2015 3rd International Conference on Future Internet of Things and Cloud
Formalizing Over Design and Under Design of Value Engineering for Context-Aware Cloud Service Development ∗
Yucong Duan∗ , Qiang Duan† , Abdelrahman Osman Elfaki ‡ , and Chengxiang Ren∗ Hainan University, Haikou, China. Email:
[email protected],
[email protected] † Pennsylvania State University, Abington PA, USA. Email:
[email protected] ‡ Tabuk University, Saudi Arabia. Email:
[email protected]
amount of context-related data, including user environment as well as system status, to make timely decision of system operations for meeting user requirements and achieving optimal system resource utilization as well. Value Engineering [8] may be applied to tackle this challenging problem. Value engineering is developed as an effective approach to maximizing the potential of optimization in system development process. Value engineering technique aims to achieve cost-effective by maintaining a high functionality to cost ratio in each stage of a system development process to obtain a value-driven design [1], which enables design optimization through value analysis. In basic value engineering, value is determined as ratio of implemented functions to the amount of consumed resources; that is
Abstract—Context-aware Cloud services, which may significantly enhance usability of the future Internet-based information infrastructure, also brings in new challenges to Cloud system development. In this paper, we investigate application of value engineering, a systematic method for achieving optimal system design, to address the challenges introduced by the complex computing and networking systems required for context-aware Cloud services. We particularly study the Under Design (UD) and Over Design (OD) problems in bridging business planning and technical implementation of Cloud services. We propose a cognitive conceptualization of UD/OD from a knowledge introduction perspective and show effectiveness of the formalization through simulations. Index Terms—Context-aware Cloud service, Under design, Over design, knowledge management, value Engineering, conceptualization
value ≈ f unction/resources. I. I NTRODUCTION
When applying value engineering to facilitate context-aware Cloud service development, we need to consider nonfunctional attributes of a service system, which is typically represented as quality of the service, is equally important as the function of the service. Therefore, we extend the above value measurement to be
In the past few years some emerging networking and computing technologies, including Internet of Things (IoT), Cloud computing, and wireless mobile networking, have caused fundamental change in architecture of the future Internet-based information infrastructure. IoT attaches a large number of smart objects such as sensors and actuators to the Internet; thus connecting the physical and cyber worlds for data collection, transmission, dissemination, and control. Cloud offers virtual computing and storage capacities that can be accessed ondemand through the Internet. Wireless mobile networks allow users to access network and Cloud services from their mobile devices at any location; thus greatly improving network and Cloud usability. These emerging technologies, working together, are shifting the Internet from a basic communication infrastructure to a comprehensive infrastructure that integrates data collection, transmission, process, storage, dissemination, and control for service provisioning. Virtualization and the Service-Oriented Architecture (SOA) form the basis of a converged networking and Cloud computing service infrastructure [7]. With the widely adoption of IoT, a large amount of context data may be collected and analyzed to facilitate provisioning of contextaware Cloud services to users. On the other hand, IoT-based context-aware Cloud services bring in new challenges to system development. The information systems that support context-aware Cloud services must be able to analyze large 978-1-4673-8103-1/15 $31.00 © 2015 IEEE DOI 10.1109/FiCloud.2015.14
value ≈ f unction∗quality/(time∗ef f ort∗knowledge∗cost). That is, value can be increased by either improving the function and quality, or reducing the resources of time, effort, knowledge and cost. Being cost-effective in every stage of a service development process guarantees positive accumulation of the total system value. A holistic profiting strategy can be built based on such a guided process through introducing an ideal routine with optimized values for each stage. Although value engineering and value-driven design may facilitate the complex development process for context-aware Cloud services, some technical issues still need to be further investigated. Actual design is expected to approximate the ideal routine to achieve optimization in service development. The value of actual design is measured as deviation from the planned value in the ideal routine. Value-driven implementation requires continous control based on the observed direction and extent of value deviation. Direction of value deviation can be mapped to two commonly accepted concepts: Over Design (OD) and Under Design (UD). However, there lacks formal definitions for UD and OD that can fully support rigorously 72
Thus, the pursuit of ideal design is a process of gathering enough knowledge to derive proper constraints for model transformation. For example, the weakest constraint of message pay in the sequence diagram is that class FlightService has a method named pay. That is to say, method pay may be in a superclass of FlightService (such as TravelAgent). The constraints are written in OCL:
reasoning in the complete solution method. To resolve this problem in this paper, we first abstract the system development process as a series of knowledge introduction. The ideal design maps to the optimized knowledge introduction set. Then we reveal the semantics of OD and UD in this context. We also show the effect of OD/UD in contrast to ID and the control process. The rest of this paper is organized as follows: the next Section introduces our running examples, which shows the UD/OD in an object oriented scenario. Section III explains and analyses UD/OD as core concepts for implementing Value Engineering for information system design. Section IV reveals the formal meaning of UD and OD from a knowledge introduction perspective. Section V shows the characteristics of UD/OD in comparison with ideal design and illustrate the effect of intervention. Finally, the paper ends with related work and conclusions in Sections VI and VII.
c1 context Package inv: self.classes->exists(c|c.name=’FlightService’) c2 context Class inv: self.name=’FlightService’ implies self.providedMethods->exists(m|m.name=’pay’)
III. E XPLANATION IN NATURAL LANGUAGE Currently UD and OD are two general concepts which do not have formal definitions other than their natural language forms indicting a design deviates expectation. In this work, we specify the meaning of them according to the purpose of using them to guide the modelling,transformation, evaluation, control of the design evolution towards deploying the resources/effort/time for maximizing the profit and satisfaction among targeted stakeholders in a Value Engineering process. The concept of value is used to unify the measure of a design state. Based on this value definition, from the knowledge management perspective we define Under Design (UD) and Over Design (OD) as follows: Definition: Over Design (OD) is the evaluated value of the design product or product family or intermediate design model in a certain design stage/state is more robust or complicated than that is necessary for its whole investment by stakeholders either from short run measurement or from a long run one. The produced extra quality or functionality will cost resources including: project time, human effort, and deviation from optimized goals. (a) Project time: usually extra functionality and quality will cost more time than normal goals. (b) Human effort: extra functionality and quality will demand additional human effort based on normal ones from an incremental perspective. (c) Deviation from optimization: OD may happen on intermediate design in the form of not extra higher or lower functionality/quality in the final design but earlier design decisions should happen in a later optimized/proper time. This earlier decision may be subjective or random which by probabilistic will subordinate to ideal design in terms of value contribution in the final system. From a variability management perspective, OD will leave less variability space for further change or more content than expected from a cost effective criteria. This decision might exclude the chance of making proper decision with enough knowledge at a later stage of the design process. This will be embodied not as extra functionality/quality but deficiencies. Then the reaching of the expected goal based on optimized design process will be hindered. This situation may be more complex when the relationship among decisions are considered. Cognitively OD can be attributed to that either subjective knowledge created by designers instead of provided by stakeholders is introduced and transformed into design at a design
II. RUNNING EXAMPLE In a Tourism Service System, we need to design a travel agent to provide car service and flight service. A traditional flight service process is shown as a sequence diagram on the right corner of Figure 1. In Figure 1, a customer interacts with an actual flight service to complete his or her flight booking. It is known that in UML modeling, every message turns into a related method of a specified class. Thus, a direct constraint can be written in ATL-like syntax as follows: from s : SequenceDiagram!Message to t : ClassDiagram!Method ( name
