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Management activities are based on the state of distributed system components, relations of these components, and their behaviour. Since management policies are applied across an abstraction of distributed systems, the quality of decisions is dependent on the representation fidelity of the real system state. Obviously, the data collection process updating the abstract representation of real distributed system components has a major impact on the quality of management decisions. Gathering most topical management data improves the quality of management decisions, but requires a high degree of monitoring activity. This is contradictory to the request for low impact management systems, where the amount of system resources used for management purpose should be as small as possible. In this paper we present a twofold approach to this problem: First a high level management architecture is described where monitoring is performed by distributed agents with generic functionality for filtering and event creation. The distribution of active management agents reduces the amount of management related traffic and avoids a potential bottleneck on a centralized management station. Second an adaptive polling frequency approach is presented which enables the monitoring agents to adapt their polling frequency automatically to different behavioral parameters of managed components. The automatic adaptation reduces the performance impact of the agents significantly while at the same time a high accuracy of management relevant information about critical components is ensured. Implementation aspects of the introduced management architecture in a CORBA environment are also discussed.

Software engineering today relies to a large extent on acquiring and composing software components and other softwarerelated artifacts from different producers, either at design or at run time. For any user of such artifacts, both as developer and as end-user, the question arises how to ensure that these artifacts are not malicious. Complete inspection of acquired code is, if not impossible, at least impractical and uneconomical for commercial software. The user thus has to trust the code, or rather its supplier and the delivery channel. This paper examines different trust models in the software supply chain and their rationales.Any trust-based supply chain also requires as prerequisite a tamper-proof distribution channel. Such channels can theoretically be realized using digital signature technology, but some practical and theoretical challenges remain. The paper outlines the challenges and shortcomings of current commercial approaches, proposes some solutions, and suggests areas for further research.

IT experts expect open distributed processing to become the predominant computing infrastructure in the late nineties. All computer supported work places of large enterprises and organizations will then be networked and will be integrated into cross-regional and cross-sector business and information processes. The size and complexity of such applications, the local autonomy, distribution and heterogeneity of participating subsystems, and their asynchronous interaction, however, require new architectures, strategies, and tools for their technical management. In previous work we placed a production rule interpreter into the monitoring, decision, control action loop to provide flexible, operational semantics of well-understood management policies. In this article we extend this work in two directions. First we map the structure and dynamic behavior of policies into a graph representation. This semantic representation enables a systematic prediction of the effects of policy executions and allows for a better impact analysis in case of policy changes. Then we introduce a declarative event definition mechanism. It supports a causal and temporal correlation of individual events and serves to instantiate and adapt a predefined generic event handler to the specific needs of the actual management application. Such event handlers join in the interaction between monitoring agents and policy interpreter. By event correlation they may reduce the number of events triggering management actions significantly and help to filter secondary events.