• Journal of Internet Computing and Services
• /
• v.19 no.2
• /
• pp.9-15
• /
• 2018

This paper proposes an energy efficient adaptive relay station ON/OFF scheme with different frequency reuse factors (FRFs) to enhance the system throughput and reduce the transmission energy consumption for the transparent mode of 2-hop cellular relay networks (CRNs) based on orthogonal frequency division multiple access and time division duplex. In the proposed scheme, the base station turns on or off the relay stations (RSs) when they are overutilized and undertuilized based on the traffic density of the cell coverage, respectively. Through the simulation results, we show that the proposed scheme outperforms the conventional CRN in terms of the energy consumption with the same system throughput. Further, in order to increase the system throughput with low energy consumption, the best way is FRF 1 when the number of operating RSs is up to 4 and FRF 2 otherwise.

• International Journal of Computer Science & Network Security
• /
• v.21 no.11
• /
• pp.345-353
• /
• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• Journal of Internet Computing and Services
• /
• v.18 no.4
• /
• pp.51-59
• /
• 2017

With the growth of Cloud computing, it is important to consider resource management techniques to minimize the overall costs of management. In cloud environments, each host's utilization and virtual machine's request based on user preferences are dynamic in nature. To solve this problem, efficient allocation method of virtual machines to hosts where the classification of virtual machines and hosts is undetermined should be studied. In reducing the number of active hosts to reduce energy consumption, thresholds can be implemented to migrate VMs to other hosts. By using Fuzzy logic in classifying resource requests of virtual machines and resource utilization of hosts, we proposed an adaptive VM allocation and migration approach. The allocation strategy classifies the VMs according to their resource request, then assigns it to the host with the lowest resource utilization. In migrating VMs from overutilized hosts, the resource utilization of each host was used to create an upper threshold. In selecting candidate VMs for migration, virtual machines that contributed to the high resource utilization in the host were chosen to be migrated. We evaluated our work through simulations and results show that our approach was significantly better compared to other VM allocation and Migration strategies.