Provision of adaptive access control is key to allowing users harness the full potential of ubiquitous computing environments. In this paper, we introduce the M-Zones Access Control (MAC) process, which provides user-centric attribute-based access control, together with automatic reconfiguration of resources in response to the changes in the set of users physically present in the environment. U...

In many modern file systems files must be periodically reorganized on disk in order to preserve disk space and provide for efficient I/O operation. Devising adaptive file system reorganization policies is a good first step towards increased flexibility, but does not achieve the goal of separating these policies from the underlying mechanisms of the operating system. The ClassAd language used to...

We investigate dynamic adaptation policies for rate adaptive multimedia streams in a network where each route traverses at most one bottleneck link. Dynamic adaptation allows clients to dynamically adapt the stream subscription level, i.e., time-average stream rate, in response to changes in available link capacity, and allows the system to maintain a lower blocking probability than is possible...

For supporting the design of self-adaptive computing systems, the PSCEL language offers a principled approach that relies on declarative definition of adaptation and authorisation policies enforced at runtime. Policies permit managing system components by regulating their interactions and by dynamically introducing new actions to accomplish task-oriented goals. However, the runtime evaluation o...

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to s...

We consider the problem of how to design resource allocation policies that both provide good performance at predicted environmental conditions and are robust against changes or misprediction of the environmental conditions. We evaluate various common threshold-based allocation policies within a simple model, where there is a clear tradeoff between the (conflicting) goals of good performance and...

This paper investigates how adaptive software agents may utilize reinforcement learning algorithms such as Q-learning to make economic decisions such as setting prices in a competitive marketplace. For a single adaptive agent facing xed-strategy opponents, ordinary Q-learning is guaranteed to nd the optimal policy. However , for a population of agents each trying to adapt in the presence of oth...

We study convex optimization problems with side constraints in a multiclass M/G/1 queue with controllable service rates. In the simplest problem of optimizing linear costs with fixed service rate, the cμ rule is known to be optimal. A natural question to ask is whether such simple policies exist for more complex control objectives. In this paper, combining the achievable region approach in queu...

Adaptive space-sharing scheduling algorithms tend to improve the performance of clusters by allocating processors to jobs based on the current system load. The focus of existing adaptive algorithms is on dedicated homogeneous and heterogeneous clusters. However commodity clusters are naturally non-dedicated and tend to be heterogeneous over the time as cluster hardware is usually upgraded and n...

We consider Bayesian information collection, in which a measurement policy collects information to support a future decision. This framework includes problems in ranking and selection, reinforcement learning, and continuous global optimization. We give sufficient conditions under which measurement policies achieve asymptotically minimal expected loss. Achieving asymptotically minimal expected l...