Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy

Ancestors protocol for scalable key management

AbstractGroup key management is an important functional building block for secure multicast architecture. Thereby, it has been extensively studied in the literature. The main proposed protocol is Adaptive Clustering for Scalable Group Key Management (ASGK). According to ASGK protocol, the multicast group is divided into clusters, where each cluster consists of areas of members. Each cluster uses its own Traffic Encryption Key (TEK). These clusters are updated periodically depending on the dynamism of the members during the secure session. The modified protocol has been proposed based on ASGK with some modifications to balance the number of affected members and the encryption/decryption overhead with any number of the areas when a member joins or leaves the group. This modified protocol is called Ancestors protocol. According to Ancestors protocol, every area receives the dynamism of the members from its parents. The main objective of the modified protocol is to reduce the number of affected members during the leaving and joining members, then 1 affects n overhead would be reduced. A comparative study has been done between ASGK protocol and the modified protocol. According to the comparative results, it found that the modified protocol is always outperforming the ASGK protocol

Enhancing highly-collaborative access control system using a new role-mapping algorithm

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, role based access-control (RBAC) Model is deployed in large organizations. The work in this paper is mainly considering the authorization scalability problem, which comes out due to the increase of shared resources and/or the number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with role-to-role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed role-mapping algorithm. A comparative study has been performed to evaluate the performance of the proposed algorithm with concerning the rule-store size and the authorization response time. According to the results, it is found that the proposed algorithm achieves more saving in the number of stored role-mapping rules which minimizes the rule-store size and reduces the authorization response time. Additionally, the RTR model using the proposed algorithm has been implemented by applying a concurrent approach to achieve more saving in the authorization response time. Therefore, it would be suitable in highly-collaborative cloud environment

Optimum Resource Allocation of Database in Cloud Computing

Cloud computing is a new generation of computing based on virtualization technology. An important application on the cloud is the Database Management Systems (DBMSs). The work in this paper concerns about the Virtual Design Advisor (VDA). The VDA is considered a solution for the problem of optimizing the performance of DBMS instances running on virtual machines that share a common physical machine pool. It needs to calibrate the tuning parameters of the DBMS’s query optimizer in order to operate in a what-if mode to accurately and quickly estimate the cost of database workloads running in virtual machines with varying resource allocation. The calibration process in the VDA had been done manually. This manual calibration process is considered a complex, time-consuming task because each time a DBMS has to run on a different server infrastructure or to replace with another on the same server, the calibration process potentially has to be repeated. According to the work in this paper, an Automatic Calibration Tool (ACT) has been introduced to automate the calibration process. Also, a Greedy Particle Swarm Optimization (GPSO) search algorithm has been proposed and implemented in the VDA instead of the existed greedy algorithm to prevent the local optimum states from trapping the search process from reaching global optima. The main function of this algorithm is to minimize the estimated cost and enhance the VMs configurations. The ACT tool and the GPSO search algorithm have been implemented and evaluated using TPC-H benchmark queries against PostgreSQL instances hosted in Virtual Machines (VMs) on the Xen virtualization environment

Optimization procedure for algorithms of task scheduling in high performance heterogeneous distributed computing systems

In distributed computing, the schedule by which tasks are assigned to processors is critical to minimizing the execution time of the application. However, the problem of discovering the schedule that gives the minimum execution time is NP-complete. In this paper, a new task scheduling algorithm called Sorted Nodes in Leveled DAG Division (SNLDD) is introduced and developed for HeDCSs with consider a bounded number of processors. The main principle of the developed algorithm is to divide the Directed Acyclic Graph (DAG) into levels and sort the tasks in each level according to their computation size in descending order. To evaluate the performance of the developed SNLDD algorithm, a comparative study has been done between the developed SNLDD algorithm and the Longest Dynamic Critical Path (LDCP) algorithm which is considered the most efficient existing algorithm. According to the comparative results, it is found that the performance of the developed algorithm provides better performance than the LDCP algorithm in terms of speedup, efficiency, complexity, and quality. Also, a new procedure called Superior Performance Optimization Procedure (SPOP) has been introduced and implemented in the developed SNLDD algorithm and the LDCP algorithm to minimize the sleek time of the processors in the system. Again, the performance of the SNLDD algorithm outperforms the existing LDCP algorithm after adding the SPOP procedure

Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

Multiway join queries incur high-cost I/Os operations over large-scale data. Exploiting sharing join opportunities among multiple multiway joins could be beneficial to reduce query execution time and shuffled intermediate data. Although multiway join optimization has been carried out in MapReduce, different design principles (i.e., in-memory Big Data platforms, Flink) are not considered. To bridge the gap of not considering the optimization of Big Data platforms, an end-to-end multiway join over Flink, which is called Join-MOTH system (J-MOTH), is proposed to exploit sharing data granularity, sharing join granularity, and sharing implicit sorts within multiple join queries. For sharing data, our previous work, Multiquery Optimization using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of sharing data opportunities among multiple queries. For sharing sort, our previous work, Sort-Based Optimizer for Big Data Multiquery (SOOM), has been introduced to consider the implicit sorts among join queries. For sharing join, additional modules have been tailored to the J-MOTH optimizer to optimize sharing work by exploiting shared pipelined multiway join among multiple multiway join queries. The experimental evaluation has demonstrated that the J-MOTH system outperforms the naive and the state-of-the-art techniques by 44% for query execution time using TPC-H queries. Also, the proposed J-MOTH system introduces maximal intermediate data size reduction by 30% in average over Hadoop-like infrastructures

Pomegranate Seeds Extract Possesses a Protective Effect against Tramadol-Induced Testicular Toxicity in Experimental Rats

Tramadol is a centrally acting opioid analgesic that is extensively used. The chronic exposure to tramadol induces oxidative stress and toxicity especially for patients consuming it several times a day. Previously, we and others reported that tramadol induces testicular damage in rats. This study was conducted to investigate the possible protective effect of pomegranate seed extract (PgSE) against tramadol-induced testicular damage in adult and adolescent rats. Male rats were orally treated with tramadol or in a combination with PgSE for three weeks. Testes were then dissected and analyzed. Histological and ultrastructural examinations indicated that tramadol induced many structural changes in the testes of adult and adolescent rats including hemorrhage of blood vessels, intercellular spaces, interstitial vacuoles, exfoliation of germ cells in lumen, cell apoptosis, chromatin degeneration of elongated spermatids, and malformation of sperm axonemes. Interestingly, these abnormalities were not observed in tramadol/PgSE cotreated rats. The morphometric analysis revealed that tramadol disrupted collagen metabolism by elevating testicular levels of collagen fibers but that was protected in tramadol/PgSE cotreatment at both ages. In addition, DNA ploidy revealed that S phase of the cell cycle was diminished when adult and adolescent rats were treated with tramadol. However, the S phase had a normal cell population in the cotreated adult rats, but adolescent rats had a lower population than controls. Furthermore, the phytochemistry of PgSE revealed a high content of total polyphenols and total flavonoids within this extract; besides, the DPPH free radical scavenging activity was high. In conclusion, this study indicated that PgSE has a prophylactic effect against tramadol-induced testicular damage in both adult and adolescent ages, although the tramadol toxicity was higher in adolescent age to be completely protected. This prophylactic effect might be due to the high antioxidant compounds within the pomegranate seeds