Cedar is a new authorization policy language designed to be ergonomic, fast, safe, and analyzable. Rather than embed authorization logic in an application’s code, developers can write that logic as Cedar policies and delegate access decisions to Cedar’s evaluation engine. Cedar’s simple and intuitive syntax supports common authorization use-cases with readable policies, naturally leveraging concepts from role-based, attribute-based, and relation-based access control models. Cedar’s policy structure enables access requests to be decided quickly. Cedar’s policy validator leverages optional typing to help policy writers avoid mistakes, but not get in their way. Cedar’s design has been finely balanced to allow for a sound and complete logical encoding, which enables precise policy analysis, e.g., to ensure that when refactoring a set of policies, the authorized permissions do not change. We have modeled Cedar in the Lean programming language, and used Lean’s proof assistant to prove important properties of Cedar’s design. We have implemented Cedar in Rust, and released it open-source. Comparing Cedar to two open-source languages, OpenFGA and Rego, we find (subjectively) that Cedar has equally or more readable policies, but (objectively) performs far better.

Recent advances in energy transfer technology is boosting the development of renewable sensor networks. To sustain such a network, a mobile robot travels from node to node to recharge each sensor before its battery runs out. Consider each node's recharge as a real-time task, the robot needs to serve these tasks by their deadlines. This represents a class of challenging mobility scheduling problems, where the nodes' deadlines and spatial distribution are often at odds with each other. In this paper, we focus on the scenario where nodes have heterogeneous energy consumption rates, and our goal is to maximize the percentage of nodes alive. We formulate this scheduling problem and prove its NP-completeness. To solve this problem, we propose a spatial dependent task scheduling algorithm, which quantifies the impact of scheduling proximate tasks on the other tasks. With extensive simulations, we reveal the trade-offs of existing solutions under a wide range of network scenarios. Our evaluation results show that our algorithms out-perform classical TSP scheduler by up to 10% and 85% in terms of coverage ratio and average tardiness, respectively.

Recent advances in energy transfer technology is boosting the development of renewable sensor networks. To sustain such a network, a mobile robot travels from node to node to recharge each sensor before its battery runs out. To solve this problem, we propose a spatial dependent task scheduling algorithm, which quantifies the impact of scheduling proximate tasks on the other tasks. Our evaluation results show that our algorithms out-perform classical TSP scheduler by up to10% and 85% in terms of coverage ratio and average tardiness, respectively.

Providing QoS and performance guarantees to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, current approaches are not efficient and do not scale well. In this paper, we propose a linear algorithm for real-time divisible load scheduling. Unlike existing approaches, the new algorithm relaxes the tight coupling between the task admission controller and the task dispatcher. By eliminating the need to generate exact schedules in the admission controller, the algorithm avoids high overhead. We experimentally evaluate the new algorithm. Simulation results demonstrate that the algorithm scales well, can schedule large numbers of tasks efficiently, and performs similarly to existing approaches in terms of providing real-time guarantees.

Providing QoS and performance guarantees to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, previous approaches have no support for advance reservations. However, with the emergence of grid applications that require simultaneous access to multi-site resources, supporting advance reservations in a cluster has become increasingly important. In this paper we propose a new divisible load real-time scheduling algorithm that supports advance reservations in a cluster. Our approach not only enforces the real-time agreement but also addresses the under-utilization concerns raised by advance reservations. The impact of advance reservations on system performance is systematically studied. Simulation results show that, with the proposed algorithm and appropriate advance reservations, the system performance could be maintained at the same level as the no reservation case.