Proposed Cost Model Research Articles

Testing effort based modeling to determine optimal release and patching time of software

In this era of information technology, our dependence on software systems is increasing day by day. This dependence on software systems has increased the pressure on software firms to fulfill the customer’s demand for highly reliable software. On the other hand, for ensuring high reliability of the software prolonged testing is required, which consumes large amount of resources hence not feasible in the current stiff market competition. Further delay in release can cost a lot in terms of market opportunity. Therefore, to sustain in the market, firms are releasing the software early and removing the remaining number of bugs by updating with patches. A patch is a piece of software designed to update a computer program or its supporting data, to fix or improve it. With such patches usually called bug fixes, firms improve the usability or performance of the software. Providing patches needs extra amount of effort and manpower which costs high. Also early patch release may result in improper removal of bugs and late release can increase the risk of more of failures in the operational phase To overcome the above issues we have proposed a testing effort based cost model to determine the optimal release and patch time of a software so that the total cost is minimized. In the proposed cost model developing team continues removing the faults even after software release. Further, we have taken different distribution function in pre and post release phase (before and after patching) to develop the proposed cost model. Numerical illustration is provided at the end of the paper for validation of the proposed cost model.

Supply chain cost model for prefabricated building material based on time-driven activity-based costing

The effect of ‘supply chain management’ can be leveraged when benefits of collaboration within and beyond the capacities of individual organizations are witnessed. One of the primary tasks in reducing total supply chain costs is to understand where the costs occur in a supply chain and how each activity impacts the total supply chain costs. Most supply chains in construction usually involve multiple entities, each one in a different process. A rebar supply chain is one example where many entities are involved in different processes. The supply chain coordinator needs a supply chain cost model, which shows how each activity impacts all supply chain costs to reduce the total costs. The research suggests a supply chain cost model using time-driven activity-based costing. The proposed cost model was applied to a building construction project, followed by sensitivity analysis identifying critical activities. This method can be adapted to analyze other fragmented material supply chains in the construction industry.

Energy and QoS aware resource allocation for heterogeneous sustainable cloud datacenters

As the demand on Internet services such as cloud and mobile cloud services drastically increases recently, the energy consumption consumed by the cloud datacenters has become a burning topic. The deployment of renewable energy generators such as PhotoVoltaic (PV) and wind farms is an attractive candidate to reduce the carbon footprint and, achieve the sustainable cloud datacenters. However, current studies have focused on geographical load balancing of Virtual Machine (VM) requests to reduce the cost of brown energy usage, while most of them have ignored the heterogeneity of power consumption of each cloud datacenter and the incurred performance degradation by VM co-location. In this paper, we propose Evolutionary Energy Efficient Virtual Machine Allocation (EEE-VMA), a Genetic Algorithm (GA) based metaheuristic which supports a power heterogeneity aware VM request allocation of multiple sustainable cloud datacenters. This approach provides a novel metric called powerMark which diagnoses the power efficiency of each cloud datacenter in order to reduce the energy consumption of cloud datacenters more efficiently. Furthermore, performance degradation caused by VM co-location and bandwidth cost between cloud service users and cloud datacenters are considered to avoid the deterioration of Quality-of-Service (QoS) required by cloud service users by using our proposed cost model. Extensive experiments including real-world traces based simulation and the implementation of cloud testbed with a power measuring device are conducted to demonstrate the energy efficiency and performance assurance of the proposed EEE-VMA approach compared to the existing VM request allocation strategies.

Cloud Computing Cost and Negotiation: A Survey

computing brings advancement in the field of information technology. Cloud computing now offers organizations more choices regarding how to run infrastructures, save costs, and delegate liabilities to third - party providers. It offers variety of computing services to end users. These services are offered at different prices using various pricing schemes and techniques. End clients will support the service supplier offering the best service with the most minimal cost. Cloud pricing models are complicated, which makes purchasing decisions for consumers difficult. Comparing of prices across providers is a challenge as well. This survey paper is based on the various proposed cost models and already executed cost models. This paper attempts to provide ideas that may help to build more appropriate pricing models in future that will benefit consumers as well as service providers. KeywordsComputing, Pricing, Negotiation, service models

