Real-time applications are indispensable for conducting research and business in government, industry, and academic organizations. Recently, real-time applications with security requirements increasingly emerged in large-scale distributed systems such as Grids. However, the complexities and specialties of diverse security mechanisms dissuade users from employing existing security services for their applications. To effectively tackle this problem, in this paper we propose a security middleware (SMW) model from which security-sensitive real-time applications are enabled to exploit a variety of security services to enhance the trustworthy executions of the applications. A quality of security control manager (QSCM), a centerpiece of the SMW model, has been designed and implemented to achieve a flexible trade-off between overheads caused by security services and system performance, especially under situations where available resources are dynamically changing and insufficient. A security-aware scheduling mechanism, which plays an important role in QSCM, is capable of maximizing quality of security for real-time applications running in distributed systems as large-scale as Grids. Our empirical studies based on real world traces from a supercomputing center demonstratively show that the proposed model can significantly improve the performance of Grids in terms of both security and schedulability.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.