Incentive-Based P2P Scheduling in Grid Computing
|
|
- Basil Gaines
- 6 years ago
- Views:
Transcription
1 Incentive-Based P2P Scheduling in Grid Computing Yanmin Zhu 1, Lijuan Xiao 2, Lionel M. Ni 1, and Zhiwei Xu 2 1 Department of Computer Science Hong Kong University of Science and Technology Clearwater Bay, Kowloon, Hong Kong {zhuym,ni}@cs.ust.hk 2 Institute of Computing Technology Chinese Academy of Sciences, Beijing, China xiaolijuan@software.ict.ac.cn, zxu@ict.ac.cn Abstract. Grid computing has emerged as an attractive computing paradigm recently. In typical grid environments, there are two distinct parties, resource consumers and resource providers, which have different optimization objectives. Enabling an effective interaction between the two parties (i.e., scheduling jobs of consumers across resources of providers) is particularly challenging due to the distributed ownership of grid resources. In this paper, we propose an incentive-based P2P scheduling for grid computing, with the goal of building a practical and robust computational economy. The goal is realized by building a computational market supporting fair and healthy competition among consumers and providers. To build the healthy computational market, we propose the P2P scheduling infrastructure to efficiently support the scheduling, and the incentive-based algorithms for consumers and providers, respectively. 1 Introduction With the rapid development of high-speed wide-area networks and powerful yet lowcost computational resources, grid computing [1] has emerged as an attractive computing paradigm. Computational grids strive to aggregate the computational power of heterogeneous, geographically distributed and dynamic computational resources. These resources are usually administrated by different domains and owned by various instances. Therefore, they are highly autonomous and differ from each other in many aspects, such as scheduling policy, security requirement, performance strategy, and desired objective. Effective scheduling is of fundamental importance. However, due to unique characteristics described above in grid computing, scheduling in grid environments is particularly challenging. In typical grid environments, on one hand, some users (resource consumers) have computational jobs to execute, but they may lack computational resources for their jobs. On the other hand, some resource owners (resource providers) have relatively underutilized resources. It is highly desirable for consumers to schedule jobs across those resources, but the scheduling is significantly complicated by the distributed ownership of grid resources. Consumers and providers are independent from each other, each having its own access policy, scheduling strategy, and optimization objec- H. Jin, Y. Pan, N. Xiao, and J. Sun (Eds.): GCC 2004, LNCS 3251, pp , Springer-Verlag Berlin Heidelberg 2004
2 210 Yanmin Zhu et al. tive. In human society, economic methods have been widely employed to solve this kind of problems. Recently a few researchers [2, 3] have started to tackle the problem by applying economic methods to grid computing. But the related research is still in its infant and extensive research efforts are still required. Grid environments are dramatically different from the human society. Scheduling based on economic models for grid computing is a highly challenging problem. In this paper, we propose an incentive-based P2P scheduling for grid computing, with the goal of building a practical and robust computational economy. The goal is realized by building a computational market which supports fair and healthy competition among consumers and providers. The market is fully decentralized, in which every participant competes actively and behaves independently for its own benefit. A market is said to be healthy if every player in the market has sufficient incentive for joining the market. To build a healthy computational market, we first propose the P2P scheduling infrastructure, taking the advantages of P2P networks to efficiently support the scheduling. Second, incentivebased algorithms are designed for consumers and providers, respectively, to ensure every participant with sufficient incentive. Detailed simulation results show that our approach is a promising solution to building a healthy and stable computational economy. The rest of the paper is organized as follows. Section 2 presents an overview of closely related work. In Section 3, we formally define the problem and state the performance objectives. In Section 4, we describe the incentive-based P2P scheduling for grid computing in detail. Section 5 presents the simulation results. Section 6 concludes the paper. 2 Related Work In this section, we will give an overview of the closely related work. Emphasis will be put on those papers involving scheduling with economic methods. Buyya et al. [3] presented some economic models, which have been used in the human society for a long time, such as auction model, commodity market model, contract-net model, bargaining model and bartering model. They discussed the possible directions how the economic models from the human society can be applied into grid computing. The discussion, however, is at high level and no implementation is presented. Applying these models to grid computing properly is a major challenge. Computer Power Market (CPM) [4] is a market-based resource management and job scheduling system for grid computing. A CPM is comprised of markets, resource consumers and resource providers. A market is the mediator between consumers and providers, which mediates all the information from both consumers and providers. CPM takes the advantages of real markets in the human society. However, the centralized market server does introduce many limitations, such as single point of failure, limited scalability, and so on. Furthermore, the centralized market server requires additional organizations for regular maintenance. Enterprise [5] is a market-like task scheduler for distributed computing environments. Each client specifies a request for bids which includes the numerical priority
3 Incentive-Based P2P Scheduling in Grid Computing 211 of the task, which is estimated by the task execution time (i.e., the shortest task has the highest priority). Each idle computer responds with a bid giving the estimated completions time. Enterprise shares with our approach the general scheduling process, whereas enterprise is limited by its design considerations. It targets local area network, and only idle workstations will bid for the jobs. More importantly, Enterprise s objective is to minimize the mean flow time, while our objective is two-fold. The papers described above have attempted to take the advantages of the economic idea for scheduling in grid computing. However, focus has only been put on one party s performance, either consumer or provider. To the best of our knowledge, no successful research has been conducted to build a computational market by guaranteeing the incentive for all participants. 3 Problem Formulation The computational market consists of two interacting parties: resource consumers and resource providers. Resource consumers are demanding of computational resources to perform their computing tasks, referred as jobs, and ready to pay for the completion of the jobs. Resource providers have computational resources and try to sell the computing cycles for profit. The problem is how to schedule consumer jobs to those resources, while guaranteeing every participant in the market gets sufficient incentive to play in the market. The incentives stated here are two folds. For consumers, the incentive means that given two jobs with the identical job length, a consumer who pays more for its job should experience better performance than the one who pay less for its job. Examples for better performance include less deadline missing rate, shorter response time, etc. For providers, the incentive means that the earned profit should conform to the cost of its resources. However, the cost may not correctly reflect the relative weight, since the prices of resources are varying over time. Therefore, in our work we replace the cost with the computational capability to represent the relative capability. It is reasonable because a higher computational capability usually results from a higher cost. A resource consumer can be anyone who has jobs to do and is ready to pay for the completion of the jobs. A job refers to a computational task which is usually computation-intensive. Before a job is able to be executed on the computational grid, some attributes have to be set properly. A job can be characterized by job length, deadline, budget, runtime requirements and data size. The job length refers to the execution time of the job on the standard platform. The budget is the amount of money that the consumer promises to pay for the completion of the job. Each user may have different budget assigning scheme. A resource provider is comprised of the computational resources from one domain. These resources are typically interconnected by a high-speed local network and protected by firewalls from the outside world. A centralized scheduler is deployed to efficiently manage these resources. Resource providers compete actively for jobs from resource consumers and execute them for gaining profits. Every provider tries to maximize its profit based on its computational capability. To estimate the computational capability of a provider, we use the widely-used method [6].
