Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

Cloud computing provides resources to its consumers as a service. The cloud computing paradigm offers dynamic services by providing virtualized resources via the internet for enabling applications, and these services are provided by large-scale data centers known as clouds. Cloud computing is entirely reliant on the internet to provide its services to consumers. Cloud computing offers several advantages, including the fact that users only pay for what they use weekly, monthly, or yearly, that anybody with an internet connection may use the cloud, and that there is no need to purchase resources, hardware, or software on their own. This paper proposes an efficient task scheduling algorithm based on the Jaya algorithm for the cloud computing environment. We evaluate the performance of our method by applying it to three instances. The recommended technique produced the optimal solution in makespan, speedup, efficiency, and throughput, according to the findings

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods

Effect of antibodies and latently infected cells on HIV dynamics with differential drug efficacy in cocirculating target cells

In this paper, we investigate the qualitative behaviors of three viral infection models with two types of cocirculating target cells. The models take into account both antibodies and latently infected cells. The incidence rate is represented by bilinear, saturation and general function. For the first two models, we have derived two threshold parameters, R0 and R1 which completely determined the global properties of the models. Lyapunov functions are constructed and LaSalle's invariance principle is applied to prove the global asymptotic stability of all equilibria of the models. For the third model, we have established a set of conditions on the general incidence rate function which are sufficient for the global stability of the equilibria of the model. Theoretical results have been checked by numerical simulations.The Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah.http://link.springer.com/journal/108192018-06-30hb2017Electrical, Electronic and Computer Engineerin

Analysis of General Humoral Immunity HIV Dynamics Model with HAART and Distributed Delays

This paper deals with the study of an HIV dynamics model with two target cells, macrophages and CD4 + T cells and three categories of infected cells, short-lived, long-lived and latent in order to get better insights into HIV infection within the body. The model incorporates therapeutic modalities such as reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs). The model is incorporated with distributed time delays to characterize the time between an HIV contact of an uninfected target cell and the creation of mature HIV. The effect of antibody on HIV infection is analyzed. The production and removal rates of the ten compartments of the model are given by general nonlinear functions which satisfy reasonable conditions. Nonnegativity and ultimately boundedness of the solutions are proven. Using the Lyapunov method, the global stability of the equilibria of the model is proven. Numerical simulations of the system are provided to confirm the theoretical results. We have shown that the antibodies can play a significant role in controlling the HIV infection, but it cannot clear the HIV particles from the plasma. Moreover, we have demonstrated that the intracellular time delay plays a similar role as the Highly Active Antiretroviral Therapies (HAAT) drugs in eliminating the HIV particles

Optimization Task Scheduling Bee Colony Algorithm for Heterogeneous Cloud Computing Systems

The primary purpose of the task scheduler is to assign tasks to available processors to produce a minimum Makespan without violating precedence constraints. In heterogeneous cloud computing resources, task assignments and schedules significantly impact system operation. In the experimental task scheduling algorithm, a different mapping of the process will result in a different maximum completion time of a batch of tasks (Makespan) on heterogeneous cloud computing resources. Thus, a scheduling algorithm has to define a schedule considering the precedence of child tasks depending on the resources required to reduce makespan. In this paper, we propose an Efficient Artificial Bee Colony Optimization Algorithm (EABCOA) to solve heterogeneous cloud computing resources task assignment and scheduling problems. The basic idea of this process is to exploit the advantages of meta-heuristic algorithms to get the optimal solution for makespan. We evaluate our algorithms performance by applying it to three cases with a different number of tasks and processors. The results show that the proposed approach significantly outperforms other methods in finding the optimal solution for makespan