RACC

Abstract

Resource optimization has always been a big challenge in modern data centers. The process of performing workload consolidation on a minimal number of physical machines is becoming more complex when these data centers began supporting containers in addition to virtual machines (VMs). With the increasing usage of containers with VMs in data centers, it becomes critical to address this problem from the container's point of view - that is to optimally allocate containers in the fewest number of physical hosts. Depending on the type of application workload or tasks, infrastructure providers may provision separate containers to handle each task. These tasks may have different resource demands, such as: some of these tasks are CPU intensive, some memory intensive, some I/O intensive and some may be network intensive. Also, the physical machines in the data center are heterogeneous i.e. the hardware configuration (resource capacity) of these machines may differ from each other. Hence, the challenge is to consolidate all the active containers with different resource requirements on the minimum number of physical machines that are not even. We formulate a multi-resource bin packing problem and propose a Deep Learning technique called Fit-for-Packing to place a near-optimal number of containers on a physical machine. Experimental results show that our model achieves an average training accuracy of 82.01% and an average testing accuracy of 82.93%.