Ant colony based optimization model for QoS-based task scheduling in cloud computing environment

Cloud computing has evolved with various techniques for satisfying the needs of users to reduce costs and offer better results. The users' needs may include sharing of resources like memories, processors, apps, information, data, applications, etc., whereas, performance should be in terms of improved DC processing period & response time. Apart from the above-said, there is also a requirement to manage the stability of the system and be flexible to make amendments to the system. Static data is used to optimize the project schedule. However, the traditional does not take into account the personnel allocation matrix when scheduling projects. The ACO model is not a suitable solution to the scheduling problem. The classic ACO methodology operates in two phases: the first phase uses an event-based scheduler to address the complex planning issue. Planning and allocating resources for a project are both constrained by these two approaches. The event-based ACO model was developed to match the resource restrictions and activity schedule to handle dynamic data allocation. When it comes to multi-objective scheduling, EBS with ACO is not an adequate approach. An updated ACO technique to optimum global search employing a neural network approach was presented to schedule many activities to tackle the difficulty of multiple objectives. Using the suggested multi-objective technique, an activity with a defined number of tasks and necessary resources may be optimally organized. The algorithm LBACO is the most effective one to apply for optimizing the objective function, according to the test results, as well as the fastest. An advantage of ACO over other techniques is its ability to provide better plans with higher statistics and mean access times, as well as more consistent job assignments.