Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

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scale to meet changing user demands quickly; separation of infrastructure maintenance duties from users; location of infrastructure in areas with lower costs of real estate and electricity; sharing of peak-load capacity among a large pool of users and so on. Given the recent popularity of cloud computing, and more importantly the appealing applicability of cloud computing to the scenario of data and computation intensive scientific workflow applications, there is an increasing demand to investigate scientific cloud workflow systems. For example, scientific cloud workflow systems can support many complex e-science applications such as climate modelling, earthquake modelling, weather forecast, disaster recovery simulation, Astrophysics and high energy physics [30, 88, 92]. These scientific processes can be modelled or redesigned as scientific cloud workflow specifications (consisting of such as workflow task definitions, process structures and QoS constraints) at build-time modelling stage [30, 55]. The specifications may contain a large number of computation and data intensive activities and their non-functional requirements such as QoS constraints on time and cost [98]. Then, at runtime execution stage, with the support of cloud workflow execution functionalities such as workflow scheduling [100], load balancing [12] and temporal verification [19], cloud workflow instances are executed by employing the supercomputing and data sharing ability of the underlying cloud computing infrastructures with satisfactory QoS. One of the research issues for cloud workflow systems is on how to deliver high QoS [19, 25]. QoS is of great significance to stakeholders, viz. service users and providers. On one hand, low QoS may result in dissatisfaction and even investment loss of service users; on the other hand, low QoS may risk the service providers of out-of-business since it decreases the loyalty of service users. QoS requirements are usually specified as quantitative or qualitative QoS constraints in cloud workflow specifications. Generally speaking, the major workflow QoS constraints include five dimensions, viz. time, cost, fidelity, reliability and security [98]. Among them, time, as one of the most general QoS constraints and basic measurements for system performance, attracts many researchers and practitioners [54, 105]. For example, a daily weather forecast scientific cloud workflow which deals with the collection and processing of large volumes of meteorological data has to be finished before the broadcasting of the weather forecast program every day at, for instance, 6:00pm. 2
Clearly, if the execution time of the above workflow applications exceeds their temporal constraints, the consequence is usually unacceptable to all stakeholders. To ensure on-time completion of these workflow applications, sophisticated strategies need to be designed to support high temporal QoS in scientific cloud workflow systems. At present, the main tools for workflow temporal QoS support is temporal checkpoint selection and temporal verification which deals with the monitoring of workflow execution against specific temporal constraints and the detection of temporal violations [20]. However, to deliver high temporal QoS in scientific cloud workflow systems, a comprehensive temporal framework which can support the whole lifecycles, viz. from build-time modelling stage to runtime execution stage, of time-constrained scientific cloud workflow applications needs to be fully investigated. 1.2 Motivating Example and Problem Analysis In this section, we present an example in Astrophysics to analyse the problem for temporal QoS support in scientific cloud workflow systems. 1.2.1 Motivating Example Parkes Radio Telescope (http://www.parkes.atnf.csiro.au/), one of the most famous radio telescopes in the world, is serving for institutions around the world. Swinburne Astrophysics group (http://astronomy.swinburne.edu.au/) has been conducting pulsar searching surveys (http://astronomy.swin.edu.au/pulsar/) based on the observation data from Parkes Radio Telescope. The Parkes Multibeam Pulsar Survey is one of the most successful pulsar surveys to date. The pulsar searching process is a typical scientific workflow which involves a large number of data and computation intensive activities. For a single searching process, the average data volume (not including the raw stream data from the telescope) is over 4 terabytes and the average execution time is about 23 hours on Swinburne high performance supercomputing facility (http://astronomy.swinburne.edu.au/supercomputing/). 3
Page 1 and 2: A Novel Probabilistic Temporal Fram
Page 3 and 4: Declaration This thesis contains no
Page 5 and 6: Abstract Cloud computing is a lates
Page 7 and 8: Component 2, the state of scientifi
Page 9 and 10: http://dx.doi.org/10.1016/j.jpdc.20
Page 11 and 12: 17. X. Liu, J. Chen, and Y. Yang, A
Page 13 and 14: 4.2 RELATED WORK AND PROBLEM ANALYS
Page 15 and 16: STRATEGY ..........................
