Towards energy-aware job consolidation scheduling in cloud

The requirement in addition to consequences in refining efficiency of energy in cloud applications have enlarged owing to speedy evolution and explosion of data center amenities everywhere in the globe. Virtual machines (VMs) embrace pillar of utmost and are frequently associated on the Physical Machine (PM) towards proficiently employ its assets. Various workload scheduling algorithms are proposed to utilize the resources, however they failed to consider the machine heterogeneity resulting in more energy consumption. To overcome these issues, we introduced Job Consolidation Algorithm (JCA) that efficiently utilize the resource in the cloud considering machine heterogeneity, and we implemented DVFS technique which remains efficiently to produce liable replacement among jobs guaranteeing reduction in energy consumption.