Autonomic System with Strong Ease-of-Use Characteristics - Term Paper Example

 Integration of Affect and Usability in New Media Introduction During 1980, the basic concept of affect was established by Luc Ciompi. Concept of Affect includes the laws of interface involving feeling and thinking. The basic phenomenon behind its discovery is, sentiment and cognition − or emotion and thoughts, affectivity and judgment − are involved in all intellectual or psychological activities. Then the concept of affect extends to common scenarios, through the incorporation of significant ideas from various branches of science. (Calisir, Fethi and Calisir, Ferah, 2004, 510) Consequently, evolved an interdisciplinary valuable meta-theory involving interactions involving feeling and cognition which has several useful implications for daily life, contemporary media, marketing, political affairs, psychology and sociology. Hence the concept of affect is a comprehensive psycho-somatic condition analogous to a particular goal-directed example of energy utilization. (Sobiesiak, Jones, and Lewis, 2008, 283) Affect can be small or long-term, weak or strong, sensible or insensible. The word usability was initially used nearly ten years ago in place of the expression “user friendly “that had obtained an undesirably indistinguishable and prejudiced implication. Usability is the evaluation of the eminence of an individual’s understanding while working with content or services. Following are the 3 different views explaining how usability can be evaluated: • Ergonomic traits of the invention; • Rational effort and mind-set of the user; • Investigating kind of interaction between user and the product, specially ease of product usage and acceptability of the product by the users. This paper describes a real-life example of integration of concept of affect and usability in new media i.e. autonomic system with strong ease-of-use characteristics. This system, the IBM DB2* Universal Database* (UDB) Version 8.1 autonomic computing system, employs a number of usability features combined with autonomic technologies to deliver an administrative interface unlike anything else in the industry. Affect and Usability in Autonomic computing Autonomic computing is all about self-managing systems. It is amongst the best examples of integration of affect and usability in new media. At the core of the autonomic computing initiative is the concept that computers need to be more self-configuring, self-healing, self-optimizing, and self-protecting in order to reduce the overall complexity of a system. Autonomic computing is described as a "closed loop" system, which includes a "monitor-analyze-plan-and-execute" process in order to make decisions. (Smith, A. 2007, 378) From ease-of-use perspective, autonomic computing offers a leap forward in the following ways: Self-configuration Rarely does an IT component work "out of the box." As part of the installation process, there is always a certain amount of configuration required by the user in order to allow the component to work appropriately within the environment in which it is installed. In an autonomic computing system, the system itself can configure its components. Maintaining the current state of a system's configuration (and that of each component) at all times, the system has the ability to predict the impact of a change to the configuration and learn from the results of such changes. It learns the ramifications of each change and decides when it is appropriate to make them. Self-optimization Today, much of an administrator's time is spent keeping a system "tuned" to changing workloads. What works today for one type of workload may no longer be optimal if the workload grows, shrinks, or changes. Manual optimization of systems is both time-consuming and difficult. Quite often it is based on human trial-and-error rather than careful analysis and planning. Using the principle of policy-based decision-making, an autonomic system tunes itself to meet a set of objectives defined in the policies. Policies direct autonomic systems in finding computational partners, prioritizing limited resources, maintaining security, and recovering from failures. Policy specification, though not a new field, takes on an entirely new significance within the context of autonomic computing. Self-protection The state of the art today in many system protection infrastructures involves the use of an alert mechanism to prompt an administrator's response when an intrusion detection system detects what appears to be a potential threat. By reducing or eliminating the role of the administrator in responding to these alerts and taking appropriate security measures automatically, autonomic systems can reduce the time required to re-establish system security. Self-healing In complex e-business infrastructures, it is critical for the system to have the ability to detect inter-component errors and perform "root cause analysis" to help correct them. Because of the heterogeneous nature of an e-business infrastructure spanning multiple components, there is no standard way to correlate and trace transactions across the system. (Nimwegen, Pouw, and Oostendorp, 2005, 20) Each component logs information in its own format, requiring the labor-intensive task of manually correlating events across the infrastructure. In an autonomic system, this information is logged and correlated at the system level, allowing the system to perform event correlation and root cause analysis. One of the key ease-of-use values of an autonomic system derives from the ability to lower the burden of mundane tasks and allow the administrator to focus on higher-order problems and responsibilities. Another key ease-of-use attribute of autonomic systems is the simplification of the administrator's interface. Autonomic computing allows for a less complex and less cluttered view of the system infrastructure because the display of much of what is currently presented to an administrator is no longer necessary. Examples of such simplifications include: 1. Allowing the system to self-query for information rather than prompting the administrator. Through the development of "autonomic widgets" for system consoles, autonomic systems will be able to query themselves for answers to questions that today must be answered by the administrator. Information about the system configuration, network settings (such as IP addresses), and system settings can all be gathered autonomically and not require human intervention. 2. Raising the threshold for alerts delivered to the administrator's console. Autonomic computing allows for advanced filtering of events and alerts that occur in the system. Through pattern recognition and similar technologies, an autonomic system can filter out "false positives" before they reach the console and handle many alerts automatically. 