Cloud computing provides unlimited on-demand resources and services through remote servers based on pay-per-use model. It includes Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS). Cloud computing facilitates efficient utilization of computing resources in large-scale cloud data centers. Day-by-day, increasing usage of cloud computing services leads to increasing energy consumption and operational cost. Moreover, it produces high amount of Co2, causing huge environmental damage. Heavy usage of cloud data centers has also become a problem to sacrifice system performance and Quality of Services (QoS). In order to overcome these problems, an efficient job-scheduling algorithm is required to reduce energy consumption and execution time without diminishing performance of the system. Apart from this, a green cloud data center plays a significant role in cloud computing to reduce Co2 emissions. Energy-efficient heuristics model is used to find an optimal solution for executing jobs of varying sizes and timings. In this paper, using Dynamic Voltage Frequency Scaling (DVFS), we introduce Energy-Efficient Job Scheduling (EEJS) algorithm to green cloud data centers. Our proposed algorithm is compared to Energy-Conscious Scheduling algorithm (ECS) and Green Energy-Efficient Scheduling algorithm (Green-EES). Experimental results are evaluated using CloudSim 3.0.3 toolkit and simulation results are validated in low-, medium-, and high-workload conditions. Compared to other two algorithms, EEJS demonstrates reduced energy consumption and execution time without violating Service Level Agreements (SLA).

Cloud Computing, Job Scheduling, Heuristics Model, DVFS, Energy Consumption, SLA Violation

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