Search Word(s) in Title, Keywords, Authors, and Abstract: Energy Consumption

An Adaptive Transmission Power Control Algorithm for Wearable Healthcare Systems Based on Variations in the Body Conditions
Woosik Lee, Namgi Kim and Byoung-Dai Lee
Page: 593~603, Vol. 15, No.3, 2019
10.3745/JIPS.03.0118
Keywords: Healthcare System, Transmission Power Control, Wireless Body Sensor Network
Show / Hide Abstract
In wearable healthcare systems, sensor devices can be deployed in places around the human body such as the stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient. A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop. If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy consumption more than previous systems that only consider the sensor part. In this paper, we propose a new approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy management of sensor devices. Our proposed approach judges the current channel condition based on the values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL without communication to save the sensor’s batteries. Otherwise, they find an optimal TPL. To compare performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as linear, binary, hybrid, and ATPC.


Routing Techniques for Data Aggregation in Sensor Networks
Jeong-Joon Kim
Page: 396~417, Vol. 14, No.2, 2018
10.3745/JIPS.04.0065
Keywords: Itinerary, R-tree, Routing, Sensor Networks, Spatio-temporal Data
Show / Hide Abstract
GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.


Clustering Algorithm Considering Sensor Node Distribution in Wireless Sensor Networks
Boseon Yu, Wonik Choi, Taikjin Lee and Hyunduk Kim
Page: 926~940, Vol. 14, No.4, 2018
10.3745/JIPS.03.0102
Keywords: CACD, Clustering, EEUC, Node Distribution, WSN
Show / Hide Abstract
In clustering-based approaches, cluster heads closer to the sink are usually burdened with much more relay traffic and thus, tend to die early. To address this problem, distance-aware clustering approaches, such as energy-efficient unequal clustering (EEUC), that adjust the cluster size according to the distance between the sink and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes, because, in randomly distributed sensor networks, the approaches do not guarantee that the cluster energy consumption will be proportional to the cluster size. To address this problem, we propose a novel approach called CACD (Clustering Algorithm Considering node Distribution), which is not only distance-aware but also node density-aware approach. In CACD, clusters are allowed to have limited member nodes, which are determined by the distance between the sink and the cluster head. Simulation results show that CACD is 20%–50% more energy-efficient than previous work under various operational conditions considering the network lifetime.


Significant Motion-Based Adaptive Sampling Module for Mobile Sensing Framework
Muhammad Fiqri Muthohar, I Gde Dharma Nugraha and Deokjai Choi
Page: 948~960, Vol. 14, No.4, 2018
10.3745/JIPS.04.0082
Keywords: Adaptive Sampling, Android Mobile Sensing Framework, Significant Motion Sensor
Show / Hide Abstract
Many mobile sensing frameworks have been developed to help researcher doing their mobile sensing research. However, energy consumption is still an issue in the mobile sensing research, and the existing frameworks do not provide enough solution for solving the issue. We have surveyed several mobile sensing frameworks and carefully chose one framework to improve. We have designed an adaptive sampling module for a mobile sensing framework to help solve the energy consumption issue. However, in this study, we limit our design to an adaptive sampling module for the location and motion sensors. In our adaptive sampling module, we utilize the significant motion sensor to help the adaptive sampling. We experimented with two sampling strategies that utilized the significant motion sensor to achieve low-power consumption during the continuous sampling. The first strategy is to utilize the sensor naively only while the second one is to add the duty cycle to the naive approach. We show that both strategies achieve low energy consumption, but the one that is combined with the duty cycle achieves better result.


An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance
Kathiravan Srinivasan, Chuan-Yu Chang, Chao-Hsi Huang, Min-Hao Chang, Anant Sharma and Avinash Ankur
Page: 989~1009, Vol. 14, No.4, 2018
10.3745/JIPS.01.0031
Keywords: Clusters, Hadoop, MapReduce, Mobile Raspberry Pi, Single-board Computer
Show / Hide Abstract
Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.


Self-Identification of Boundary’s Nodes in Wireless Sensor Networks
Kouider Elouahed Moustafa and Haffaf Hafid
Page: 128~140, Vol. 13, No.1, 2017
10.3745/JIPS.03.0062
Keywords: Boundary Recognition, Military Applications, Military Surveillance, Wireless Sensor Network
Show / Hide Abstract
The wireless sensor networks (WSNs) became a very essential tool in borders and military zones surveillance, for this reason specific applications have been developed. Surveillance is usually accomplished through the deployment of nodes in a random way providing heterogeneous topologies. However, the process of the identification of all nodes located on the network’s outer edge is very long and energy-consuming. Before any other activities on such sensitive networks, we have to identify the border nodes by means of specific algorithms. In this paper, a solution is proposed to solve the problem of energy and time consumption in detecting border nodes by means of node selection. This mechanism is designed with several starter nodes in order to reduce time, number of exchanged packets and then, energy consumption. This method consists of three phases: the first one is to detect triggers which serve to start the mechanism of boundary nodes (BNs) detection, the second is to detect the whole border, and the third is to exclude each BN from the routing tables of all its neighbors so that it cannot be used for the routing.


