78 research outputs found
Towards the decentralized coordination of multiple self-adaptive systems
When multiple self-adaptive systems share the same environment and have
common goals, they may coordinate their adaptations at runtime to avoid
conflicts and to satisfy their goals. There are two approaches to coordination.
(1) Logically centralized, where a supervisor has complete control over the
individual self-adaptive systems. Such approach is infeasible when the systems
have different owners or administrative domains. (2) Logically decentralized,
where coordination is achieved through direct interactions. Because the
individual systems have control over the information they share, decentralized
coordination accommodates multiple administrative domains. However, existing
techniques do not account simultaneously for both local concerns, e.g.,
preferences, and shared concerns, e.g., conflicts, which may lead to goals not
being achieved as expected. Our idea to address this shortcoming is to express
both types of concerns within the same constraint optimization problem. We
propose CoADAPT, a decentralized coordination technique introducing two types
of constraints: preference constraints, expressing local concerns, and
consistency constraints, expressing shared concerns. At runtime, the problem is
solved in a decentralized way using distributed constraint optimization
algorithms implemented by each self-adaptive system. As a first step in
realizing CoADAPT, we focus in this work on the coordination of adaptation
planning strategies, traditionally addressed only with centralized techniques.
We show the feasibility of CoADAPT in an exemplar from cloud computing and
analyze experimentally its scalability
Spatio-Temporal Context in Agent-Based Meeting Scheduling
Meeting scheduling is a common task for organizations of all sizes. It involves searching for a time and place when and where all the participants can meet. However, scheduling a meeting is generally difficult in that it attempts to satisfy the preferences of all participants. Negotiation tends to be an iterative and time consuming task. Proxy agents can handle the negotiation on behalf of the individuals without sacrificing their privacy or overlooking their preferences. This thesis examines the implications of formalizing meeting scheduling as a spatiotemporal negotiation problem. The “Children in the Rectangular Forest” (CRF) canonical model is applied to meeting scheduling. By formalizing meeting scheduling within the CRF model, a generalized problem emerges that establishes a clear relationship with other spatiotemporal distributed scheduling problems. The thesis also examines the implications of the proposed formalization to meeting scheduling negotiations. A protocol for meeting location selection is presented and evaluated using simulations
Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law
In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to ef¿ciently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics.Work funded by IEA (TIN2006-15662-C02-01), AT (CONSOLIDER CSD2007-0022, INGENIO 2010) and EVE (TIN2009-14702-C02-01 and 02). Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe
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