3. Allowing the system to act using learned behaviors. An autonomic system monitors the actions taken by administrators and "learns" from them; that is, when a certain event occurs and the administrator responds, this event/response association is learned. The next time such an event occurs, the system can make recommendations regarding what action to take in response. Over time, the system will be able to take many actions without needing human intervention. Autonomic computing has potential problem areas with respect to ease of use, and systems need to be designed with care. Adding autonomic capabilities to a system can actually make the user experience more challenging-exactly the opposite of what was intended. As stated by Russell et al., ". . . autonomic computing makes effective design of the user experience even more challenging and critical than it is now”. (Nimwegen, Pouw, and Oostendorp, 2005, 24) The reason is that autonomic actions taken by the system must be understandable by the user and capable of review, revision, and alteration. ‘Because such actions are often made autonomously, a heavy burden is placed on the ability of the system to explain what it is doing and why.’ (Russell, Maglio, Dordick, and Neti, 2003, 180) As enterprises adopt and integrate more technology components and realize greater production efficiencies, their IT infrastructures grow in complexity. Quite simply, there is a growing amount of hardware and software to install and configure, many more elements that need to work together properly, and a great deal of pressure to keep it all up and running on a daily basis. It is interesting to note that in such complex, heterogeneous systems, the total cost of ownership (TCO) is increasingly dominated by human costs. Through autonomic computing, system management is simplified not only by improved ease of use, but also by a marked reduction in problems caused through human error, and the subsequent reduction in human interaction required for corrective action. A brief examination of the causes of system crashes (though certainly only a subset of possible system errors) is illustrative. Recent projections suggest that the percentage of system failures caused by human error has grown since 1985 from 15 percent to an estimated 69 percent in 2008.Through autonomic computing, many of the configuration and maintenance operations that human administrators previously had to perform can be automated, resulting in fewer system failures (because the autonomic system will neither forget nor make random errors), and, therefore, a reduction in outages. Of course, autonomic systems can make mistakes as well. The advantage of autonomic computing is, in part, the notion of consistent control-that is, that the autonomic system never forgets and never suffers from random faults, as human administrators certainly do. Patterson et al. suggest that the sharp increase in human-related causes for system crashes is due largely to the increased complexity of heterogeneous systems and middleware. Autonomic computing offers a paradigm for computer systems to deal with this complexity in ways that human beings simply cannot. (Sobiesiak, Jones, and Lewis, 2008, 305) A number of initiatives within IBM and the IT industry are currently focused on using autonomic computing as a way to address system complexity and overall ease of use. The experience of IBM's database technology team offers an excellent example of how a system can be transformed and ease of use can be improved through autonomic computing. Skilled database administrators and application developers have become increasingly rare, and, following the law of supply and demand, increasingly expensive. A 2001 report from D. H. Brown Associates that compared two leading database products, for both data-warehouse and online-transaction-processing (OLTP) applications, found that a significant portion of the TCO was represented by human administration costs. (Sobiesiak, Jones, and Lewis, 2008, 282)The cost for purchasing and support (including the cost of the product licensing) is significantly less than the "build and maintain" cost (which includes the installation, deployment, and ongoing administration of the product.) Researchers in various disciplines have shown interest in studying human computer interaction and user interfaces. The popularity and expansion of hypertext and the World Wide Web (WWW) has accelerated and unprecedented growth in the computer-based information (Sobiesiak, Jones, and Lewis, 2008, 280). Since a variety of techniques have been utilized, and if we know how user interface elements are suited to a specific task, we can develop and enhance user interface designs that are more satisfactorily and applicable to the users. Those who work full-time on databases are typically highly skilled database users and rely heavily on their previous experience to solve problems. When fulltime users are questioned about the level of database automation that they are willing to accept, they typically opt for very low levels. Full-time DBAs are not willing to trust decisions that could affect the performance of their database to an automated solution. They are, however, interested in the system's ability to offer recommendations. The experienced users want to view the recommended commands in detail and have the ability to directly edit them before their execution. Part-time database users are significantly more interested in database automation capabilities than their full-time counterparts. The part-time DBAs are happy to accept system-generated recommendations; these DBAs are willing to take them at face value and do not require a complex explanation of the proposed commands. Conclusion In conclusion, after above discussion one can see an autonomic system as the best example of integration of concept of affect and Usability in new media. Autonomic computing offers a fresh approach toward ease of use, an approach focused on self-management rather than on the simplicity of the interface. The autonomic computing work for DB2 has included a focus on User-Centered Design, and has resulted in the development of a number of powerful usability enhancements for administrators. The latest of these features support system integrity assurance, physical database design, and database tuning. A number of additional features currently under research and development will expand autonomic capabilities for physical database design, advanced problem determination, and system tuning. References Calisir, Fethi and Calisir, Ferah (2004), "The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems," Computers in Human Behavior Vol. 20, No. 4, pp. 505-515. Nimwegen, C. V., Pouw, M., and Oostendorp, H. V. (2005) The Influence of Structure and Reading Manipulation on Usability of Hypertexts. Interacting with Computers, 12, 7-21. Russell, D. M., Maglio, P, R. Dordick, and C. Neti, (2003) "Dealing with Ghosts: Managing the User Experience of Autonomic Computing," IBM Systems Journal 42, No. 1, 177-188. Smith, A. (2007) Towards a Practical Measure of Hypertext Usability. Interacting with Computers, 8 (4), 365-381. Sobiesiak,R. B., Jones, and Lewis, S (2008) "DB2 Universal Database: A Case Study of a Successful User Centered Design Program," International Journal of Human-Computer Interaction 14, Nos. 3-4, 279-309 Read More