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.

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 A Dynamic Pricing Reverse Auction-Based Resource Allocation Mechanism in Cloud Workflow Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xuejun Li ◽  
Ruimiao Ding ◽  
Xiao Liu ◽  
Xiangjun Liu ◽  
Erzhou Zhu ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Dynamic Pricing ◽  
Completion Time ◽  
Reverse Auction ◽  
Allocation Mechanism ◽  
Workflow Systems ◽  
Monetary Cost ◽  
Low Efficiency ◽  
Resource Allocation Mechanism

Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time⁎Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.

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 An Efficient Resource Allocation Mechanism for LTE–GEPON Converged Networks

2013 ◽  
Vol 22 (3) ◽  
pp. 437-461 ◽  
Author(s):  
Chathurika Ranaweera ◽  
Elaine Wong ◽  
Christina Lim ◽  
Ampalavanapillai Nirmalathas ◽  
Chamil Jayasundara
Keyword(s):  
Resource Allocation ◽  
Allocation Mechanism ◽  
Efficient Resource ◽  
Resource Allocation Mechanism

scholarly journals Modeling a holonic agent based solution by Petri nets

2012 ◽  
Vol 9 (3) ◽  
pp. 1287-1305 ◽  
Author(s):  
Carlos Pascal ◽  
Doru Panescu
Keyword(s):  
Resource Allocation ◽  
Petri Nets ◽  
Petri Net ◽  
Coordination Mechanisms ◽  
Allocation Mechanism ◽  
Simulation Experiments ◽  
Agent Based ◽  
Manufacturing Execution Systems ◽  
Resource Allocation Mechanism ◽  
Manufacturing Execution

One of the key design issues for distributed systems is to find proper planning and coordination mechanisms when knowledge and decision capabilities are spread along the system. This contribution refers holonic manufacturing execution systems and highlights the way a proper modeling method - Petri nets - makes evident certain problems that can appear when agents have to simultaneously treat more goals. According to holonic organization the planning phase is mainly dependent on finding an appropriate resource allocation mechanism. The type of weakness is established by means of the proposed Petri net models and further proved by simulation experiments. A solution to make the holonic scheme avoid a failure in resource allocation is mentioned, too.

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