DTG Big Data Analysis for Fuel Consumption Estimation
Wonhee Cho and Eunmi Choi
Page: 285~304, Vol. 13, No.2, 2017
10.3745/JIPS.04.0031
Keywords: Big Data Analysis, DTG, Eco-Driving, Fuel Economy, Fuel Consumption Estimation, MapReduce
Show / Hide Abstract
Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.


An Improved Zone-Based Routing Protocol for Heterogeneous Wireless Sensor Networks
Liquan Zhao and Nan Chen
Page: 500~517, Vol. 13, No.3, 2017
10.3745/JIPS.03.0072
Keywords: Energy Consumption, Heterogeneous Wireless Sensor Networks, Stable Election Protocol, Zone-Based
Show / Hide Abstract
In this paper, an improved zone-based routing protocol for heterogeneous wireless sensor networks is proposed. The proposed protocol has fixed the sized zone according to the distance from the base station and used a dynamic clustering technique for advanced nodes to select a cluster head with maximum residual energy to transmit the data. In addition, we select an optimal route with minimum energy consumption for normal nodes and conserve energy by state transition throughout data transmission. Simulation results indicated that the proposed protocol performed better than the other algorithm by reducing energy consumption and providing a longer network lifetime and better throughput of data packets


A Tier-Based Duty-Cycling Scheme for Forest Monitoring
Fuquan Zhang, Deming Gao and In-Whee Joe
Page: 1320~1330, Vol. 13, No.5, 2017
10.3745/JIPS.04.0043
Keywords: Link Redundancy, Rechargeable Dynamic Duty Cycle, Tier, Wireless Sensor Networks
Show / Hide Abstract
Wireless sensor networks for forest monitoring are typically deployed in fields in which manual intervention cannot be easily accessed. An interesting approach to extending the lifetime of sensor nodes is the use of energy harvested from the environment. Design constraints are application-dependent and based on the monitored environment in which the energy harvesting takes place. To reduce energy consumption, we designed a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time. The dynamic duty cycle scheduling is realized based on a tier structure in which the network is concentrically organized around the sink node. In addition, the multi-paths preserved in the tier structure can be used to deliver residual packets when a path failure occurs. Experimental results show that the proposed method has a better performance.


Using Mobile Data Collectors to Enhance Energy Efficiency and Reliability in Delay Tolerant Wireless Sensor Networks
Yasmine-Derdour, Bouabdellah-Kechar and Mohammed Fayc?al-Khelfi
Page: 275~294, Vol. 12, No.2, 2016
10.3745/JIPS.03.0032
Keywords: Data Collection, MDCs, Mobility Model, Mobile Relay, Mobile Sink, Simulation, WSNs
Show / Hide Abstract
A primary task in wireless sensor networks (WSNs) is data collection. The main objective of this task is to collect sensor readings from sensor fields at predetermined sinks using routing protocols without conducting network processing at intermediate nodes, which have been proved as being inefficient in many research studies using a static sink. The major drawback is that sensor nodes near a data sink are prone to dissipate more energy power than those far away due to their role as relay nodes. Recently, novel WSN architectures based on mobile sinks and mobile relay nodes, which are able to move inside the region of a deployed WSN, which has been developed in most research works related to mobile WSN mainly exploit mobility to reduce and balance energy consumption to enhance communication reliability among sensor nodes. Our main purpose in this paper is to propose a solution to the problem of deploying mobile data collectors for alleviating the high traffic load and resulting bottleneck in a sink’s vicinity, which are caused by static approaches. For this reason, several WSNs based on mobile elements have been proposed. We studied two key issues in WSN mobility: the impact of the mobile element (sink or relay nodes) and the impact of the mobility model on WSN based on its performance expressed in terms of energy efficiency and reliability. We conducted an extensive set of simulation experiments. The results obtained reveal that the collection approach based on relay nodes and the mobility model based on stochastic perform better.


Two Machine Learning Models for Mobile Phone Battery Discharge Rate Prediction Based on Usage Patterns
Chantana Chantrapornchai and Paingruthai Nusawat
Page: 436~454, Vol. 12, No.3, 2016
10.3745/JIPS.03.0048
Keywords: Battery Discharge Rate, Mobile Battery Usage, Multi-Layer Perceptron, Prediction Model, Support Vector Machine
Show / Hide Abstract
This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.