4 212 Yanmin Zhu et al. The goal is to build a computational economy to enable efficient interaction between consumers and providers. It is to be realized by building a healthy computational market. The health of the market means every player in the market can have sufficient incentive for joining in the market such that the market is stable and lasts. 4 Incentive-Based P2P Scheduling The large scale of the virtual market implies that simplicity, self-organizing, robustness and scalability are important features that the system should possess. To this end, we propose the P2P scheduling infrastructure for organizing the resource consumers and resource providers into a P2P alike network. Taking the advantages of P2P networks, the scheduling infrastructure greatly facilitate the scheduling operation over the distributed grid system. The basic idea is that we try to form a complete competition among all participants. On one hand, given a job request, enough providers will actively compete for the job request. On the other hand, given a provider, enough consumers will compete for the provider s resource. We expect that the incentives of both consumers and providers can be automatically achieved through the complete competition mechanism. In Figure 1, the main steps for executing a job in the computational market are listed. Every job will experience the same steps until its completion. 4.1 P2P Scheduling Infrastructure P2P networks, such as Gnutella [7], and Kazza [8], have been widely used in file sharing for their simplicity, scalability and robustness. In general, there is no centralized controller in P2P networks and each peer is autonomous. To take the advantages of P2P networks, we organize resource providers into a P2P network. The P2P network forms the scheduling infrastructure on which job announcements are forwarded. A consumer initiates a job announcement and sends it into the P2P network. On receiving the job announcement, every provider is required to forward the job announcement to its neighboring providers except the one which forwards this job announcement to it. 4.2 Incentive-Based Consumer Algorithm The behavior of a consumer is characterized by two operations: budget assigning and job offering. Given a job, the budget assigning algorithm is responsible for assigning a proper budget to the job. And the job offering scheme determines which provider the job is offered to from the candidates that replied. Many factors are involved in deciding the budget of a job. These factors can be generally classified into two categories: internal ones and external ones. Internal factors include the job length and the urgent level of the deadline, and external factors include the overall system load and the budget assigning schemes of other consumers in the system.
5 Incentive-Based P2P Scheduling in Grid Computing 213 Fig. 1. The General Scheduling Procedure. The urgent level of a job is defined as follows. According to the definition, a higher urglev value means more urgently the job is required to be completed. joblength urglev = deadline creationtime It is intuitive that a longer job length requires more computing cycles and therefore a job with longer job length needs more budget. And a higher urgent level implies that the job should precede many other jobs to be executed, and therefore it requires higher priority. To obtain a higher priority, the job has to be given more budget. Thus, the budget is proportional to the job length and job urgent level. The following is the proposed budget assigning scheme for consumers. jobbudget = λ ( a joblen + b urglev ) - jobbudget : job budget - λ : job importance factor - a, b : constant coefficient In addition to the internal factors, the budget assigning algorithm should be aware of the external factors. One basic observation is that when the deadline missing rate increases, a consumer should conclude that jobs were assigned relatively less budget such that the jobs were inferior while competing with other jobs. Therefore, the budget assigning algorithm has to be adaptive to the deadline missing rate. When the deadline missing rate increases, the algorithm should increase importance factor accordingly; otherwise, it should decrease λ. After sending out a job announcement, the consumer waits for a short while and expects to receive a number of replies from those providers meeting the job s deadline. A reply includes the completion time when the provider claims to complete the job. The job offering scheme is responsible for determining to which provider the job is going to be offered. Different consumers may have different optimization objectives, and therefore have different job offering schemes. We have implemented the job offering scheme that a consumer will offer the job to the provider who claims the earliest completion time for the job.
