Ahmed R. Kadhim Al- Khafajee

Abstract: Cloud computing has demonstrated to be a modern model that offers IT assets depend on the payper- use basis as a service over the internet. Scientific workflow applications are benefiting from running on the cloud computing services. However, the challenge is to optimize workflow scheduling algorithms are still required an additional work. This study proposes max-min+ which is an extension for the max-min algorithm that is able to minimize the total execution time and cost of workflow execution. We tested the proposed algorithm via WorkflowSim with using 5 realistic scientific workflow applications. The results of max-min+ outperforms the original max-min algorithm by minimizing the overall workflow execution time.

Internet of Things (IoT) is a sophisticated concept of the traditional internet. In IoT, all things in our lives can be connected with the internet or with each other to exchange data and perform specific functions through the network. However, combining several devices-especially by unskilled users-may pose a number of security risks. In addition, some commonly used communication protocols in the IoT area are not secure. Security, on the other hand, increases overhead by definition, resulting in performance degradation. The Message Queuing Telemetry Transport (MQTT) protocol is a lightweight protocol and can be considered as one of the most popular IoT protocols, it is a publish/subscribe messaging transport protocol that uses a client-server architecture. MQTT is built to run over TCP protocol, thus it does not provide any level of security by default. Therefore, Transport Layer Security (TLS) can be used to ensure the security of the MQTT protocol. This paper analyzed the impact on the performance and security of the MQTT protocol in two cases. The first case, when using TLS protocol to support the security of the MQTT protocol. The second case, using the traditional MQTT without providing any level of security for the exchanged data. The results indicated that there is a tradeoff between the performance and the security when using MQTT protocol with and without the presence of TLS protocol.

Wireless multimedia sensor networks (WMSNs) are special kinds of wireless sensor networks (WSN) that can send multimedia data such as audio and video streams. Sensors used in WMSNs are smart, tiny, and resource constraint sensor nodes (SNs) distributed in a large area. Typically, multimedia data are large in comparison to other data types. As a result, WMSNs have to deal with high volumes of packet transmission, leading to a high rate of packet loss and network congestion. Network congestion can significantly affect the quality of service and usually lead to high energy consumption. Thus, to improve the quality of service (QoS) and transmission performance, it is necessary to deal with network congestion. In the past, different packet prioritizing methods were proposed to deal with this issue. However, improving QoS usually requires high energy to function correctly. Consequently, using rechargeable sensor nodes to reduce energy consumption is an acute solution. In this research, priority-based data collection is considered to cut down on data distortion and improve the QoS of the multimedia sensor network. Additionally, energy harvesting sensor nodes were used to reduce energy consumption due to the high transmission rate. The simulation result shows a noticeable improvement in the performance of our proposed method in comparison to previous methods.

Cloud computing has become an essential technology in many industries due to its scalability and costeffectiveness. The dynamic nature of cloud computing, including elasticity, on-demand provisioning, diverse resource types, and varied pricing model, presents a significant challenge in scheduling tasks for cloud-based systems, especially when considering user quality of service (QoS) constraints such as deadlines and budgets. Therefore, in order to optimize the performance of the cloud systems and end-user satisfaction, an efficient budget and deadline-aware scheduling model is necessary. Game theory provides a framework for modeling and analyzing the strategic interactions between self-interested entities, which makes it an ideal tool for task scheduling in cloud computing. Additionally, the versatility of game models enables the analysis of various cloud computing architectures. This paper proposes a dynamic Stackelberg (leader-follower) game model for modeling the interactions between tasks, scheduler, and cloud resources to find an equilibrium for the game under both budget and deadline constraints. The proposed dynamic task scheduling based on Stackelberg game (DTSSG) model is assisted by the pricing model and satisfaction factors to select the optimal virtual machine for processing the user task. To achieve high average resource utilization, the utilization factor of the cloud resources is considered in the proposed work. Experimental results show that the Stackelberg model equilibrium has been very effective in scheduling the user tasks across the data center resources by selecting the optimal virtual machines. The results demonstrate improved execution efficiency in terms of decreased makespan by 30%, reduced number of deadline violations by 52%, decreased total gain cost by 27.13% and increased provider profit by 19.15 % on average as compared to existing deadline budget scheduling (DBS), genetic algorithm, and MAX–MIN methods. Also, the results show the effectiveness of the proposed work in terms of increased throughput by 59.4 % and decreased makespan by 27.95% using Google cloud jobs dataset (GoCJ) as compared to existing gradient-based optimization (GBO), multi-verse optimizer (MVO), enhanced multi-verse optimizer (EMVO).

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution