Versatile, scalable, and accurate simulation of distributed applications and platforms

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the paid cost for the booking nodes is increased. However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively.

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“…The experiments were conducted over SimGrid/SMPI simulator [15]. This simulator introduces good and easy tools to simulate the grid platform.…”

Section: Resultsmentioning

confidence: 99%

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

Fanfakhri

Al-Sultan

Alwan

2017

KJAR

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“…The simulation performed by the simulation engine relies on the SimGrid toolkit [5,10], a well-established discrete event simulator. The inputs to SimGrid are a description of the physical infrastructure (hosts and capabilities of the CPUs, network characteristics and architecture, .…”

Section: The Simulation Frameworkmentioning

confidence: 99%

Schlouder: A broker for IaaS clouds

Michon¹,

Gossa²,

Genaud³

et al. 2017

Future Generation Computer Systems

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In the field of cloud computing, Infrastructure as a Service (IaaS) provides virtualized on-demand computing resources on a pay-per-use model. IaaS Cloud differ from traditional mutualized infrastructures in that the resources can be dynamically claimed and released, and the real hardware infrastructure is unknown to its users. These properties drastically changes the way resource provisioning and job scheduling can be addressed by the user because i) the large number of jobs and resources to handle becomes rapidly overwhelming for human operators, and ii) the real performances of the platform should be inferred from observations to make robust scheduling decisions. In order to optimize the resources usage by the client, we advocate the need for brokers on the client-side. This article presents our work based on Schlouder, a broker of IaaS cloud resources able to provision and schedule independent jobs or static workflows according to strategies chosen by the client. Further, we advocate that simulation can be a precious auxiliary to help the user to choose between provisioning strategies. Schlouder brings a unique feature which is to predict through simulation the makespan and cost of executions under various strategies. The contribution of this work is twofold. First, it presents the broker, available as an open source project, in which new provisioning strategies can be plugged in by third parties. The effectiveness of the tool is demonstrated through experiments involving actual applications and platforms. Second, we show that simulation produces accurate predictions making this feature a helpful means for the user to choose the appropriate strategy.

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“…BIGhybrid is modular and it is built on top of Simgrid [5], which is a simulation-based framework for evaluating cluster, clouds, grid and P2P (peer-to-peer) algorithms and heuristics. Different from others simulators, BIGhybrid has two independent systems, through which it is possible to use different configurations for DFS, schedulers, input/output data size, number of workers, homogeneous and heterogeneous environments, combine two different platforms, and make the simulation parallel.…”

Section: Bighybrid Simulatormentioning

confidence: 99%

“…We analyze the characteristics of hybrid MR runtime environment and design the simulator accordingly. The simulator is based on SimGrid [5] and leverages solutions proposed in the scope of the MapReduce ARN project [6].…”

Section: Introductionmentioning

confidence: 99%

BIGhybrid -- A Toolkit for Simulating MapReduce in Hybrid Infrastructures

Anjos

Fedak

Geyer

2014

2014 International Symposium on Computer Architecture and High Performance Computing Workshop

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Abstract-Cloud computing has increasingly been used as a platform for running large business and data processing applications. Although clouds have become highly popular, when it comes to data processing, the cost of usage is not negligible. Conversely, Desktop Grids, have been used by a plethora of projects, taking advantage of the high number of resources provided for free by volunteers. Merging cloud computing and desktop grids into hybrid infrastructure can provide a feasible low-cost solution for big data analysis. Although frameworks like MapReduce have been conceived to exploit commodity hardware, their use on hybrid infrastructure poses some challenges due to large resource heterogeneity and high churn rate. This study introduces BIGhybrid a toolkit to simulate MapReduce on hybrid environments. The main goal is to provide a framework for developers and system designers to address the issues of hybrid MapReduce. In this paper, we describe the framework which simulates the assembly of two existing middleware: BitDewMapReduce for Desktop Grids and Hadoop-BlobSeer for Cloud Computing. Experimental results included in this work demonstrate the feasibility of our approach.

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Versatile, scalable, and accurate simulation of distributed applications and platforms

Cited by 335 publications

References 72 publications

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

Schlouder: A broker for IaaS clouds

BIGhybrid -- A Toolkit for Simulating MapReduce in Hybrid Infrastructures

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