6 214 Yanmin Zhu et al. 4.3 Incentive-Based Provider Algorithm The behavior of a resource provider is characterized by two operations: competing for jobs and local scheduling. The former operation is responsible for deciding how to compete for jobs, and the later operation is to schedule the local offered jobs, with the aim of maximizing the profit. Once a resource provider is offered with a job, it will perform scheduling for its own benefit only, without taking into account the job s performance. In order to prevent the provider from not keeping the promise it made to the consumer, we propose a penalty model. The basic idea is that a provider will be penalized with an amount of money if the provider could not complete the job before the completion time it promised to the consumer. The amount of penalty is related to the budget of the job and the length of exceeding time. To some extent, the penalty model helps force the provider to keep its promise, but still allows the freedom of local scheduling such that it is able to maximize its profit. The following expression summarizes the penalty model mathematically. p 1 B and p 2 B are the slopes of the penalty lines, where p 1 and p 2 are constant coefficients. In general, p 1 is less than p 2 because for consumers exceeding deadline is more serious than exceeding the claimed completion time. MaxPen represents the maximal penalty that could be posed. 0 T CT p1b(t CT ) CT < T DL penalty = p2b(t DT ) + p1b( DL CT ) DL < T T0 MaxPen T > T0 In the computational market, each provider competes actively for jobs, and tries to maximize its profit. One basic operation of providers is how to respond to job announcements. Ideally, the provider could try to compete for those jobs that will result in the maximal profit. But it is hardly possible because the provider is not able to predict the possible future job announcement arrivals, and also cannot determine whether it will be offered with a specific job. We propose an aggressive competing algorithm for providers. By this algorithm, each provider tries to complete for every job whenever it can satisfy the job s deadline. Whenever a job is completed, the provider has to make a decision which job to execute next. Suppose that there are n offered pending jobs. It is a well-known NP complete problem to schedule the jobs such that the resulting profit is maximized [9]. One simple optimal solution is to investigate each of the n! permutations, compute the profit, and select the permutation which produces the maximal profit. But it is certainly computationally infeasible when n is large. We propose a heuristic local scheduling algorithm, which is computationally efficient and produces near-optimal profit. The basic idea is that for each provider a sorted list of offered pending jobs is maintained with respect to the profit. The ordering of these jobs results in a near maximal profit. The heuristic is that after a new offered job is inserted, the relative order of the original jobs is probably not changed. According to the heuristic, only n+ 1 possible positions for the new job should be investigated. The new offered job will be inserted to the position which produces the maximal profit out of the n+1 posi-
7 Incentive-Based P2P Scheduling in Grid Computing 215 tions. With the availability of the sorted list of jobs, it is simple to decide which job to execute next. The provider will select the job on the head of the list to execute whenever a previous job is completed. 5 Simulation Results We design the first experiment to study the incentives obtained by consumers with different job importance factor. In this experiment, simulations are performed using the following parameters. There are in total 20 consumers and 80 providers. 10% of the consumers are assigned with job importance factor λ=1.5, 10% of consumers with λ=0.5 and the rest with λ=1.0. The system load is varying from 0.3 to 0.7. Figure 2 shows the resulting incentives in terms of the deadline missing rate with respect to the system load. As seen in this figure, the consumers with higher importance factor really experience less deadline missing rate. Those jobs with relatively higher importance factor will gain relatively higher priority and consequently be executed earlier. This trend becomes sharper when the system load is increasing. Thus, it makes sense that consumers with importance factor λ=1.5 experience minimum deadline missing rate. This experiment demonstrates that our approach achieves to guarantee the incentive of consumers. The second experiment is designed to study the incentive of providers. Before talking about the experiment results, we define the terminology fairness scale of an individual provider. The fairness scale is defined by the following expression. normalized profiti fairness scalei = normalized capability A fairness scale reflects the individual incentive for a provider. The ideal case is that the fairness scale of every provider is one. A fairness scale less than one means that the provider does not make the profit conforming to its computational capability, which leads to the situation that the provider does not get enough incentive. If the fairness scale of a provider remains much less than one, it possibly quits the market. The standard deviation (SD) of fairness scales of providers is employed to study the overall incentive situation of providers. As shown in Figure 3, the SD of fairness scales is fairly good, which is less than 25% of the ideal fairness scale. When the system load is increasing, the SD will be further reduced. This experiment demonstrates that our approach really guarantees the incentive for every provider. i 6 Conclusion Distributed ownership of resources greatly complicates scheduling in grid computing. Enabling the interaction between consumers and providers is highly challenging. In this paper, we have proposed the incentive-based P2P scheduling, aiming at building a decentralized, scalable and robust computational market. In this market, each participant behaves for its own benefit only. However, the computational market is proved to be healthy since each participant is guaranteed to obtain sufficient incentive
