• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.933-937
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• 2009

This paper proposes a dynamic timeout scheduling for energy efficient and accurate aggregation by analyzing the single hop delay in wireless sensor networks based on IEEE 802.15.4. The proposed scheme dynamically configures the timeout value depending on both the number of nodes sharing a channel and the type of wireless media, with considering the results of delay analysis of the single hop delay. The timeout of proposed scheme is much smaller than the maximum single hop delay which is used as the timeout of traditional data aggregation schemes. Therefore the proposed scheme considerably reduces the energy consumption of idle monitoring for waiting messages. Also, the proposed scheme maintains the data accuracy by guaranteeing the reception ratio required by the sensor network applications. Extensive simulation has revealed that proposed scheme enhances energy consumption by 30% with maintaining data accuracy, as compared with the TAG data aggregation.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.12
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• pp.479-486
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• 2017

Volunteer computing is a new computing paradigm that performs operations on idle resources of many nodes. The operation method of the client application for the execution of the volunteer computing is determined by the setting information of the user. Ideal operation requires optimized settings for system features and operating methods of other applications. In this paper, we analyze the usage ratio of CPU and GPU periodically, and develop a manager that dynamically applies optimized options. Through our proposed mechanism, the performance of the task computing is higher than that of the existing Volunteer Computing, and the performance interference is minimized. It is expected that volunteers will be able to provide higher computing resources for Volunteer Computing Project.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.87-103
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• 2006

Advances in computer and network technology provide new computing environment that were only possible with supercomputers before. In order to provide the environment, a distributed runtime system has to be provided, but most of the conventional distributed runtime systems lack in providing dynamic and flexible system reconfiguration depending on workload variance, due to a static architecture of fixed master node and slave working nodes. This paper proposes and implements a new model for distributed job allocation and management which is a distributed runtime system is P2P environment for flexible and dynamic system reconfiguration. The implemented systems enables job program transfer and management, remote compile and execution among cooperative developers based on P2P standard protocol Jxta platform. Since it makes dynamic and flexible system reconfiguration possible, the proposed method has some advantages in that it can collect and utilize idle computing resources immediately at a needed time for distributed job processing. Moreover, the implemented system's effectiveness and performance increase are shown by applying and processing the crawler jobs, in a distributed way, for collecting a large amount of data needed for internet search.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1704-1712
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• 2014

The unused Worst-Case Execution Time (WCET) allocated to a real-time task occurs when the actual execution time of the task can be far less than the WCET preassigned to the task for a schedulability test. Any unused WCET allocated to the task can be exploited to reduce the power consumption of battery-powered sensor nodes through real-time power-aware scheduling techniques. From the distribution perspective of the unused WCET, the unused WCET distribution policy is classified into three types: Conservative Unused WCET (CU-WCET), Moderate Unused WCET (MU-WCET), and Aggressive Unused WCET (AU-WCET) distribution policies. We evaluated the performance of real-time power-aware scheduling techniques incorporating each of three unused WCET distribution policies in terms of low power consumption.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.137-142
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• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.429-439
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• 2006

A desktop grid, which is a computing grid composed of idle computing resources in a large network of desktop computers, is a promising platform for compute-intensive distributed computing applications. However, due to reliability and unpredictability of computing resources, effective scheduling of parallel computing applications on such a platform is a difficult problem. This paper proposes a new scheduling method aimed at reducing the total execution time of a parallel application on a desktop grid. The proposed method is based on utilizing the histories of execution behavior of individual computing nodes in the scheduling algorithm. In order to test out the feasibility of this idea, execution trace data were collected from a set of 40 desktop workstations over a period of seven weeks. Then, based on this data, the execution of several representative parallel applications were simulated using trace-driven simulation. The simulation results showed that the proposed method improves the execution time of the target applications significantly when compared to previous desktop grid scheduling methods. In addition, there were fewer instances of application suspension and failure.

• Journal of Digital Contents Society
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• v.17 no.1
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• pp.51-57
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• 2016

Distributed Computing attempts to harness a massive computing power using a great numbers of idle PCs resource distributed linked to the internet and processes a variety of applications parallel way such as bio, climate, cryptology, and astronomy. In this paper, we develop internet-distributed computing environment, so that we can analyze High Resolution Rainfall Prediction application in meteorological field. For analyze the rainfall forecast in Korea peninsula, we used QPM(Quantitative Precipitation Model) that is a mesoscale forecasting model. It needs to a lot of time to construct model which consisted of 27KM grid spacing, also the efficiency is degraded. On the other hand, based on this model it is easy to understand the distribution of rainfall calculated in accordance with the detailed topography of the area represented by a small terrain model reflecting the effects 3km radius of detail and terrain can improve the computational efficiency. The model is broken down into detailed area greater the required parallelism and increases the number of compute nodes that efficiency is increased linearly.. This model is distributed divided in two sub-grid distributed units of work to be done in the domain of $20{\times}20$ is networked computing resources.

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