Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

2014 ◽  
Vol 4 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Manisha Malhotra ◽  
Rahul Malhotra
Keyword(s):  
Resource Allocation ◽  
Parallel Processing ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Cloud Provider ◽  
Adaptive Resource Allocation ◽  
Cloud Environment ◽  
Allocation Mechanism ◽  
Adaptive Resource ◽  
Resource Allocation Mechanism

As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.

scholarly journals CL-SA-OFDM: Cross-layer and smart antenna based ofdm system performance enhancement

2020 ◽  
Vol 10 (5) ◽  
pp. 4663
Author(s):  
Shivapanchakshari T. G. ◽  
H. S. Aravinda
Keyword(s):  
Resource Allocation ◽  
Performance Enhancement ◽  
Smart Antenna ◽  
Cross Layer ◽  
Adaptive Resource Allocation ◽  
Ofdm System ◽  
Allocation Mechanism ◽  
Adaptive Resource ◽  
Resource Allocation Mechanism

The growing usage of wireless services is lacking in providing high-speed data communication in recent times. Hence, many of the modulation techniques are evolved to attain these communication needs. The recent researches have widely considered OFDM technology as the prominent modulation mechanism to fulfill the futuristic needs of wireless communication. The OFDM can bring effective usage of resources, bandwidth, and system performance enhancement in collaboration with the smart antenna and resource allocation mechanism (adaptive). However, the usage of adaptive beamforming with the OFDM leads to complication in the design of medium access layer and which causes a problem in adaptive resource allocation mechanism (ARAM). Hence, the proposed manuscript intends to design an OFDM system by considering different switched beam smart antenna (SBSA) along with the cross-layer adaptive resource allocation (CLARA) and hybrid adaptive array (HAA). In this, various smart antenna mechanism are considered to analyze the quality of service (QoS) and complexity reduction in the OFDM system. In this paper, various SA schemes are used as per the quality of service (QoS) requirement of the different users. The performance analysis is conducted by considering data traffic reduction, bit-rate reduction, and average delay.

Efficient Resource Allocation Mechanism for Federated Clouds

2015 ◽  
Vol 7 (4) ◽  
pp. 74-87 ◽  
Author(s):  
Chien-Yu Liu ◽  
Kuo-Chan Huang ◽  
Yi-Hsuan Lee ◽  
Kuan-Chou Lai
Keyword(s):  
Resource Allocation ◽  
Virtual Machines ◽  
Vertical Scaling ◽  
System Throughput ◽  
Communication Overhead ◽  
Virtual Cluster ◽  
Allocation Mechanism ◽  
Efficient Resource ◽  
Multiple Clouds ◽  
Resource Allocation Mechanism

This study proposes a novel efficient resource allocation mechanism for federated clouds, which takes the communication overhead into consideration, to improve system throughput and reduce resource repacking overhead in the auto-scaling mechanism. In general, when the amount of service requests increases, more and more resources are allocated to satisfy these requests. However, single cloud cannot provide unlimited services with limited physical resources; therefore, the federation of multiple clouds may be one possible solution. In the federated cloud environment, when the workload changes, the resource allocation mechanism could adopt vertical/horizontal scaling fashions to repack the required resource into virtual machines. In the vertical scaling approach, the resource allocation mechanism allocates more resources into virtual machines for improving virtual machine's capability. In the horizontal scaling approach, the resource allocation mechanism allocates more virtual machines for enhancing the virtual cluster's capability. However, frequent resource repacking may reduce the system performance. Therefore, in order to improve system throughput and reduce repacking overhead, the proposed mechanism captures the execution pattern of applications by the profiling system and the resource status by the monitoring system, and then allocates resources for configuring the virtual cluster. Performance for NAS Parallel Benchmarks is evaluated. Experimental results show that the authors' approach could reduce repacking overhead and improve system throughput by comparing two previous works.

scholarly journals Feedback-Based Resource Allocation in MapReduce-Based Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Bunjamin Memishi ◽  
María S. Pérez ◽  
Gabriel Antoniu
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Feedback Systems ◽  
Allocation Mechanism ◽  
Fine Grain ◽  
Resource Allocation Mechanism ◽  
Exact Amount ◽  
Better Than

Containers are considered an optimized fine-grain alternative to virtual machines in cloud-based systems. Some of the approaches which have adopted the use of containers are the MapReduce frameworks. This paper makes an analysis of the use of containers in MapReduce-based systems, concluding that the resource utilization of these systems in terms of containers is suboptimal. In order to solve this, the paper describes AdaptCont, a proposal for optimizing the containers allocation in MapReduce systems. AdaptCont is based on the foundations of feedback systems. Two different selection approaches, Dynamic AdaptCont and Pool AdaptCont, are defined. Whereas Dynamic AdaptCont calculates the exact amount of resources per each container, Pool AdaptCont chooses a predefined container from a pool of available configurations. AdaptCont is evaluated for a particular case, the application master container of Hadoop YARN. As we can see in the evaluation, AdaptCont behaves much better than the default resource allocation mechanism of Hadoop YARN.

Adaptive Resource Allocation for WiMAX Mesh Network

2019 ◽  
Vol 107 (2) ◽  
pp. 849-867 ◽  
Author(s):  
Mahboubeh Afzali ◽  
Kamalrulnizam AbuBakar ◽  
Jaime Lloret
Keyword(s):  
Resource Allocation ◽  
Mesh Network ◽  
Adaptive Resource Allocation ◽  
Adaptive Resource ◽  
Wimax Mesh Network
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