8 216 Yanmin Zhu et al. Deadline Missing Rate (%) λ=0.5 λ=1.0 λ= System Load Standard Deviation of Fairness Scale System Load Fig. 2. Resource Consumer Incentive. Fig. 3. Resource Provider Incentive. for joining the market. Detailed simulation results demonstrate that our approach is successful in building a healthy and scalable computational economy. References 1. Foster, I. and C. Kesselman, The Grid 2: Blueprint for a New Computing Infrastructure. 2003: Morgan Kaufmann Publishers. 2. Shetty, S., P. Padala, and M. Frank, A Survey of Market Based Approaches in Distributed Computing Buyya, R., et al., Economic Models for Resource Management and Scheduling in Grid Computing. Special Issue on Grid Computing Environments, Journal of Concurrency and Computation: Practice and Experience, (13-15): p Buyya, R. and S. Vazhkudai. Compute power market: Towards a market-oriented grid. in the First International Symposium on Cluster Computing and the Grid Malone, T.W., et al., Enterprise: A market-like task scheduler for distributed computing environments, in The Ecology of Computation, B.A. Huberman, Editor. 1988, Amsterdam: north-holland. p The Standard Performance Evaluation Corporation (SPEC) Home Page, 7. Gnutella Homepage, April, Kazaa Homepage, April, Gonzalez, M.J., Deterministic Processor Scheduling. ACM Computing Surveys, (3): p
Integrating New Cost Model into HMA-Based Grid Resource Scheduling
Integrating New Cost Model into HMA-Based Grid Resource Scheduling Jun-yan Zhang, Fan Min, and Guo-wei Yang College of Computer Science and Engineering, University of Electronic Science and Technology
More informationAn Agent-Based Scheduling Framework for Flexible Manufacturing Systems
An Agent-Based Scheduling Framework for Flexible Manufacturing Systems Iman Badr International Science Index, Industrial and Manufacturing Engineering waset.org/publication/2311 Abstract The concept of
More informationDetermination of a Fair Price for Blood Transportation by Applying the Vehicle Routing Problem: A Case for National Blood Center, Thailand
Determination of a Fair Price for Blood Transportation by Applying the Vehicle Routing Problem: A Case for National Blood Center, Thailand S. Pathomsiri, and P. Sukaboon Abstract The National Blood Center,
More informationModeling of competition in revenue management Petr Fiala 1
Modeling of competition in revenue management Petr Fiala 1 Abstract. Revenue management (RM) is the art and science of predicting consumer behavior and optimizing price and product availability to maximize
More informationQoS-based Scheduling for Task Management in Grid Computing
QoS-based Scheduling for Task Management in Grid Computing Xiaohong Huang 1, Maode Ma 2, Yan Ma 1 Abstract--Due to the heterogeneity, complexity, and autonomy of wide spread Grid resources, the dynamic
More informationCOLLABORATIVE WORKSPACE OVER SERVICE-ORIENTED GRID
COLLABORATIVE WORKSPACE OVER SERVICE-ORIENTED GRID SHEN ZHIQI Information Communication Institute of Singapore, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang
More informationTRANSPORTATION PROBLEM AND VARIANTS
TRANSPORTATION PROBLEM AND VARIANTS Introduction to Lecture T: Welcome to the next exercise. I hope you enjoyed the previous exercise. S: Sure I did. It is good to learn new concepts. I am beginning to
More informationOperations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras
Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 24 Sequencing and Scheduling - Assumptions, Objectives and Shop
More informationIBM Grid Offering for Analytics Acceleration: Customer Insight in Banking
Grid Computing IBM Grid Offering for Analytics Acceleration: Customer Insight in Banking customers. Often, banks may purchase lists and acquire external data to improve their models. This data, although
More informationWEB SERVICES COMPOSING BY MULTIAGENT NEGOTIATION
Jrl Syst Sci & Complexity (2008) 21: 597 608 WEB SERVICES COMPOSING BY MULTIAGENT NEGOTIATION Jian TANG Liwei ZHENG Zhi JIN Received: 25 January 2008 / Revised: 10 September 2008 c 2008 Springer Science
More informationPh.D. Defense: Resource Allocation Optimization in the Smart Grid and High-performance Computing Tim Hansen
Ph.D. Defense: Resource Allocation Optimization in the Smart Grid and High-performance Computing Tim Hansen Department of Electrical and Computer Engineering Colorado State University Fort Collins, Colorado,
More informationGang Scheduling Performance on a Cluster of Non-Dedicated Workstations
Gang Scheduling Performance on a Cluster of Non-Dedicated Workstations Helen D. Karatza Department of Informatics Aristotle University of Thessaloniki 54006 Thessaloniki, Greece karatza@csd.auth.gr Abstract
More informationInternational Journal of Scientific & Engineering Research, Volume 7, Issue 12, December ISSN
International Journal of Scientific & Engineering Research, Volume 7, Issue 12, December-2016 1 Task Scheduling in Cloud Computing Aakanksha Sharma Research Scholar, Department of Computer Science & Applications,
More informationSOA Oriented Web Services Operational Mechanism
SOA Oriented Operational Mechanism Meiyun Zuo and Bei Wu School of Information, Renmin University of China, Beijing 100872, P.R. China zuomeiyun@263.net wubeiwb@gmail.com Abstract. SOA is a very important
More informationAGORA: An Architecture for Strategyproof Computing in Grids
AGORA: An Architecture for Strategyproof Computing in Grids Daniel Grosu Department of Computer Science Wayne State University Detroit, Michigan 48202, USA Email: dgrosu@cs.wayne.edu Abstract Grids enable
More informationPriority-Driven Scheduling of Periodic Tasks. Why Focus on Uniprocessor Scheduling?
Priority-Driven Scheduling of Periodic asks Priority-driven vs. clock-driven scheduling: clock-driven: cyclic schedule executive processor tasks a priori! priority-driven: priority queue processor tasks
More informationOutline. Introduction. Introduction. Overview of Microgrid Management and Control
Outline Overview of Microgrid Management and Control Michael Angelo Pedrasa Microgrids Research Management of Microgrids Agent-based Control of Power Systems Energy Systems Research Group School of Electrical
More informationGlobal position system technology to monitoring auto transport in Latvia
