scholarly journals On the impact of job size variability on heterogeneity-aware load balancing

2019 ◽  
Vol 293 (1) ◽  
pp. 371-399
Author(s):  
Ignace Van Spilbeeck ◽  
Benny Van Houdt
Keyword(s):  
Load Balancing ◽  
Size Variability ◽  
The Impact

scholarly journals Back to Basics: Does Seed Size Affect Germination and Plant Uniformity in Broccoli (Brassica Oleracea var Italica)?

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 181
Author(s):  
Celia van Sprang ◽  
Gayathri Rajagopal
Keyword(s):  
Brassica Oleracea ◽  
Seed Size ◽  
Abiotic Factors ◽  
Crop Growth ◽  
Significant Difference ◽  
Field Uniformity ◽  
Direct Seeded ◽  
Subsequent Crop ◽  
Size Variability ◽  
The Impact

Hand harvested crops, such as brassicas and lettuce, are prone to high levels of variability during growth and at harvest. This necessitates multiple harvest passes and substantially increased labour costs for the grower. Both biotic and abiotic factors contribute to this lack of field uniformity. The main objective was to evaluate the impact of broccoli (Brassica oleracea var. Italica) seed size variability on germination, subsequent crop growth and harvest uniformity. An initial experiment was conducted where germination counts across three seed-size categories including, 2.0mm (SS1), 2.3 mm (SS2) and 2.45 mm (SS3), were recorded at 3, 7, 10 and 14 days after sowing (DAS). At 14 DAS, germination was greater in the SS1 (95%) and SS2 (91%) than the SS3 (66%) (P < 0.005). A second experiment evaluated the same seed categories planted under direct seeded (DS) and transplanted (TR) conditions to identify differences in crop growth and development. At 49 DAS, DS plant counts per plot were lowest for the SS3 (54.5 plants plot−1) compared with the SS1 (70.5 plants plot−1) and SS2 (64 plants plot−1). This could be attributed to the reduced seed coat thickness evident in the SS3 (66.3 μm) which can potentially lead to increased damage and mortality of the seed, compared with the SS1 (79.3 μm) and SS2 (73.1 μm). The TR treatment gave greater uniformity with no significant difference (P < 0.05) in plant populations across seed size categories (SS1 = 95, SS2 = 90 and SS3 = 96 plants plot−1).

Load Balancing Using Processor Groups

2000 ◽  
Vol 10 (01) ◽  
pp. 59-72 ◽  
Author(s):  
Hervé GUYENNET ◽  
Michel TREHEL
Keyword(s):  
Load Balancing ◽  
Distributed System ◽  
Modelling And Simulation ◽  
Group Preference ◽  
The Impact ◽  
Partitioning Technique

This paper defines a group partitioning model with the view to improving the load balancing in a distributed system. Using modelling and simulation, we analyze the impact of this new partitioning technique on load balancing strategies. We show that a strategy based on group preference gives efficient results.

scholarly journals Dynamic access class barring and relay assisted radio resource allocation methods for cellular M2M networks

2021 ◽  
Author(s):  
Lilatul Ferdouse
Keyword(s):  
Resource Allocation ◽  
Load Balancing ◽  
Transmission Rate ◽  
Random Access ◽  
Base Station ◽  
Base Stations ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Selection Probability ◽  
The Impact

Cellular based M2M systems generate massive number of access requests which create congestion in the cellular network. The contention-based random access procedures are designed for cellular networks which cannot accommodate a large number of M2M traffic. Moreover, M2M systems share same radio resources with cellular users. Resource allocation problem becomes a challenging issue in cellular M2M systems. In this thesis, we address these two problems by analyzing a contention-based slotted Aloha random access procedure for M2M networks using different performance metrics. The impact of massive M2M traffic over cellular traffic is studied based on different arrival rate, random access opportunity and throughput. An analytical model of selecting a base station (eNB) along with load balancing is developed. Finally, two methods have been presented and evaluated with M2M traffic. First one is dynamic access class barring method which controls RAN level congestion by selecting an appropriate eNB and applying load balancing method. Second one is relay-assisted radio resource allocation method which maximizes the sum throughput of the system by utilizing the available radio resource blocks and relay nodes to the MTC systems. Numerical results show that frame transmission rate influences the selection probability of the base stations. Moreover, the dynamic access class barring parameter along with frame transmission rate improve the overall throughput and access success probability among base stations as well as avoid overload situation in a particular base station.

scholarly journals Integration of 6LOWPAN and Cloud Data Centers: The Role of Link Capacity between Sensor Networks and Cloud Data Centers

2019 ◽  
Vol 9 (1) ◽  
pp. 2159-2164
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Poor Response ◽  
Optimal Time ◽  
Available Bandwidth ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
The Impact

Internet of Things (IoT) and Internet of Mobile Things (IoMT) acquired widespread popularity by its ease of deployment and support for innovative applications. The sensed and aggregated data from IoT and IoMT are transferred to Cloud through Internet for analysis, interpretation and decision making. In order to generate timely response and sending back the decisions to the end users or Administrators, it is important to select appropriate cloud data centers which would process and produce responses in a shorter time. Beside several factors that determine the performance of the integrated 6LOWPAN and Cloud Data Centers, we analyze the available bandwidth between various user bases (IoT and IoMT networks) and the cloud data centers. Amidst of various services offered in cloud, problems such as congestion, delay and poor response time arises when the number of user request increases. Load balancing/sharing algorithms are the popularly used techniques to improve the performance of the cloud system. Load refers to the number of user requests (Data) from different types of networks such as IoT and IoMT which are IPv6 compliant. In this paper we investigate the impact of homogeneous and heterogeneous bandwidth between different regions in load balancing algorithms for mapping user requests (Data) to various virtual machines in Cloud. We investigate the influence of bandwidth across different regions in determining the response time for the corresponding data collected from data harvesting networks. We simulated the cloud environment with various bandwidth values between user base and data centers and presented the average response time for individual user bases. We used Cloud- Analyst an open source tool to simulate the proposed work. The obtained results can be used as a reference to map the mass data generated by various networks to appropriate data centers to produce the response in an optimal time.

