Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center. (October 2015) - Shaw, Subhadra Bose; Singh, Anil Kumar
Graphical abstract: Highlights: Time-series based forecasting methods are used to predict future load of a system. If the current and the next predicted load of a server exceed the dynamic upper threshold then migration will take place. The forecasting methods are also used to predict multiple (n) future load of the system. Our algorithms are able to find more suitable destination host for VM placement. They are capable of saving energy by reducing number of over-utilized hosts and virtual machine migration. Maximum QoS requirements are fulfilled due to less violation of SLA. Abstract: The increasing demand of cloud computing motivates the researchers to make cloud environment more efficient for its users and more profitable for the providers. Though virtualization technology helps to increase the resource utilization, still the operational cost of cloud gradually increases mainly due to the consumption of large amount of electrical energy. So to reduce the energy consumption virtual machines (VM) are dynamically consolidated to lesser number of physical machines (PMs) by live VM migration technique. But this may cause SLA violation and the provider is penalized. So to maintain an energy-performance trade-off, the number of VM migration should be minimized. VM migration primarily takes place in two cases: for hotspot mitigation and to switch off the underutilized nodes by migrating all its VMs. If a host is found to be overloaded then instead of immediately migrating some of its VMs we can check whether the migration is really required or not. For this we have proposed a load prediction algorithm to decide whether the migration will be performed or not. After the decision has been taken the algorithm finds a suitable destination host where the VM will be shifted. For this we have proposed a novel approach to decide whether a particular host is suitable as destination depending on its probable future load. We have simulated our algorithms in CloudSim using real world workload traces and compared them with the existing benchmark algorithms. Results show that the proposed methods significantly reduce the number of VM migration and subsequent energy consumption while maintaining the SLA.