Cloud storage systems generally add redundancy in storing content files such that K files are replicated or erasure coded and stored on N > K nodes. In addition to providing reliability against failures, the redundant copies can be used to serve a larger volume of content access requests. A request for one of the files can either be sent to a systematic node, or one of the repair groups. In this paper, we seek to maximize the service capacity region, that is, the set of request arrival rates for the K files that can be supported by a coded storage system. We explore two aspects of this problem: 1) for a given erasure code, how to optimally split incoming requests between systematic nodes and repair groups, and 2) choosing an underlying erasure code that maximizes the achievable service capacity region. In particular, we consider MDS and Simplex codes. Our analysis demonstrates that erasure coding makes the system more robust to skews in file popularity than simply replicating a file at multiple servers, and that coding and replication together can make the capacity region larger than either alone.

Due to copyright restrictions, the access to the full text of this article is only available via subscription.We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule-based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.TÜBİTA

Abstract

Summary In recent years, the credibility of information on social networks has attracted considerable of interest due to its critical role in the spread of information online. In this paper, we argue that the quality of information created on social networks can be analyzed using its provenance data. In particular, we propose an algorithm that assesses information credibility on social networks in order to detect fake or malicious information using a fuzzy analytic hierarchy process to assign proper weights to the proposed metrics. In order to test the usability of the proposed algorithm, we introduce a prototype implementation and test it on a large‐scale synthetic social provenance dataset. The initial results reveal a proportional relationship between our proposed distance from positivity algorithm and the provenance graph metrics‐based user credibility.

Locating resources of interest in a large resourceintensive environment is a challenging problem. In this paper we present research on addressing this problem through the development of a recommender system to aid in metadata discovery. Our recommender approach uses Conversational Case-Based Reasoning (CCBR), with semantic web markup languages providing a standard form for case representation. We present our initial efforts in designing and developing ontologies for an Earthquake Simulation Grid, to use these to guide case retrieval, discuss how these are exploited in a prototype application, and identify future steps for this approach.