Page 17 and 18: FIGURE 7.4 EXPERIMENTAL RESULTS (NO
Page 19: Chapter 1 Introduction This thesis
Page 23 and 24: to seek all the candidates. Further
Page 25 and 26: e unnecessary. Hence, an effective
Page 27 and 28: temporal violations, viz. recoverab
Page 29 and 30: In Chapter 2, we introduce the rela
Page 31 and 32: a two-stage searching process with
Page 33 and 34: QoS requirements. Generally speakin
Page 35 and 36: handling strategies employed in a s
Page 37 and 38: activity points to conduct temporal
Page 39 and 40: compensation, is believed as a suit
Page 41 and 42: successful completion. Specifically
Page 43 and 44: Chapter 4 and Chapter 5 respectivel
Page 45 and 46: etween time deficit compensation an
Page 47 and 48: service user’s requirements and t
Page 49 and 50: conducted for three times, i.e. sta
Page 51 and 52: PTDA+ACOWR as an example to introdu
Page 53 and 54: currently running. It is built on t
Page 55 and 56: workflow instance to determine whet
Page 57 and 58: framework for cost-effective delive
Page 59 and 60: pattern based forecasting strategy.
Page 61 and 62: 4.2 Related Work and Problem Analys
Page 63 and 64: 4.2.2 Problem Analysis In cloud wor
Page 65 and 66: significantly deteriorate the overa
Page 67 and 68: activity durations: characteristics
Page 69 and 70: specified with different means and
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Table 4.1 Notations Used in K-MaxSD
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1) Duration series building As ment
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Table 4.5 Algorithm 4: Pattern Matc
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to search for those activities whic
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cycle. Here, we also trace back the
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ecognition. Sliding Window ranks in
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predicted value will be the one pre
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segmentation algorithm is capable o
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distributed soft real-time system,
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Table 5.1 Overview on the Support o
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2 2 can be denoted as N ( µ , σ )
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mean iteration times or with some p
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Figure 5.4 Choice Building Block No
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activities may be omitted for the e
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5.3.1 Calculating Weighted Joint Di
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constraints until the constraint is
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Therefore, it can be expressed as n
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Table 5.3 Specification of the Work
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and the constraint for activity X 1
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ease of discussion and to avoid the
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Unfortunately, conventional discret
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The probability consistency range w
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6.2.3 Temporal Checkpoint Selection
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100 times each. All the activity du
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normal distribution and correlated
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Figure 6.3 Checkpoint Selection wit
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work and problem analysis. Section
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expected time redundancy of subsequ
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the probability for self-recovery i
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skipped if self-recovery applies. A
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To evaluate the average performance
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activities and around 300 violation
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Figure 7.3 Experimental Results (No
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Figure 7.5 Cost Reduction Rate vs.
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8.1 Related Work and Problem Analys
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scientific processes, human interve
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alone does not involve any time def
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swap slower machines in the active
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TD( a p ) L{ ( ai , R j ) | i = p +
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For example, in Figure 8.2, local w
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value to sample all of the solution
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mapping task a i to resource R j fr
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have done some simulation experimen
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violations can still be recovered b
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PTDA+ACOWR for Level III Violations
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an integer from 1 to 5 where the ex
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D: Definition for Fitness Value Fit
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strategy; and End(Rescheduling) is
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(a) Optimisation Ratio on Total Cos
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makespan. In our two stage workflow
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espectively. It is clearly that ACO
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comparison results on the violation
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percentiles. The average global vio
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equivalent times of ACOWR are calcu
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Chapter 9 Conclusions and Future Wo
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the real world, simulation experime
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ased temporal consistency model. Ac
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the whole lifecycle support for hig
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which can be further investigated i
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Bibliography [1] W. M. P. van der A
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Systems", ACM Trans. on Software En
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conjunction with 23 rd Parallel and
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Elsevier, vol. 84, no. 3, pp. 354-3
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Distributed Computing Systems", Jou
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Appendix: Notation Index Symbols α
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PTR ( U ( SW ), ( a p + , a p + 1 m
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