Abstract-In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

Steganography has become an important method for concealed communication especially through image files. Recent proposed steganographic methods employ multiple levels of complex techniques. Hence, there is an increasing significance for hardware implementation and its performance metrics. The objective of this article is to analyze and model the performance of FPGA hardware implementations of several spatial steganography methods, including: least significant bit (LSB), random LSB, mix-bit LSB and texture method. This paper presents innovative models to estimate energy-to-embed-secret-bit, peak signal-to-noise-ratio (PSNR) energy cost, power and resources in complex systems. Examining the performance results of the FPGA implementations shows that embedding misalignment degrades the performance, and random embedding increases resources by 43% and power by 13%. Furthermore, the mix-bit method has the best results in terms of balancing the energy consumption and PSNR. Moreover, the accuracy of the model to predict the energy to embed a single secret bit is 2%, and the accuracy of the model to predict complex system performance is 1% for hardware resources and 16.6% for power.

Firewalls are an essential part of any information security system being the first defense line against security attacks. The sea-saw effect between firewalls and network performance is most concerning to network users; where strict security settings result in weak network performance and permeant security settings allow for a stronger one. Hence, evaluating firewall platforms and their impact on network performance is important when assessing the effectiveness of network security. In this paper, we present an assessment methodology to analyze the performance of different firewalls platforms. The analysis considers the following metrics: delay, jitter, throughput, and packet loss. Moreover, the information security of the firewalls is also tested by applying a set of attacks and observing the reaction of the firewalls. The proposed assessment methodology is tested by performing real experiments on different types of firewalls including those that are personal and network-based. Moreover, a quantitative study is conducted to explore the level of knowledge among the educated category in the community, represented by a sample of college students, on the importance of firewall and their use.

In cognitive radio networks (CRNs), unlicensed users (secondary users-SUs) lease free spectrum with quality of service (QoS) guarantees from a multitude of spectrum owners (primary users, PUs) based on service level agreements (SLA). Free spectrum is used to establish the links of secondary network. The amount of leased spectrum influences the admitted number of SU's requests, PUs' profits, and the cost of renting spectrum. Hence, the PU can maximise its profit by adapting its resources to the changes in the traffic load and SLA costs conditions. We propose a novel approach that maximizes PU's profit using economic model. Our economic model integrates the network routing with the adaptation of the capacity of secondary network links. For SUs, QoS should be maintained while adapting the secondary network capacity. Our adaptation scheme is based on the profit maximisation. The Markov decision process (MDP) is used to derive the adaption scheme. Numerical results show the ability of the proposed scheme to attain the optimal profit under different conditions and constraints.

Thermodynamic modelling of Mn-Sn and Mn-Sr binary systems is carried out using the reliable data from the literature. Thermodynamic properties of the binary liquid solutions are described using the modified quasi-chemical model. The calculated phase diagrams and the thermodynamic properties are found to be in good agreement with the experimental data from the literature. A self-consistent thermodynamic database for the Mg-Mn-{Sn, Sr} systems is constructed by combining the thermodynamic descriptions of their constituent binaries. The constructed database is used to calculate and predict liquidus projection and invariant reactions of these ternary systems. The Mg-Mn-Sr system has nine ternary eutectic reactions, two saddle points and eleven crystallisation fields. Mg-Mn-Sn has four saddle points, two quasi-peritectic and six ternary eutectic reactions.

No abstract

No abstract

No abstract