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Dovetail Usage Prediction Model for Resource-Efficient Virtual Machine Placement in Cloud Computing Environment
Hyeongbin Kang, Hyeon-Jin Yu, Jungbin Kim, Heeseok Jeong, Jae-Hyuck Shin, Seo-Young Noh
http://doi.org/10.5626/JOK.2023.50.12.1041
As IT services have migrated to the cloud, efficient resource management in cloud computing environments has become an important issue. Consequently, research has been conducted on virtual machine placement(VMP), which can increase resource efficiency without the need for additional equipment in data centers. This paper proposes the use of a usage prediction model as a method for selecting and deploying hosts suitable for virtual machine placement. The dovetail usage prediction model, which improves the shortcomings of the existing usage prediction models, measures indicators such as CPU, disk, and memory usage of virtual machines running on hosts and extracts features using a deep learning model by converting them into time series data. By utilizing this approach in virtual machine placement, hosts can be used efficiently while ensuring appropriate load balancing of the virtual machines.
Load Balancing for Distributed Processing of Real-time Spatial Big Data Stream
http://doi.org/10.5626/JOK.2017.44.11.1209
A variety of sensors is widely used these days, and it has become much easier to acquire spatial big data streams from various sources. Since spatial data streams have inherently skewed and dynamically changing distributions, the system must effectively distribute the load among workers. Previous studies to solve this load imbalance problem are not directly applicable to processing spatial data. In this research, we propose Adaptive Spatial Key Grouping (ASKG). The main idea of ASKG is, by utilizing the previous distribution of the data streams, to adaptively suggest a new grouping scheme that evenly distributes the future load among workers. We evaluate the validity of the proposed algorithm in various environments, by conducting an experiment with real datasets while varying the number of workers, input rate, and processing overhead. Compared to two other alternative algorithms, ASKG improves the system performance in terms of load imbalance, throughput, and latency.
Network Topology Discovery with Load Balancing for IoT Environment
Hyunsu Park, Jinsoo Kim, Moosung Park, Youngbae Jeon, Jiwon Yoon
http://doi.org/10.5626/JOK.2017.44.10.1071
With today"s complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor"s resource usage.
A Priority Based Multipath Routing Mechanism in the Tactical Backbone Network
Yongsin Kim, Sang-heon Shin, Younghan Kim
The tactical network is system based on wireless networking technologies that ties together surveillance reconnaissance systems, precision strike systems and command and control systems. Several alternative paths exist in the network because it is connected as a grid to improve its survivability. In addition, the network topology changes frequently as forces and combatants change their network access points while conducting operations. However, most Internet routing standards have been designed for use in stable backbone networks. Therefore, tactical networks may exhibit a deterioration in performance when these standards are implemented. In this paper, we propose Priority based Multi-Path routing with Local Optimization(PMPLO) for a tactical backbone network. The PMPLO separately manages the global and local metrics. The global metric propagates to other routers through the use of a routing protocol, and it is used for a multi-path configuration that is guaranteed to be loop free. The local metric reflects the link utilization that is used to find an alternate path when congestion occurs, and it is managed internally only within each router. It also produces traffic that has a high priority privilege when choosing the optimal path. Finally, we conducted a simulation to verify that the PMPLO can effectively distribute the user traffic among available routers.
A Multi-path Routing Mechanism with Local Optimization for Load Balancing in the Tactical Backbone Network
In this paper, we propose MPLO(Multi-Path routing with Local Optimization) for load balancing in the tactical backbone network. The MPLO manages global metric and local metric separately. The global metric is propagated to other routers via a routing protocol and is used for configuring loop-free multi-path. The local metric reflecting link utilization is used to find an alternate path when congestion occurs. We verified MPLO could effectively distribute user traffic among available routers by simulation.
A Dynamic Partitioning Scheme for Distributed Storage of Large-Scale RDF Data
Cheon Jung Kim, Ki Yeon Kim, Jong Hyeon Yoon, Jong Tae Lim, Kyoung Soo Bok, Jae Soo Yoo
In recent years, RDF partitioning schemes have been studied for the effective distributed storage and management of large-scale RDF data. In this paper, we propose an RDF dynamic partitioning scheme to support load balancing in dynamic environments where the RDF data is continuously inserted and updated. The proposed scheme creates clusters and sub-clusters according to the frequency of the RDF data used by queries to set graph partitioning criteria. We partition the created clusters and sub-clusters by considering the workloads and data sizes for the servers. Therefore, we resolve the data concentration of a specific server, resulting from the continuous insertion and update of the RDF data, in such a way that the load is distributed among servers in dynamic environments. It is shown through performance evaluation that the proposed scheme significantly improves the query processing time over the existing scheme.
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