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Context. Software metrics are surrogates of software quality. Software metrics can be used to find possible problems or chances for improvements in software quality. However, software metrics are numbers that are not easy to interpret. Previous analysis of software metrics has shown fat tails in the distribution. The skewness and fat tails of such data are properties of many statistical distributions and more importantly the phenomena of the power law. These statistical properties affect the interpretation of software quality metrics. Objectives. The objective of this research is to validate the effect of power laws on the interpretation of software metrics. Method. To investigate the effect of power law properties on software quality, we study five open-source systems to investigate the distribution and their effect on fault prediction models. Results. Study shows that power law behavior has an effect on the interpretation and usage of software metrics and in particular the CK metrics. Many metrics have shown a power law behavior. Threshold values are derived from the properties of the power law distribution when applied to open-source systems. Conclusion. The properties of a power law distribution can be effective in improving the fault-proneness models by setting reasonable threshold values.

Cloud security has become one of the emergent issues because of the immense growth of cloud services. A major concern in cloud security is the insider threat because of the harm that it poses. Therefore, defending cloud systems against insider attacks has become a key demand. This work deals with insider threat in cloud relational database systems. It reveals the flaws in cloud computing that insiders may use to launch attacks and discusses how load balancing across availability zones may increase insider threat. To mitigate this kind of threat, the paper proposes four models, which are peer‐to‐peer model, centralized model, Mobile‐Knowledgebases model, and Guided Mobile‐Knowledgebases model, and it discusses their advantages as well as their limitations. Moreover, the paper provides experiments and analysis that compare among the proposed models, demonstrate their effectiveness, and show the conditions under which they work with highest performance. Copyright © 2016 John Wiley & Sons, Ltd.

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Efficiently scheduling MapReduce tasks is considered as one of the major challenges that face MapReduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized clusters and more power consumption. Virtualized clusters provide a viable solution to support both data locality and better cluster resources utilization. In this paper, we evaluate the major MapReduce scheduling algorithms such as FIFO, Matchmaking, Delay, and multithreading locality (MTL) on virtualized infrastructure. Two major factors are used to test the evaluated algorithms: the simulation time and the energy consumption. The evaluated schedulers are compared, and the results show the superiority and the preference of the MTL scheduler over the other existing schedulers. Also, we present a comparison study between virtualized and non-virtualized clusters for MapReduce tasks scheduling. EVALUATING MAP REDUCE TASKS SCHEDULING ALGORITHMS 5687 as 'datasets whose size are beyond the ability of typical database software tools to capture, store, manage and analyze.' New technologies are needed to be able to extract values from those datasets; such processed data might be used in other fields such as artificial intelligence, data mining, health care, and social networks. International Business Machine researchers [4] characterized big data with the 3Vs: variety, volume, and velocity. Variety is used to refer to the multiple types/formats in which big data is generated such as digits, texts, audios, videos, and log files.The second characteristic is the huge volume of big data which can reach hundreds or thousands of terabytes. The third characteristic is the velocity where processing and analyzing data must be performed in a fast manner to extract value of data within an appropriate time. These characteristics drive for developing new methodologies to deal with such huge amounts of data. So, comes to existence the term 'big data management'.Big data operations are widely used in many technologies, for example, cloud computing, distributed systems, data warehouse, Hadoop, and MapReduce. MapReduce is one of these technologies that are utilized to handle such big data. It is a software framework introduced by Google for processing large amounts of data in a parallel manner [5]. In fact, it provides a set of features such as user-defined functions, automatic parallelization and distribution, fault tolerance, and high availability by data replicating.MapReduce works in two phases: the map phase and the reduce phase. In the map phase, a dedicated node called the master node takes the input, divides it into smaller shared data splits, and assigns them to worker nodes. The worker nodes may perform the same splitting operation, leading to a hierarchal tree structure. The worker node processes the assigned splits and sends the results back to the master node. The reduce phase then begins...