Cloud computing services are becoming ubiquitous, and are becoming the primary source of computing power for both enterprises and personal computing applications. One of the fundamental issues in this environment is related to task scheduling. The scheduler should do the scheduling process efficiently in order to utilize the available resources. In this paper a cloud task scheduling policy based on artificial bee colony algorithm compared with different scheduling algorithms has been proposed. The main goal of the proposed algorithm is minimizing the makespan of a given tasks set. Artificial bee colony algorithm models the behavior of honey bees and can be used to find solutions for difficult or impossible combinatorial problems. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the artificial bee colony algorithm outperformed ACO, FPLTF and FCFS algorithms.
we have a common problem in wireless sensor networks which is the missing data problem due to the nature of the wireless communication and the limited resources of the sensor nodes. This problem can't be ignored because it has a negative effect on the applications that use the sensor data. Estimating these missing data is important for the applications that concern with the sensor data. However, the traditional estimation techniques failed to be applied with the sensor data and the existing techniques have high computation complexity, high computation time, or low accuracy. So we introduce the simplified Spatial and Temporal Correlation (STC) estimation algorithm which uses the most related surrounding previous data to increase the accuracy of the estimation and reduce incremental error. The proposed algorithm utilizes the time correlation by using the closet data before the time of missing and utilizes the space correlation by using the data of the nearest sensor depending on the missing pattern. The experimental results show that our algorithm can reduce the error in the estimating process compared with the other algorithms in most of the missing patterns.
With the recent rapid increase of interactive web applications that employ back-end database services, a SQL injection attack has become one of the most serious security threats. This type of attack can compromise confidentiality and integrity of information and database. Actually, an attacker intrudes to the web application database and consequently, access to data. For preventing this type of attack different techniques have been proposed by researchers but they are not enough because most of implemented techniques cannot stop all type of attacks. In this paper our proposed technique are detection of SQL injection and prevention based on first order, second order and blind SQL injection attacks online. The proposed technique implemented in JAVA and evaluated for seven types of SQL injection attacks. Experimental results have shown that the proposed technique is efficient related to execution time overhead. Our technique need to be one second overhead to execution time. Moreover, we have compared the proposed technique with the popular web application vulnerabilities scanner techniques. The most advantages of proposed technique Its easiness to adopt by software developer, having the same syntactic structure as current popular record set retrieval methods.
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