Peer-reviewed & Open access journal www.academicpublishingplatforms.com The primary version of the journal is the on-line version ATI - Applied Technologies & Innovations Volume 8 Issue 3 November 2012
More informationHeuristic Techniques for Solving the Vehicle Routing Problem with Time Windows Manar Hosny
Heuristic Techniques for Solving the Vehicle Routing Problem with Time Windows Manar Hosny College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia mifawzi@ksu.edu.sa Keywords:
More informationDYNAMIC RESOURCE PRICING ON FEDERATED CLOUDS
DYNAMIC RESOURCE PRICING ON FEDERATED CLOUDS CCGRID 2010 The 10th IEEE/ACM Intl. Symp. on Cluster, Cloud and Grid Computing Marian Mihailescu Yong Meng Teo Department of Computer Science National University
More informationCOMPARING VARIOUS WORKFLOW ALGORITHMS WITH SIMULATED ANNEALING TECHNIQUE
COMPARING VARIOUS WORKFLOW ALGORITHMS WITH SIMULATED ANNEALING TECHNIQUE Dr.V.Venkatesakumar #1, R.Yasotha #2 # Department of Computer Science and Engineering, Anna University Regional Centre, Coimbatore,
More informationSOFTWARE AGENT AND CLOUD COMPUTING: A BRIEF REVIEW Gambang, Pahang, Malaysia 2 College of Information Technology, Universiti Tenaga Nasional,
International Journal of Software Engineering & ComputerSystems (IJSECS) ISSN: 2289-8522, Volume 2, pp. 108-113, February 2016 Universiti Malaysia Pahang DOI: http://dx.doi.org/10.15282/ijsecs.2.2016.9.0020
More informationTowards Modelling-Based Self-adaptive Resource Allocation in Multi-tiers Cloud Systems
Towards Modelling-Based Self-adaptive Resource Allocation in Multi-tiers Cloud Systems Mehdi Sliem 1(B), Nabila Salmi 1,2, and Malika Ioualalen 1 1 MOVEP Laboratory, USTHB, Algiers, Algeria {msliem,nsalmi,mioualalen}@usthb.dz
More informationCHAPTER 1 DEREGULATION OF ELECTRICITY MARKETS AROUND THE WORLD
CHAPTER 1 DEREGULATION OF ELECTRICITY MARKETS AROUND THE WORLD 1 INTRODUCTION In 1990, the electricity industry in England and Wales was the first to introduce competition to the activities of generation
More informationPricing-based Energy Storage Sharing and Virtual Capacity Allocation
Pricing-based Energy Storage Sharing and Virtual Capacity Allocation Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin Department of Information Engineering, The Chinese University of Hong Kong Department
More informationChapter 2: Economic Systems
2-1 Summary: Fill in the missing words. Because economic resources are limited, a country must answer three key economic questions. These are: 1)? 2)? 3)? In answering these questions, societies must consider
More informationThe Job Assignment Problem: A Study in Parallel and Distributed Machine Learning
The Job Assignment Problem: A Study in Parallel and Distributed Machine Learning Gerhard Weiß Institut für Informatik, Technische Universität München D-80290 München, Germany weissg@informatik.tu-muenchen.de
More informationTerms and Conditions
- 1 - Terms and Conditions LEGAL NOTICE The Publisher has strived to be as accurate and complete as possible in the creation of this report, notwithstanding the fact that he does not warrant or represent
More informationAdobe Certified Expert Exam Guide Exam number: 9A0-394
Adobe Certified Expert Exam Guide Exam number: 9A0-394 Revised 2 April 2015 ABOUT ADOBE CERTIFIED EXPERT EXAMS To be an Adobe Certified Expert is to demonstrate expertise in helping clients realize value
More informationMethodologies to Implement ERP Systems: Are they PMBOK Compliant? Andres E. Diaz, P.Eng., MBA, PMP Hunter Business Group Inc.
Methodologies to Implement ERP Systems: Are they PMBOK Compliant? Andres E. Diaz, P.Eng., MBA, PMP Hunter Business Group Inc. www.hunter-inc.com Abstract Enterprise Resources Planning (ERP) systems are
More informationMARI First Consultation. Call For Input
MARI First Consultation Call For Input Abstract This document provides details on the options of the European mfrr platfrom design, which are consulted from 21 November 2017 20 December 2017-0 - Table
More informationLecture Note #4: Task Scheduling (1) EECS 571 Principles of Real-Time Embedded Systems. Kang G. Shin EECS Department University of Michigan
Lecture Note #4: Task Scheduling (1) EECS 571 Principles of Real-Time Embedded Systems Kang G. Shin EECS Department University of Michigan 1 Reading Assignment Liu and Layland s paper Chapter 3 of the
More informationUsing k-pricing for Penalty Calculation in Grid Market
Using k-pricing for Penalty Calculation in Grid Market Michael Becker, Institute of Information Systems and Management, University of Karlsruhe 1 Nikolay Borrisov, Institute of Information Systems and
More informationWfMC BPM Excellence 2013 Finalist Copyright Bizagi. All rights reserved.
WfMC BPM Excellence 2013 Finalist Copyright 2002-2015 Bizagi. All rights reserved. WfMCBPM Excelence2013Finalist 2 1. Overview Initially, BBVA decided to set up a real-estate unit within the bank to manage
More informationSolving the Top 5 Enterprise IT Infrastructure Software Management Challenges
White Paper Solving the Top 5 Enterprise IT Infrastructure Software Management Challenges Sponsored by: Cisco Nolan Greene Elaina Stergiades November 2017 Rohit Mehra IDC OPINION As enterprises move quickly
More informationResource Allocation Strategies in a 2-level Hierarchical Grid System
st Annual Simulation Symposium Resource Allocation Strategies in a -level Hierarchical Grid System Stylianos Zikos and Helen D. Karatza Department of Informatics Aristotle University of Thessaloniki 5
More informationCost-based Job Grouping and Scheduling Algorithm for Grid Computing Environments
Cost-based Job Grouping and Scheduling Algorithm for Grid Computing Environments Sonal Yadav M.Tech (CSE) Sharda University Greater Noida, India Amit Agarwal, Ph.D Associate Professor University of Petroleum
More informationJob Scheduling in Cluster Computing: A Student Project
Session 3620 Job Scheduling in Cluster Computing: A Student Project Hassan Rajaei, Mohammad B. Dadfar Department of Computer Science Bowling Green State University Bowling Green, Ohio 43403 Phone: (419)372-2337
More informationModernize your grid: Simplify smart metering with an intelligent partner.