Energy Consumption Scheduling in a Smart Grid Including Renewable Energy
Nadia Boumkheld, Mounir Ghogho and Mohammed El Koutbi
Page: 116~124, Vol. 11, No.1, 2015
10.3745/JIPS.03.0022
Keywords: Demand Response, Energy Management, Energy Scheduling, Optimization
Show / Hide Abstract
Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.


Energy Efficient Architecture Using Hardware Acceleration for Software Defined Radio Components
Chen Liu, Omar Granados, Rolando Duarte and Jean Andrian
Page: 133~144, Vol. 8, No.1, 2012
10.3745/JIPS.2012.8.1.133
Keywords: Software Communication Architecture, Software Defined Radio, Energy Efficiency, FPGA, Cognitive Radio
Show / Hide Abstract
In order to make cognitive radio systems a practical technology to be deployed in real-world scenarios, the core Software Defined Radio (SDR) systems must meet the stringent requirements of the target application, especially in terms of performance and energy consumption for mobile platforms. In this paper we present a feasibility study of hardware acceleration as an energy-efficient implementation for SDR. We identified the amplifier function from the Software Communication Architecture (SCA) for hardware acceleration since it is one of the functions called for most frequently and it requires intensive floating-point computation. Then, we used the Virtex5 Field- Programmable Gate Array (FPGA) to perform a comparison between compiler floatingpoint support and the on-chip floating-point support. By enabling the on-chip floating-point unit (FPU), we obtained as high as a 2X speedup and 50% of the overall energy reduction. We achieved this with an increase of the power consumption by no more than 0.68%. This demonstrates the feasibility of the proposed approach.


A Clustering Protocol with Mode Selection for Wireless Sensor Network
Aries Kusdaryono and Kyung Oh Lee
Page: 29~42, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.029
Keywords: Ad Hoc Network, Wireless Sensor Networks, Clustering, Routing Protocol
Show / Hide Abstract
Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way, since their energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor networks. In this paper, we introduce a clustering protocol with mode selection (CPMS) for wireless sensor networks. Our scheme improves the performance of BCDCP (Base Station Controlled Dynamic Clustering Protocol) and BIDRP (Base Station Initiated Dynamic Routing Protocol) routing protocol. In CPMS, the base station constructs clusters and makes the head node with the highest residual energy send data to the base station. Furthermore, we can save the energy of head nodes by using the modes selection method. The simulation results show that CPMS achieves longer lifetime and more data message transmissions than current important clustering protocols in wireless sensor networks.


MAP : A Balanced Energy Consumption Routing Protocol for Wireless Sensor Networks
Mohamed Mostafa A. Azim
Page: 295~306, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.295
Keywords: Wireless Sensor Network(WSN), Energy Efficient Routing
Show / Hide Abstract
Network lifetime is a critical issue in Wireless Sensor Networks (WSNs). In which, a large number of sensor nodes communicate together to perform a predetermined sensing task. In such networks, the network life time depends mainly on the lifetime of the sensor nodes constituting the network. Therefore, it is essential to balance the energy consumption among all sensor nodes to ensure the network connectivity. In this paper, we propose an energy-efficient data routing protocol for wireless sensor networks. Contrary to the protocol proposed in [6], that always selects the path with minimum hop count to the base station, our proposed routing protocol may choose a longer path that will provide better distribution of the energy consumption among the sensor nodes. Simulation results indicate clearly that compared to the routing protocol proposed in [6], our proposed protocol evenly distributes the energy consumption among the network nodes thus maximizing the network life time.


On the Handling of Node Failures: Energy-Efficient Job Allocation Algorithm for Real-time Sensor Networks
Hamid Karimi, Mehdi Kargahi and Nasser Yazdani
Page: 413~434, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.413
Keywords: Failure Recovery, Job Allocation, Quality of Service, Real-Time Scheduling, Wireless Sensor Network
Show / Hide Abstract
Wireless sensor networks are usually characterized by dense deployment of energy constrained nodes. Due to the usage of a large number of sensor nodes in uncontrolled hostile or harsh environments, node failure is a common event in these systems. Another common reason for node failure is the exhaustion of their energy resources and node inactivation. Such failures can have adverse effects on the quality of the real-time services in Wireless Sensor Networks (WSNs). To avoid such degradations, it is necessary that the failures be recovered in a proper manner to sustain network operation. In this paper we present a dynamic Energy efficient Real-Time Job Allocation (ERTJA) algorithm for handling node failures in a cluster of sensor nodes with the consideration of communication energy and time overheads besides the nodes’ characteristics. ERTJA relies on the computation power of cluster members for handling a node failure. It also tries to minimize the energy consumption of the cluster by minimum activation of the sleeping nodes. The resulting system can then guarantee the Quality of Service (QoS) of the cluster application. Further, when the number of sleeping nodes is limited, the proposed algorithm uses the idle times of the active nodes to engage a graceful QoS degradation in the cluster. Simulation results show significant performance improvements of ERTJA in terms of the energy conservation and the probability of meeting deadlines compared with the other studied algorithms.


