The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively. The predictive model’s performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively.

The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. Our simulations show that the proposed game-theoretic technique can minimize the cloud operating cost more effectively than existing approaches.Ĭloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. Our framework performs intelligent workload management that considers heterogeneity in data center compute capability, cooling power, interference effects from task co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network pricing. In this paper, we propose a novel game theory-based workload management framework that takes a holistic approach to the cloud operating cost minimization problem by making intelligent scheduling decisions aware of data transfer costs and the data center queueing delay. However, prior work does not comprehensively and simultaneously consider data center energy costs, data transfer costs, and data center queueing delay. For geo-distributed data centers, many researchers have been exploring strategies for energy cost minimization and intelligent inter-data-center workload distribution separately. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers.

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution.

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