Modernize your grid: Simplify smart metering with an intelligent partner. White paper Turn to a trusted partner to decrease the complexities of building and maintaining a smart grid infrastructure and
More informationA Meta-model Approach to Scenario Generation in Bank Stress Testing
A Meta-model Approach to Scenario Generation in Bank Stress Testing Zhimin Hua, J. Leon Zhao Department of Information Systems, City University of Hong Kong zmhua2@student.cityu.edu.hk, jlzhao@cityu.edu.hk
More informationTactical Planning using Heuristics
Tactical Planning using Heuristics Roman van der Krogt a Leon Aronson a Nico Roos b Cees Witteveen a Jonne Zutt a a Delft University of Technology, Faculty of Information Technology and Systems, P.O. Box
More informationMake-to-Stock under Drum-Buffer-Rope and Buffer Management Methodology
I-09 Elyakim M. Schragenheim Make-to-Stock under Drum-Buffer-Rope and Buffer Management Methodology WHY MAKE-TO-STOCK? At least from the theory of constraints (TOC) perspective this is a valid question.
More informationOn the Combined Behavior of Autonomous Resource Management Agents
On the Combined Behavior of Autonomous Resource Management Agents Siri Fagernes 1 and Alva L. Couch 2 1 Faculty of Engineering Oslo University College Oslo, Norway siri.fagernes@iu.hio.no 2 Computer Science
More informationPricing/Formula Grids: Which Fit and Which Don't Fit
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Range Beef Cow Symposium Animal Science Department December 1997 Pricing/Formula Grids: Which Fit and Which Don't Fit Dillon
More informationRisk-Based Approach to SAS Program Validation
Paper FC04 Risk-Based Approach to SAS Program Validation Keith C. Benze SEC Associates, Inc. 3900 Paramount Parkway, Suite 150 South Morrisville, NC 27560 ABSTRACT SAS is widely used throughout the FDA
More informationProduct Brief SysTrack VMP
Product Brief SysTrack VMP Benefits Optimize desktop and server virtualization and terminal server projects Anticipate and handle problems in the planning stage instead of postimplementation Use iteratively
More informationOptimized Virtual Resource Deployment using CloudSim
Optimized Virtual Resource Deployment using CloudSim Anesul Mandal Software Professional, Aptsource Software Pvt. Ltd., A-5 Rishi Tech Park, New Town, Rajarhat, Kolkata, India. Kamalesh Karmakar Assistant
More informationDiscovering the Scope of Mobile Agent Technology in Cloud Computing Environment: A Study
Discovering the Scope of Mobile Agent Technology in Cloud Computing Environment: A Study Mrs.Snehal A.Narale Abstract- The Cloud Computing has come into spectacle as a new computing archetype. It proposed
More informationGrid Economics. Integrating Economic Principles in Grid Resource Management
Grid Economics Integrating Economic Principles in Grid Resource Management Kurt Vanmechelen, Jan Broeckhove, Gunther Stuer Research Group Computational Programming Agenda Introduction Commodity Market
More informationGoodbye to Fixed Bandwidth Reservation: Job Scheduling with Elastic Bandwidth Reservation in Clouds
Goodbye to Fixed Bandwidth Reservation: Job Scheduling with Elastic Bandwidth Reservation in Clouds Haiying Shen *, Lei Yu, Liuhua Chen &, and Zhuozhao Li * * Department of Computer Science, University
More informationThe Green Index: A Metric for Evaluating System-Wide Energy Efficiency in HPC Systems
The Green Index: A Metric for Evaluating System-Wide Energy Efficiency in HPC Systems Balaji Subramaniam and Wu-chun Feng Department of Computer Science Virginia Tech {balaji, feng}@cs.vt.edu Abstract
More informationGame Theoretic Methods for the Smart Grid
Game Theoretic Methods for the Smart Grid Walid Saad 1, Zhu Han 2, H. Vincent Poor 3, and Tamer Başar 4 1 Electrical and Computer Engineering Department, University of Miami, Coral Gables, FL, USA, email:
More informationOPERATING SYSTEMS. Systems and Models. CS 3502 Spring Chapter 03
OPERATING SYSTEMS CS 3502 Spring 2018 Systems and Models Chapter 03 Systems and Models A system is the part of the real world under study. It is composed of a set of entities interacting among themselves
More informationScheduling and Resource Management in Grids
Scheduling and Resource Management in Grids ASCI Course A14: Advanced Grid Programming Models Ozan Sonmez and Dick Epema may 13, 2009 1 Outline Resource Management Introduction A framework for resource
More informationReoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC
Konrad-Zuse-Zentrum fu r Informationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Germany BENJAMIN HILLER SVEN O. KRUMKE JO RG RAMBAU Reoptimization Gaps versus Model Errors in Online-Dispatching
More informationPredictive Planning for Supply Chain Management
Predictive Planning for Supply Chain Management David Pardoe and Peter Stone Department of Computer Sciences The University of Texas at Austin {dpardoe, pstone}@cs.utexas.edu Abstract Supply chains are
More information8 REASONS Why Your Inbound Trailers are Underutilized
SEPTEMBER 2016 8 REASONS Why Your Inbound Trailers are Underutilized 1 2 3 4 5 6 7 8 SUPPLIERS PROVIDE INACCURATE DATA MILK RUNS AREN T OPTIMIZED SUPPLIERS DON T STACK PROPERLY SUPPLIERS DON T USE THE
More informationIntroduction to Real-Time Systems. Note: Slides are adopted from Lui Sha and Marco Caccamo
Introduction to Real-Time Systems Note: Slides are adopted from Lui Sha and Marco Caccamo 1 Overview Today: this lecture introduces real-time scheduling theory To learn more on real-time scheduling terminology:
More informationSMPS EMBRACING TECHNOLOGY
SMPs Technology SMPS EMBRACING TECHNOLOGY Moving to cloud computing is inevitable for small- and medium-sized practices looking to remain as relevant as larger firms with more resources. Michelle Perry
More informationDeterministic Crowding, Recombination And Self-Similarity
Deterministic Crowding, Recombination And Self-Similarity Bo Yuan School of Information Technology and Electrical Engineering The University of Queensland Brisbane, Queensland 4072 Australia E-mail: s4002283@student.uq.edu.au
More informationBy Eli Schragenheim Supporting TOC implementations worldwide
By Eli Schragenheim Supporting TOC implementations worldwide The need How decisions regarding new opportunities in the market are made today? Fully based on the intuition of Sales and top management? Based
More informationCourse Contents: TM Activities Identification: Introduction, Definition, Identification processes, Case study.
