Today's SAN architectures promise unmediated host access to storage (i.e., without going through a server). To achieve this promise, however, we must address several issues and opportunities raised by SANs, including security, scalability and management. Object storage, such as introduced by the NASD work [14], is a means of addressing these issues and opportunities. An object store raises the level of abstraction presented by a storage control unit from an array of 512 byte blocks to a collection of objects. The object store provides "fine-grain," object-level security, improved scalability by localizing space management, and improved management by allowing end-to-end management of semantically meaningful entities. This paper presents a detailed description of how an object store works and describes the design of Antara, our prototype object store. For a cache hit workload, our pure software prototype is able to service roughly 14000 4K I/O requests per second. We also present a layered security model for an object store which separates concerns of access security and network security, leveraging existing security infrastructure.

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In a perfectly-periodic schedule, time is divided into time-slots, and each client gets a time slot precisely every predefined number of time slots, called the period of that client. Periodic schedules are useful in mobile communication where they can help save power in the mobile device, and they also enjoy the best possible smoothness. In this paper we study the question of dispatching in a perfectly periodic schedule, namely how to find the next item to schedule, assuming that the schedule is already given somehow. Simple dispatching algorithms suffer from either linear time complexity per slot or from exponential space requirement. We show that if the schedule is given in a natural tree representation, then there exists a way to get the best possible running time per slot for a given space parameter, or the best possible space (up to a polynomial) for a given time parameter. We show that in many practical cases, the running time is constant and the space complexity is polynomial.

Abstract-In a perfectly-periodic schedule, time is divided into time-slots, and each client gets a time slot precisely every predefined number of time slots. The input to a schedule design algorithm is a frequency request for each client, and its task is to construct a perfectly periodic schedule that matches the requests as "closely" as possible. The quality of the schedule is measured by the ratios between the requested frequency and the allocated frequency for each client (either by the weighted average or by the maximum of these ratios over all clients). Periodic schedules enjoy maximal fairness, and are very useful in many contexts of asymmetric communication, e.g., push systems and Bluetooth networks. However, finding an optimal periodic schedule is NP-hard in general. Tree scheduling is a methodology for developing perfectly periodic schedules with quality guarantees by constructing trees that correspond to periodic schedules. We explore a few aspects of tree scheduling. First, noting that a complete schedule table may be exponential in size, and that using the tree for scheduling directly may require logarithmic time on average, we give algorithms that find the next client to schedule in constant amortized time, using only polynomial space in most practical cases. Second, we present a few heuristic algorithms for generating schedules, based on analysis of optimal tree-scheduling algorithms, for both the average and maximum measures. Simulation results indicate that some of these heuristics produce excellent schedules in practice, sometimes even beating the best known non-periodic schedules.