Data centers (DC) host a large number of servers, computing devices and computing infrastructure, and hence incur significant amount of electricity / energy. This also results in high amount of heat produced, which if not addressed can lead to overheating of computing devices in the DC. In addition, temperature mismanagement can lead to thermal imbalance within the DC environment, which may result in the creation of hotspots. The energy consumed during the life of a hotspot is greater than the energy saved during computation. Hence, the thermal imbalance impacts on the efficiency of the cooling mechanism installed inside the DC, and the consequence is high energy consumption. One popular strategy to minimize energy consumption is to optimize resource allocation within the DC. However, existing scheduling strategies do not consider the ambient effect of the surrounding nodes at the time of job allocation. Moreover, thermal-aware resource scheduling as an optimization problem is a topic that is relatively understudied in the literature. Therefore, in this research, we propose a novel Game-based Thermal-Aware Resource Allocation (GTARA) strategy to reduce the thermal imbalance within the DC. Specifically, we use cooperative game theory with a Nash-bargaining solution concept to model the resource allocation as an optimization problem, where user jobs are assigned to the computing nodes based on their thermal profiles and their potential effect on the surrounding nodes. This allows us to improve thermal balance and avoid hotspots. We then demonstrate the effectiveness of GTARA in comparison to TACS (Thermal-aware Control Strategy), TASA (Thermal-aware Scheduling Algorithm) and FCFS (First-Come First-Served), in terms of minimizing thermal imbalance and hotspots.

The purpose of this paper is to investigate the chaotic influence of the fractional order jerk system with the theoretical execution of circuit and practical consequent on cryptography. The Caputo fractional derivative (CFD) has been used for the commensurate order and obtains the necessary condition to appear chaos using Lyapunov exponent (LE). The existence, uniqueness of the system is analyzed, and the stabilities of the equilibrium points are explored. The output of the electronic circuit equations has been inspected graphically and the values of passive components resistor, capacitor and voltage are tabulated. The scrambling protocol is designed in multiple paradigmatic language Python for various aspects of information security such as sound and image for confidentiality. The analog and numerical simulations carry out in Multisim and Mathematica respectively, to see the effects of physical parameters on phase portraits which are incorporated through graphs and tables. The significance level of occur fluctuation is also measured by a statistical package the NIST test suite to ensure the stream of generating numbers for a particular cryptographic application. Furthermore, originality of the system also tested with the help of error measuring tool named mean absolute error (MAE) and found that the performance index of the designed system is in good agreement.

One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimise project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.

Project management tools are critical for managing software projects, decreasing costs, accelerating timelines, and using available resources effectively. However, project managers find it very tedious to choose a suitable software project management (SPM) tool from available tools in the market. The appropriateness of the SPM tool depends on the nature of the software project being developed – an SPM tool suitable for a large-scale software project may not be appropriate for small-scale projects. However, due to the complexities of the software projects and the development conditions, the selection of an SPM tool is often biased by personal preferences or marketing publicities which may lead to inefficiencies in SPM and consequently, lead to higher cost and lower quality of end product. In this work, we present a decision support framework for SPM tool selection. The proposed system develops a set of selection criteria based on an analytic network process (ANP). To demonstrate the internal working of the proposed framework, we put forward an example of the tool selection problem using the proposed system. Furthermore, the proposed system can be used as is for SPM tool selection for other than software projects.