Distributed Energy-Efficient Scheduling for Data-Intensive Applications with Deadline Constraints on Data Grids

Liu, Cong; Qin, Xiao; Kulkarni, S.; Wang, Chengjun; Li, Shuang; Manzanares, Adam; Baskiyar, Sanjeev

doi:10.1109/pccc.2008.4745123

Cited by 19 publications

(8 citation statements)

References 12 publications

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“…Beloglazov et al solved the similar problem by applying their Modified Best Fit Decreasing algorithm [15]. Liu et al proposed an EnergyEfficient Scheduling (DEES) algorithm that saves energy by integrating the process of scheduling tasks and data placement [16]. Shires et al [17] proposed cloudlet seeding, a strategic placement of high performance computing assets in wireless ad-hoc network such that computational load is balanced.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Chen

Won

Stoleru

et al. 2015

IEEE Trans. Cloud Comput.

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Abstract-Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of our knowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobile cloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibility of our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in larger scale networks.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Chen

Won

Stoleru

et al. 2015

IEEE Trans. Cloud Comput.

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“…Other studies, such as those in [19][20][21], emphasized energy efficiency. Liu et al [19] introduced a distributed scheduler to schedule tasks in an energy-efficient manner, and derived its energy consumption model.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al [19] introduced a distributed scheduler to schedule tasks in an energy-efficient manner, and derived its energy consumption model. Lang and Patel [20] studied the impact of different MapReduce-node power-down strategies on the overall energy consumption and workload response time.…”

Section: Related Workmentioning

confidence: 99%

Analyzing job completion reliability and job energy consumption for a heterogeneous MapReduce cluster under different intermediate-data replication policies

2014

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Recently, MapReduce has been a popular distributed programming framework for solving data-intensive applications. However, a large-scale MapReduce cluster has inevitable machine/node failures and considerable energy consumption. To solve these problems, MapReduce has employed several policies for replicating input data, storing/replicating intermediate data, and re-executing failed tasks. In this study, we concentrate on two typical policies for storing/replicating intermediate data, and derive the job completion reliability (JCR for short) and job energy consumption (JEC for short) of a MapReduce cluster when the two policies are individually employed. The two policies are further analyzed and compared given various scenarios in which jobs with different input data sizes, numbers of reduce tasks, and other parameters are run in a MapReduce cluster with two extreme parallel execution capabilities. From the analytical results, MapReduce managers are able to comprehend how the two policies influence the JCR and JEC of a MapReduce cluster.

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“…Many recent works have utilized resource allocation specifically for data allocation in distributed systems [8] [9]. As cloud storage has become a major backbone for many network services, efficiently allocating data on servers to minimize the communication cost is an important problem.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation for Energy Efficient k-out-of-n System in Mobile Ad Hoc Networks

Chen¹,

Won²,

Stoleru³

et al. 2013

2013 22nd International Conference on Computer Communication and Networks (ICCCN)

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Resource Allocation has been widely used for improving various performance metrics in wireless networks. Applying resource allocation to a Mobile Ad Hoc Network (MANET), however, is a challenging problem because of dynamic network topology. In this paper, we develop a novel resource allocation scheme designed for MANETs that minimizes the communication cost for accessing distributed resources while improving the reliability by adopting the k-out-of-n system, a widely used technique for reliability control. Specifically, we propose a scheme that allocates resources to n nodes, called service centers, such that the expected energy consumption for nodes to access k service centers out of the n service centers (k ≤ n) is minimized. Our scheme accounts for dynamic network topology by estimating the failure probabilities of nodes and monitoring the network for significant topology changes. In addition, an Importance Sampling technique is used to reduce the computation-overhead. To evaluate the performance, we build a mobile distributed file system based on our resource allocation scheme. Through both extensive simulations and real hardware implementation on Smartphones, we show that our resource allocation scheme effectively reduces energy consumption by up to 45% and increases the successful data retrieval rate by up to 50% in comparison with a greedy algorithm.

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Distributed Energy-Efficient Scheduling for Data-Intensive Applications with Deadline Constraints on Data Grids

Abstract: Abstract

Cited by 19 publications

References 12 publications

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud

Analyzing job completion reliability and job energy consumption for a heterogeneous MapReduce cluster under different intermediate-data replication policies

Resource Allocation for Energy Efficient k-out-of-n System in Mobile Ad Hoc Networks

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