Towards An Implementation of A Modified Static Load Balancing Algorithm To Minimize Execution Time

2019 ◽  
Vol 12 (1) ◽  
pp. 69-74
Author(s):  
Hioual Ouided ◽  
Laskri Mhamed Tayeb ◽  
Hemam Sofiane Mounine ◽  
Hioual Ouassila ◽  
Maifi Lyes
Keyword(s):  
Load Balancing ◽  
Failure Probability ◽  
Execution Time ◽  
Static Load ◽  
Optimal Algorithm ◽  
Scheduling Algorithm ◽  
Static Load Balancing ◽  
Load Balancing Algorithm ◽  
Two Parameters ◽  
The Impact

Purpose: The aim of this article is to discuss the impact of static load balancing over a set of heterogeneous processors, where tasks are independent and unitary in static environments, by showing how to distribute task in order to optimize both the average response time and the degree of the resources used. Methods: Implementation of a modified scheduling algorithm, the latter is based on two parameters which are the execution time and the failure probability. The algorithm is based on the results of an optimal algorithm that already exists, with only one parameter that is execution time. Results: The obtained results show that the modified scheduling algorithm gives us the good results. Conclusion: The modified algorithm assumes that the processor has smallest execute time. So, the failure probability increases because of it’s frequently use. The results obtained by testing this proposed algorithm are better than the optimal algorithm.

ALTERNATIF PENANGANAN LOSSES AKIBAT KETIDAKSEIMBANGAN BEBAN PADA TRAFO DISTRIBUSI

2021 ◽  
Vol 9 (1) ◽  
pp. 83-90
Author(s):  
Anang Dasa Novfowan ◽  
Mochammad Mieftah ◽  
Wijaya Kusuma
Keyword(s):  
Load Balancing ◽  
Energy Losses ◽  
One Stage ◽  
Distribution Transformers ◽  
Long Time ◽  
Unbalanced Load ◽  
Changing Patterns ◽  
Phase Data ◽  
The Impact ◽  
A Current

The unbalanced load on distribution transformers is mostly caused of connecting new customers and changing patterns of electricity usage in the community. The impact of the unbalanced load is the emergence of a current flowing on the transformer neutral conductor that causes energy losses. Load balancing of distribution transformers is commonly done by technicians, but the implementation takes quite a long time and is repeated, sometimes up to 4-5 times to get the appropriate results. This is more due to less valid customer phase data. Customer phase verification is needed to accelerate the load balancing process in an effort to reduce losses in distribution transformers. With valid customer phase data, just one stage of load balancing can reduce losses by 1.06%.

scholarly journals Small-Scale Drop Size Variability: Impact on Estimation of Cloud Optical Properties

2005 ◽  
Vol 62 (7) ◽  
pp. 2555-2567 ◽  
Author(s):  
Y. Knyazikhin ◽  
R. B. Myneni ◽  
A. Marshak ◽  
W. J. Wiscombe ◽  
M. L. Larsen ◽  
...  
Keyword(s):  
Drop Size ◽  
Water Drop ◽  
Small Scale ◽  
Size Dependent ◽  
Radiative Impact ◽  
Size Variability ◽  
The Mean ◽  
Conventional Models ◽  
Processing Techniques ◽  
The Impact

Abstract Most cloud radiation models and conventional data processing techniques assume that the mean number of drops of a given radius is proportional to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, this proportionality breaks down; the number of cloud drops of a given radius is instead proportional to the volume raised to a drop size–dependent nonunit power. The coefficient of proportionality, a generalized drop concentration, is a function of the drop size. For abundant small drops the power is unity as assumed in the conventional approach. However, for rarer large drops, it falls increasingly below unity. This empirical fact leads to drop clustering, with the larger drops exhibiting a greater degree of clustering. The generalized drop concentration shows the mean number of drops per cluster, while the power characterizes the occurrence frequency of clusters. With a fixed total number of drops in a cloud, a decrease in frequency of clusters is accompanied by a corresponding increase in the generalized concentration. This initiates a competing process missed in the conventional models: an increase in the number of drops per cluster enhances the impact of rarer large drops on cloud radiation while a decrease in the frequency suppresses it. Because of the nonlinear relationship between the number of clustered drops and the volume, these two opposite tendencies do not necessarily compensate each other. The data analysis suggests that clustered drops likely have a stronger radiative impact compared to their unclustered counterpart; ignoring it results in underestimation of the contribution from large drops to cloud horizontal optical path.

scholarly journals Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3135
Author(s):  
Wen Chen ◽  
Yongqi Zhu ◽  
Jiawei Liu ◽  
Yuhu Chen
Keyword(s):  
Load Balancing ◽  
Mobile Devices ◽  
Edge Computing ◽  
Load Estimation ◽  
Mobile Edge Computing ◽  
Dense Network ◽  
Ping Pong ◽  
Load Balancing Algorithm ◽  
The Impact ◽  
Ultra Dense Network

With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability.

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