Medium Access Control with Dynamic Frame Length in Wireless Sensor Networks
Dae-Suk Yoo and Seung Sik Choi
Page: 501~510, Vol. 6, No.4, 2010
10.3745/JIPS.2010.6.4.501
Keywords: Sensor Networks, Energy-Efficient MAC, S-MAC
Show / Hide Abstract
Wireless sensor networks consist of sensor nodes which are expected to be battery-powered and are hard to replace or recharge. Thus, reducing the energy consumption of sensor nodes is an important design consideration in wireless sensor networks. For the implementation of an energy-efficient MAC protocol, a Sensor-MAC based on the IEEE 802.11 protocol, which has energy efficient scheduling, has been proposed. In this paper, we propose a Dynamic S-MAC that adapts dynamically to the network-traffic state. The dynamic S-MAC protocol improves the energy consumption of the S-MAC by changing the frame length according to the network-traffic state. Using an NS-2 Simulator, we compare the performance of the Dynamic S-MAC with that of the SMAC protocol.


On Effective Slack Reclamation in Task Scheduling for Energy Reduction
Young Choon Lee and Albert Y Zomaya
Page: 175~186, Vol. 5, No.4, 2009
10.3745/JIPS.2009.5.4.175
Keywords: Scheduling, Energy Awareness, Green Computing, Dynamic Voltage and Frequency Scaling, Data Centers
Show / Hide Abstract
Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energyconscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.


TASL: A Traffic-Adapted Sleep/Listening MAC Protocol for Wireless Sensor Network
Yuan Yang, Fu Zhen, Tae-Seok Lee and Myong-Soon Park
Page: 39~43, Vol. 2, No.1, 2006
None
Keywords: Wireless Sensor Network, MAC protocol, traffic aware, sleep/wake mechanism
Show / Hide Abstract
In this paper, we proposed TASL-MAC, a medium-access control (MAC) protocol for wireless sensor networks. In wireless sensor networks, sensor nodes are usually deployed in a special environment, are assigned with long-term work, and are supported by a limited battery. As such, reducing the energy consumption becomes the primary concern with regard to wireless sensor networks. At the same time, reducing the latency in multi-hop data transmission is also very important. In the existing research, sensor nodes are expected to be switched to the sleep mode in order to reduce energy consumption. However, the existing proposals tended to assign the sensors with a fixed Sleep/Listening schedule, which causes unnecessary idle listening problems and conspicuous transmission latency due to the diversity of the traffic-load in the network. TASL-MAC is designed to dynamically adjust the duty listening time based on traffic load. This protocol enables the node with a proper data transfer rate to satisfy the application¡¯s requirements. Meanwhile, it can lead to much greater power efficiency by prolonging the nodes¡¯ sleeping time when the traffic load of the network decreases. We evaluate our implementation of TASL-MAC in NS-2. The evaluation result indicates that our proposal could explicitly reduce packet delivery latency, and that it could also significantly prolong the lifetime of the entire network when traffic is low.


Distance Functions to Detect Changes in Data Streams
Ulziitugs Bud and JongTae Lim
Page: 44~47, Vol. 2, No.1, 2006
None
Keywords: change detection, distance functions.
Show / Hide Abstract
One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.


Energy-Aware Virtual Data Center Embedding
Xiao Ma, Zhongbao Zhang and Sen Su
Page: 0~0, Vol. 0, No.0, 0
10.3745/JIPS.02.0112
Keywords: Energy-Aware, Particle Swarm Optimization, Virtual Data Center Embedding, Virtual Link, Virtual Node
Show / Hide Abstract
As one of the most significant challenges in the virtual data center, the virtual data center embedding has attracted extensive attention from researchers. The existing research works mainly focus on how to design algorithms to increase operating revenue. However, they ignore the energy consumption issue of the physical data center in virtual data center embedding. In this paper, we focus on studying the energy-aware virtual data center embedding problem. Specifically, we first propose an energy consumption model. It includes the energy consumption models of the virtual machine node and the virtual switch node, aiming to quantitatively measure the energy consumption in virtual data center embedding. Based on such a model, we propose two algorithms regarding virtual data center embedding: one is heuristic, and the other is based on particle swarm optimization. The second algorithm provides a better solution to virtual data center embedding by leveraging the evolution process of particle swarm optimization. Finally, experiment results show that our proposed algorithms can effectively save energy while guaranteeing the embedding success rate.