeled concepts and procedures -- at which point the tasks that previously required guidance (e.g., scientific reasoning, problem-solving, setting up a scientific experiment) can now be done independently. In short,4 Vygotsky's notion of guidance is crucial as a direct connection between students' individual learning and the teacher-student and student-student social interactions that commonly occur in the classroom.

Research topic. With guidance playing such a key role in the learning process -- and especially in the wake of the National Research Council's (1996) recommendation that science education should be grounded in collaborative, inquiry-based activity -- it is therefore logical to turn to smallgroup student discourse (conversation) for insights into science learning in today's classrooms. For, as related to the construction of new scientific understanding, it is in these groups that students have the opportunity to test each other's experimental predictions, elaborate on each other's ideas, and engage in other types of discourse which lay the groundwork for establishing the meaning of scientific concepts.

And, while educators would love for these sorts of sense-making discussions to be commonplace in their classrooms, it is clear that these discussions aren't likely to occur without support from the teacher and the school curriculum (Johnson & Johnson, 1994). Ultimately, the idea is that educators will be able to create formal and informal learning environments that are conducive to small-group sense-making discussions once they become more aware of the many factors affecting this special brand of discourse. This is why the goal of the present study was to identify those factors having the most influence on middle school students' small-group sense-making discussions.5

Research Methods. This study was based on quantitative and
qualitative analyses of student conversations recorded in two middle school inquiry-based science classrooms. Allowing students to work in the classroom was important because it isn't clear that the results from investigations in an artificial environment (e.g., having student participants engage in activities after school, or outside the classroom) always apply to "real-life" classrooms. In general, it has been found that the use of activity-driven conversations -- whether they be conducted in museums (Borun, Chambers, & Cleghorn, 1996; Diamond, 1986), classrooms (diSessa, Hammer, Sherin, & Kolpakowski, 1991; Hammer, 1995), or other educational contexts -- lend themselves nicely to careful, descriptive analysis of student scientific discussion. Research Questions

In alignment with my interest in studying the factors affecting students' sense-making discussion, this study contributes to the research in science education by answering the following research questions. In particular, the context for this study was middle school students' discussions as they worked on force- and motion-based science activities in small groups. 1. How can we classify students' sense-making statements?

To study students' sense-making discussion, one must be able to identify and categorize it when it appears.
In this study, I constructed my own framework for verbal sense-making. This framework evolved from a framework for nonverbal sense-making known as comprehension activity (outlined in Chapter 2).6
2. To what extent do students engage in sense-making
It was of interest to determine to what extent certain students would engage in some, none, or a good deal of sense-making discussion in those portions of the curriculum where sense-making discussion is expected. 3. Which factors provide support for students' sense-making

The final purpose was to identify those personal, task-related, grouprelated (i.e., social), and contextual factors providing the greatest support for -- or hindrance to -- students' small-group sense-making discussions. Personal factors are those relatively stable intrinsic factors that one would normally associate with individual students (e.g., learning and performance goals, interpersonal skills, and subject matter interest). Task-related factors reflect the various ways that the educational task drives group discussion, via science content, task goals, prompts in the curricular materials, and the degree to which the task is intrinsically motivating. Group-related factors, such as group norms, social roles, and leadership styles, describe the ways that social interactions, group norms, and student leadership affect the group's sensemaking conversation. Contextual factors include the physical, organizational, and cultural aspects of the learning environment (e.g., classroom norms, the physical layout of the classroom, and the role of the teacher).7 Overview of Upcoming Chapters

In Chapter 2, I do two things: (1) provide the conceptual frameworks for comprehension activity (nonverbal sense-making), discourse, and collaboration, including their relationship to the learning process, and (2) summarize the personal, task, group, and contextual factors that were likely to affect sense-making discussion in this study.

In Chapter 3, I outline my methods of analysis; in particular, I describe the methods used to:
• establish the extent of students' SMD in an inquiry-based middle school science curriculum;
• determine significant differences in SMD between group, students, and curriculum areas; and
• identify the factors that explained these significant differences in sense-making.
In Chapter 4, I chronicle how, after careful analysis of the small-group discussions in this study, I modified the comprehension activity framework for nonverbal sense-making in order to arrive at my own framework for SMD. Included are examples of the six different components of SMD that comprise my framework.

In Chapter 5, I document the extent of students' sense-making discussions and summarize significant differences in sense-making across students, groups, and areas of the curriculum. In Chapter 6, I then analyze to what extent the list of factors drawn from prior research (as outlined in Chapter8 2) contributed to these significant differences in SMD, and also to what extent any additional factors contributed to these differences.

Finally, in Chapter 7, I comment on (a) the implications of this study for classroom practice in inquiry-based science classrooms, and (b) the type of future research that could be fruitful in helping students engage in small-group discussion in order to better understand the principles of physical science.

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