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

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2.5 Temporal Violation Handling After temporal verification, the current temporal consistency state is verified, and if a temporal violation is identified, the follow-up step is to handle such a violation, i.e. temporal violation handling. So far, the study on temporal violation handling is still in its infancy. Temporal violation handling is very different from conventional violation handling which mainly deals with functional failures in workflow systems [42, 83]. Usually, after functional failures are detected, violation handling process such as rollback and recovery is triggered [9]. Another way to prevent functional failures is to run duplicate instances of the same workflows, and hence if one instance fails, the others can still be running. However, duplicate instances will result in huge management cost which is several times higher than the needed execution cost in general [28]. As for temporal violations, as a type of non-functional QoS failures, they cannot be handled after they actually took place since no actions in the real world can reduce the execution time of workflow activities after they have already been finished. In fact, in a cloud computing environment, if a workflow application has exceeded its deadline (i.e. the global temporal constraint), the service provider can do nothing but most likely to pay the penalty according to service contracts or negotiate with the service user for endurance, i.e. extra waiting time. Therefore, rather than passive recovery, temporal violations need to be proactively prevented by temporal verification and temporal violation handling before actual large overruns take place [56]. In other words, effective handling of local temporal violations along workflow execution instead of recovery of global temporal violations is what needs to be investigated for the delivery of high temporal QoS in scientific cloud workflow systems. The work in [82] introduces five types of workflow exceptions where temporal violation can be classified into deadline expiry. Meanwhile, three alternate courses of recovery action, i.e. no action (NIL), rollback (RBK) and compensate (COM), are also presented. NIL which counts on the automatic recovery of the system itself is normally not acceptable for the ‘risk-free’ purpose. As for RBK, unlike handling conventional system function failures, it normally causes extra delays and makes current inconsistency state even worse. However, COM, i.e. time deficit 20
compensation, is believed as a suitable recovery action for temporal violation. One of the representative solutions introduced in [17] is a time deficit allocation strategy (TDA) which compensates current time deficit by utilising the expected time redundancies of subsequent activities. However, since time deficit has not been truly reduced, this strategy can only delay the violations of some local constraints with no effectiveness on overall constraints, e.g. the deadlines. On the contrary, workflow rescheduling [101] can indeed make up time deficit by expediting the execution of those not-yet-commenced workflow activities. However, since general workflow scheduling is an NP complete problem, extra cost is hence inevitable [100]. Up to now, there are few efforts dedicated to this topic. However, in order to deliver high temporal QoS in scientific cloud workflow systems, temporal violation handling plays a significant role. Therefore, it is important that cost-effective temporal violation handling strategies should be investigated. As for the measurement, the key criterion is their performance, i.e. how much time deficit can be compensated. Meanwhile, the cost of the compensation process (e.g. time overheads and monetary cost) should also be considered since it is unacceptable if the cost of the compensation process itself is significant, e.g. exceeding the expected cost such as the penalty brought by those temporal violations. 2.6 Summary In this chapter, the literatures and problems for the recent studies related to the temporal QoS support in scientific cloud workflow systems including temporal consistency model, temporal constraint setting, temporal consistency monitoring, and temporal violation handling have been presented. Meanwhile, based on the literature review and problem analysis, the general requirements for temporal consistency model, constraint setting, consistency monitoring, and temporal violations handling have also been discussed. 21
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 and 20: Chapter 1 Introduction This thesis
Page 21 and 22: Clearly, if the execution time of t
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: activity points to conduct temporal
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
Page 71 and 72: Table 4.1 Notations Used in K-MaxSD
Page 73 and 74: 1) Duration series building As ment
Page 75 and 76: Table 4.5 Algorithm 4: Pattern Matc
Page 77 and 78: to search for those activities whic
Page 79 and 80: cycle. Here, we also trace back the
Page 81 and 82: ecognition. Sliding Window ranks in
Page 83 and 84: predicted value will be the one pre
Page 85 and 86: segmentation algorithm is capable o
Page 87 and 88: distributed soft real-time system,
Page 89 and 90:
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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