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

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Chapter 2 Literature Review and Problem Analysis This chapter reviews the existing work related to temporal QoS. This chapter is organised as follows. Section 2.1 gives a general introduction of workflow temporal QoS. Section 2.2 reviews the temporal consistency model. Section 2.3 reviews the temporal constraint setting. Section 2.4 reviews the temporal checkpoint selection and temporal verification. Section 2.5 reviews the temporal violation handling. In each section, the problems with the current work are analysed along the line. 2.1 Workflow Temporal QoS In a cloud environment, there are a large number of similar or equivalent resources provided by different service providers. Cloud service users can select suitable resources and deploy them for cloud workflow applications. These resources may provide the same functionality, but optimise different QoS measures [81]. Meanwhile, different service users or applications may have different expectations and requirements. Therefore, it is insufficient for a scientific cloud workflow system to only consider functional characteristics of workflow applications. QoS requirements such as time limits (temporal constraints) and budget limits (cost constraints) for cloud workflow execution also need to be managed by scientific cloud workflow systems. Service users must be able to specify their QoS requirements of scientific cloud workflows at build time. Then, the actions taken by the cloud workflow systems at runtime must be chosen according to the original 14
QoS requirements. Generally speaking, there are five basic dimensions for cloud workflow QoS, viz. time, cost, fidelity, reliability and security [98]. Time is a basic measurement of system performance [1, 2, 49]. For workflow systems, the makespan often refers to the total time overheads required for completing the execution of a workflow. The total cost often refers to the monetary cost associated with the execution of workflows including such as the cost for managing workflow systems and usage charge of cloud resources for processing workflow activities. Fidelity refers to the measurement related to the quality of the output of workflow execution. Reliability is related to the number of failures of workflows. Security refers to confidentiality of the execution of workflow tasks and trustworthiness of resources. Among them, time, as a basic measurement of performance and general non-functional requirement, has attracted most of the attention from researchers and practitioners in the area of such as Software Engineering [84], Parallel and Distributed Computing [44, 71] and Service Orientated Architectures [35]. In this thesis, we focus on time, i.e. we investigate the support of high temporal QoS in scientific cloud workflow systems. In the real world, most scientific processes are assigned with specific deadlines in order to achieve their scientific targets on time. For those processes with deterministic process structures and fully controlled underlying resources, individual activity durations, i.e. the completion time of each activity, are predictable and stable. Therefore, process deadlines can normally be satisfied through a build-time static scheduling process with resource reservation in advance [36, 100]. However, stochastic processes such as scientific workflows are characterised with dynamic changing process structures due to the nature of scientific investigation. Furthermore, with a vast number of data and computation intensive activities, complex workflow applications are usually deployed on dynamic high performance computing infrastructures, e.g. cluster, peer-to-peer, grid and cloud computing [4, 65, 92, 95]. Therefore, how to ensure cloud workflow applications to be finished within specific deadlines is a challenging issue. In fact, this is why temporal QoS is more emphasised in large scale distributed workflow applications compared with traditional centralised workflow applications [1]. In the following sections, we will introduce the current work related to temporal 15
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: a two-stage searching process with
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
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
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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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