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An emerging technology of cloud computing has major issue as scheduling the task and resource allocating. To avoid this issue, there are suggestions on different scheduling algorithm and that has its own merits and demerits. The proposed virtual task scheduling in multi cloud environment using three different scheduling algorithms such as equal load balancing (ELB) algorithm, high priority scheduling algorithm and rate based scheduling (RBS) algorithm. These different scheduling algorithms are used based on the number of tasks and number of virtual machine in multi cloud environment architecture. If the number of task is equal to number of virtual machine, ELB scheduling algorithm is used. If the number of tasks is greater than the number of virtual machine, high prioritization scheduling algorithm is used. If the number of tasks are lesser than the number of virtual machines, RBS algorithm is used. By using these three different scheduling algorithms we can improve the makespan, average efficiency of the multi cloud computing. Simulation results have analyzed the overall performance by comparing these three different scheduling algorithms. From these simulation results, proposed virtual task scheduling which increases the makespan and reduces the delay and energy consumption.

In this paper, researching on task scheduling is a way from the perspective of resource allocation and management to improve performance of Hadoop system. In order to save the network bandwidth resources in Hadoop cluster environment and improve the performance of Hadoop system, a ReduceTask scheduling strategy that based on data-locality is improved. In MapReduce stage, there are two main data streams in cluster network, they are slow task migration and remote copies of data. The two overlapping burst data transfer can easily become bottlenecks of the cluster network. To reduce the amount of remote copies of data, combining with data-locality, we establish a minimum network resource consumption model (MNRC). MNRC is used to calculate the network resources consumption of ReduceTask. Based on this model, we design a delay priority scheduling policy for the ReduceTask which is based on the cost of network resource consumption. Finally, MNRC is verified by simulation experiments. Evaluation results show that MNRC outperforms the saving cluster network resource by an average of 7.5% in heterogeneous.

In today's scaled out systems, co-scheduling data analytics work with high priority user workloads is common as it utilizes better the vast hardware availability. User workloads are dominated by periodic patterns, with alternating periods of high and low utilization, creating promising conditions to schedule data analytics work during low activity periods. To this end, we show the effectiveness of machine learning models in accurately predicting user workload intensities, essentially by suggesting the most opportune time to co-schedule data analytics work. Yet, machine learning models cannot predict the effects of performance interference when co-scheduling is employed, as this constitutes a "new" observation. Specifically, in tiered storage systems, their hierarchical design makes performance interference even more complex, thus accurate performance prediction is more challenging. Here, we quantify the unknown performance effects of workload co-scheduling by enhancing machine learning models with queuing theory ones to develop a hybrid approach that can accurately predict performance and guide scheduling decisions in a tiered storage system. Using traces from commercial systems we illustrate that queuing theory and machine learning models can be used in synergy to surpass their respective weaknesses and deliver robust co-scheduling solutions that achieve high performance.