Chapter 2 Technology Identification Course Contents: TM Activities Identification: Introduction, Definition, Identification processes, Case study. Contents Chapter 2 Technology Identification... 1 Introduction...
More informationAutomatic Formation and Analysis of Multi-Agent Virtual Organization
Automatic Formation and Analysis of Multi-Agent Virtual Organization Qinhe Zheng, Xiaoqin Zhang Department of Computer and Information Science University of Massachusetts at Dartmouth 285 Old Westport
More informationarxiv:cs/ v1 [cs.os] 4 Feb 2005
Markets are Dead, Long Live Markets Kevin Lai arxiv:cs/0502027v1 [cs.os] 4 Feb 2005 Abstract Researchers have long proposed using economic approaches to resource allocation in computer systems. However,
More informationA Deadline and Budget Constrained Cost-Time Optimisation Algorithm for Scheduling Task Farming Applications on Global Grids
A and Constrained Cost-Time Optimisation Algorithm for Scheduling Task Farming Applications on Global Grids Rajkumar Buyya, Manzur Murshed *, and David Abramson School of Computer Science and Software
More informationThe Benefits of a. PRO & CON ANALYSIS Service Level Agreement with an Upfitter BY MIKE ANTICH
PART 1: The Benefits of a PRO & CON ANALYSIS Service Level Agreement with an Upfitter BY MIKE ANTICH This two-part article examines the pros and cons of a service level agreement (SLA) with an upfitter.
More informationInternational Journal of Computer Trends and Technology (IJCTT) volume 10number5 Apr 2014
Survey Paper for Maximization of Profit in Cloud Computing Ms. Honeymol.O Department of Computer Science and Engineering Nehru College of Engineering and Research Centre, Pampady, Thrissur, University
More informationRobust Multi-unit Auction Protocol against False-name Bids
17th International Joint Conference on Artificial Intelligence (IJCAI-2001) Robust Multi-unit Auction Protocol against False-name Bids Makoto Yokoo, Yuko Sakurai, and Shigeo Matsubara NTT Communication
More informationMetaXpress High-Content Image Acquisition and Analysis Software
High-Content Image Acquisition and Analysis Software BENEFITS Meet high throughput requirements with a scalable, streamlined workflow Adapt your analysis tools to tackle your toughest problems, including
More informationMigrating From ProClarity
Migrating From ProClarity History of ProClarity From ProClarity Corporation s brochure: ProClarity Analytics 6 provides organizations with powerful yet simple-to-use analysis tools to cover everything
More informationEngageOne INTERACTIVE COMMUNICATIONS. An Advanced Interactive Technology Solution for a New Era of Enterprise Communications
EngageOne INTERACTIVE COMMUNICATIONS An Advanced Interactive Technology Solution for a New Era of Enterprise Communications ENTERPRISE CUSTOMER COMMUNICATION MANAGEMENT Companies send many types of document
More informationEfficiency of Dynamic Pricing in Priority-based Contents Delivery Networks
Efficiency of Dynamic Pricing in Priority-based Contents Delivery Networks Noriyuki YAGI, Eiji TAKAHASHI, Kyoko YAMORI, and Yoshiaki TANAKA, Global Information and Telecommunication Institute, Waseda Unviersity
More informationSAVE MAINFRAME COSTS ZIIP YOUR NATURAL APPS
ADABAS & NATURAL SAVE MAINFRAME COSTS ZIIP YOUR NATURAL APPS Reduce your mainframe TCO with Natural Enabler TABLE OF CONTENTS 1 Can you afford not to? 2 Realize immediate benefits 2 Customers quickly achieve
More informationCost Optimization for Cloud-Based Engineering Simulation Using ANSYS Enterprise Cloud
Application Brief Cost Optimization for Cloud-Based Engineering Simulation Using ANSYS Enterprise Cloud Most users of engineering simulation are constrained by computing resources to some degree. They
More informationPricing with Bandwidth Guarantees for Clients with multi-isp Connections
Pricing with Bandwidth Guarantees for Clients with multi-isp Connections Rohit Tripathi and Gautam Barua Department of Computer Science and Engineering Indian Institute of Technology, Guwahati Guwahati-78039,
More informationDesign of a Performance Measurement Framework for Cloud Computing
A Journal of Software Engineering and Applications, 2011, *, ** doi:10.4236/jsea.2011.***** Published Online ** 2011 (http://www.scirp.org/journal/jsea) Design of a Performance Measurement Framework for
More informationUIC NEWS WEEKLY ADVERTISING AGREEMENT
UIC NEWS WEEKLY ADVERTISING AGREEMENT This Advertising Agreement is between The Board of Trustees of the University of Illinois, a body corporate and politic of the State of Illinois ( University ) for
More informationPassenger Batch Arrivals at Elevator Lobbies
Passenger Batch Arrivals at Elevator Lobbies Janne Sorsa, Juha-Matti Kuusinen and Marja-Liisa Siikonen KONE Corporation, Finland Key Words: Passenger arrivals, traffic analysis, simulation ABSTRACT A typical
More informationMulti-depot Vehicle Routing Problem with Pickup and Delivery Requests
Multi-depot Vehicle Routing Problem with Pickup and Delivery Requests Pandhapon Sombuntham a and Voratas Kachitvichyanukul b ab Industrial and Manufacturing Engineering, Asian Institute of Technology,
More informationCROWNBench: A Grid Performance Testing System Using Customizable Synthetic Workload
CROWNBench: A Grid Performance Testing System Using Customizable Synthetic Workload Xing Yang, Xiang Li, Yipeng Ji, and Mo Sha School of Computer Science, Beihang University, Beijing, China {yangxing,
More informationPilot Opening Auction For Ground-Mounted PV To Bidders From Other EU Member States. Concept-Note
Pilot Opening Auction For Ground-Mounted PV To Bidders From Other EU Member States Concept-Note Imprint Publisher Federal Ministry for Economic Affairs and Energy (BMWi) Public Relations 11019 Berlin,
More informationTowards Autonomic Virtual Applications in the In-VIGO System
Towards Autonomic Virtual Applications in the In-VIGO System Jing Xu, Sumalatha Adabala, José A. B. Fortes Advanced Computing and Information Systems Electrical and Computer Engineering University of Florida
More informationA Dynamic Trust Network for Autonomy-Oriented Partner Finding
A Dynamic Trust Network for Autonomy-Oriented Partner Finding Hongjun Qiu Abstract The problem of partner finding is to identify which entities (agents) can provide requested services from a group of entities.
