Scheduling security-critical real-time applications on clusters
Security-critical real-time applications such as military aircraft flight control systems have mandatory security requirements in addition to stringent timing constraints. Conventional real-time scheduling algorithms, however, either disregard applications' security needs and thus expose the applications to security threats or run applications at inferior security levels without optimizing security performance. In recognition that many applications running on clusters demand both real-time performance and security, we investigate the problem of scheduling a set of independent real-time tasks with various security requirements. We build a security overhead model that can be used to reasonably measure security overheads incurred by the security-critical tasks. Next, we propose a security-aware real-time heuristic strategy for clusters (SAREC), which integrates security requirements into the scheduling for real-time applications on clusters. Further, to evaluate the performance of SAREC, we incorporate the earliest deadline first (EDF) scheduling policy into SAREC to implement a novel security-aware real-time scheduling algorithm (SAEDF). Experimental results from both real-world traces and a real application show that SAEDF significantly improves security over three existing scheduling algorithms (EDF, least laxity first, and first come first serve) by up to 266.7 percent while achieving high schedulability.

title: Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity
abstract: High quality of security service is increasingly critical for applications running on heterogeneous distributed systems. However, existing scheduling algorithms for heterogeneous distributed systems disregard security requirements of applications. To address this issue, in this paper, we introduce security heterogeneity concept for our scheduling model in the context of distributed systems. Based on the concept, we propose a novel heuristic scheduling algorithm, or SATS, which strives to maximize the probability that all tasks are executed without any risk of being attacked. Extensive experimental studies using real-world traces indicate that the scheduling performance is affected by heterogeneities of security and computational power. Additionally, empirical results demonstrate that with respect to security and performance, the proposed scheduling algorithm outperforms existing approaches under a wide spectrum of workload conditions.

title: Approximate Top-k Structural Similarity Search over XML Documents
abstract: With the development of XML applications, such as Digital Library, XML subscribe/publish system, and other XML repositories, top-k structural similarity search over XML documents is attracting more attention. The similarity of two XML documents can be measured by using the edit distance defined between XML trees in previous work. Since the computation of edit distances is time consuming, some recent work presented some approaches to calculate edit distance by using structural summaries to improve the algorithm performance. However, most existing algorithms for calculating edit distance between trees ignore the fact that nodes in a tree may be of different significance, and the same edit operation costs are assumed inappropriately for all nodes in XML document tree. This paper addresses this problem by proposing a summary structure which could be used to make the tree-based edit distance more rational; furthermore, a novel weighting scheme is proposed to indicate that some nodes are more important than others with respect for structural similarity. We introduce a new cost model for computing structural distance and takes weight information into account for nodes in distance computation in this paper. Compared with former techniques, our approach can approximately answer the top-k queries efficiently. We verify this approach through a series of experiments, and the results show that using weighted structural summaries for top-k queries is efficient and practical.