More informationIntelligent Agent Supported Flexible Workflow Monitoring System
Intelligent Supported Flexible Workflow System Minhong Wang and Huaiqing Wang Department of Information Systems, City University of Hong Kong Kowloon, Hong Kong {iswmh,iswang}@is.cityu.edu.hk Abstract.
More informationBenefits of a Service Level Agreement with Upfitters
AS FEATURED IN Work Truck Magazine Benefits of a Service Level Agreement with Upfitters January 2017, Work Truck - Feature By Mike Antich A service level agreement (SLA) is a contract between a service
More informationSINGLE MACHINE SEQUENCING. ISE480 Sequencing and Scheduling Fall semestre
SINGLE MACHINE SEQUENCING 2011 2012 Fall semestre INTRODUCTION The pure sequencing problem is a specialized scheduling problem in which an ordering of the jobs completely determines a schedule. Moreover,
More informationSmall Businesses Go Solar. Mari Kong 11/20/14
Small Businesses Go Solar Mari Kong 11/20/14 1 Summary When I talk to local businesses about going solar, the first questions people usually ask are, Why me? Why does it matter if I go solar? This paper
More information1. For s, a, initialize Q ( s,
Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. A REINFORCEMENT LEARNING ALGORITHM TO MINIMIZE THE MEAN TARDINESS
More informationELOenterprise. Document Management Archiving Workflow
ELOenterprise Document Management Archiving Workflow ELO Enterprise Content Management E L O E n t e r p r i s e C o n t e n t M a n a g e m e n t Dear Sir or Madam I would like to take this opportunity
More informationProduction Management Modelling Based on MAS
International Journal of Automation and Computing 7(3), August 2010, 336-341 DOI: 10.1007/s11633-010-0512-x Production Management Modelling Based on MAS Li He 1 Zheng-Hao Wang 2 Ke-Long Zhang 3 1 School
More informationLearning Based Admission Control and Task Assignment in MapReduce
Learning Based Admission Control and Task Assignment in MapReduce Thesis submitted in partial fulfillment of the requirements for the degree of MS by Research in Computer Science and Engineering by Jaideep
More informationII. INFORMATION NEEDS ASSESSMENT: A TOP-DOWN APPROACH
II. INFORMATION NEEDS ASSESSMENT: A TOP-DOWN APPROACH The challenge: Know thy market. Your market has many constituencies, with many distinct information needs.. With changes in the marketplace occurring
More informationONBOARDING. 4 Ways icims Talent Platform Helps Build Best-Practices icims Inc. All Rights Reserved.
ONBOARDING 4 Ways icims Talent Platform Helps Build Best-Practices Table of Contents The Rising Importance of Onboarding...... 3 Your Employees Notice the Difference...... 6 The Power of a Talent Acquisition
More informationOvercoming the Management Challenges of Portal, SOA, and Java EE Applications
An Oracle White Paper April 2010 Overcoming the Management Challenges of Portal, SOA, and Java EE Applications Disclaimer The following is intended to outline our general product direction. It is intended
More informationCapitalism: Meaning, Features, Merits and De-Merits
Capitalism: Meaning, Features, Merits and De-Merits Meaning of Capitalism: Definition: Under capitalism, all farms, factories and other means of production are the property of private individuals and firms.
More informationBest in Service Program Overview
Best in Service Program Overview Effective November 1, 2016 Contents Overview... 3 1.1 What is Best in Service?... 3 1.2 Program Goals... 3 1.3 Data Center Considerations... 4 1.4 Designation Types: Orange
More informationComparative Analysis of Scheduling Algorithms of Cloudsim in Cloud Computing
International Journal of Computer Applications (975 8887) Comparative Analysis of Scheduling Algorithms of Cloudsim in Cloud Computing Himani Department of CSE Guru Nanak Dev University, India Harmanbir
More informationA General Maturity Model and Reference Architecture for SaaS Service
A General Maturity Model and Reference Architecture for SaaS Service Seungseok Kang 1, Jaeseok Myung 1, Jongheum Yeon 1, Seong-wook Ha 2, Taehyung Cho 2, Ji-man Chung 2, and Sang-goo Lee 1 1 Department
More informationIMAGE PROCESSING SERVICES OVER NETWORKS
IMAGE PROCESSING SERVICES OVER NETWORKS Deepika Pahuja Assistant Professor, DAVIM ABSTRACT Image Processing can be used for distributed environment in order to provide image processing services over integrated
More information1 of 15 2/4/09 8:01 PM
Automatic Formation and Analysis of Multi-Agent Virtual Organization* Qinhe Zheng & Xiaoqin Zhang Department of Computer and Information Science University of Massachusetts at Dartmouth 285 Old Westport
More information