An Optimal Ordering Policy for a Probabilistic Fixed Lifetime Inventory Model with Continuous Demand Rate

This paper deals with the problem of computing optimal or dering policies for the probabilistic fixed lifetime inventory model with continuous demand rate. We proposed a probabilistic fixed lifetime inventory model with continuous demand rate. The necessary condition for minimizing the expected proposed cost model was derived. The condition is also sufficient because the model is convex in S. The optimal ordering policies for this probabilistic fixed li fetime inventory system with continuous demand rate were given. The objective of the study is to examine decisions regarding when to order or not. This was investigated under some conditions. The operating charact eristics obtained in this article are very significant because, for practical problems, available m athematically optimal solutions to the fixed lifetime inventory problem cannot be realized due to their co mputational complexity arising from the fact that exact formulation of the problem requires information on the age distribution of the items in inventory and the corresponding quantity of items of each age. Hence there is a gap between theoretical results and practical requirements for computational result s. We have been able to bridge the gap between theoretical results and practical requirements for comput ational results. We computed the ordering cost, expected holding cost, expected shortage cost and expected out dates cost, and these computations were applied to determine the expected cost for the fixed lifet ime inventory system. The expected cost model with Set- up Cost and without Set- up Cost were useful costs for the determination of the optimal ordering policy for this type of inventory system Finally, orga nization may like to operate under a given aspiration scenario, values of inventory level that satisfy such cond ition are identified and are used in the cost function to determine optimal operating conditions, this will go a long way to reduce waste and holding cost.

Cost model based service placement in federated hybrid clouds

As cloud federation allows companies in need of computational resources to use computational resources hosted by different cloud providers, it reduces the cost of IT infrastructure by lowering capital and operational expenses. This is the result of economies of scale and the possibility for organizations to purchase just as much computing and storage resources as needed whenever needed. However, a clear specification of cost savings requires a detailed specification of the costs incurred. Although there are some efforts to define cost models for clouds, the need for a comprehensive cost model, which covers all cost factors and types of clouds, is undeniable. In this paper, we cover this gap by suggesting a cost model for the most general form of a cloud, namely federated hybrid clouds. This type of cloud is composed of a private cloud and a number of interoperable public clouds. The proposed cost model is applied within a cost minimization algorithm for making service placement decisions in clouds. We demonstrate the workings of our cost model and service placement algorithm within a specific cloud scenario. Our results show that the service placement algorithm with the cost model minimizes the spending for computational services.

When to stop testing multi upgradations of software based on cost criteria

Software testing is an important phase of the software development life cycle to achieve highly reliable software. Due to the time and resource limitation during the testing phase, firms do not attempt to deliver a complete and perfect product in one development cycle. They plan multi upgradations of software by adding new functionalities. Many models have been developed in the past which discuss about when to stop testing and when to release the software to the users. But they have been limited to the study of single version only. In the present framework, we describe a unified approach to address an important issue of when to stop testing the multi-upgradation of software, which is a complex process. The total debugging cost for each upgradation includes the cost of debugging in the warranty period along with the testing cost. It is assumed that the software is supported till the warranty period is over. In the proposed cost model for each upgradation, we consider that some of the remaining faults of previous release are reported and removed partly during the testing period and partly during the warranty period of new upgradation. An algorithm for finding the optimal release time for each version is developed. We estimate the parameters of the model using Statistical Package for Social Sciences on the real data set, and obtain optimum stopping time for each version of the software using Maple software.

Agile Urban Parking Recommendation Service for Intelligent Vehicular Guiding System

Nowadays, Intelligent Transportation Systems (ITS) technologies are exploring a wide range of services such as freeway management, crash prevention & safety, driver assistance, and infotainment of drivers and/or passengers. In this paper, an agile urban parking recommendation service for vehicular intelligent guiding system is designed to facilitate city citizens with fully efficient, real-time and precise parking lot guiding suggestions for the sustainability of the future green city. The system offers drivers a friendly parking lot recommendation sequence and saves driver's time circling around by the accurate prediction of the successful parking probability in each parking lot. The proposed cost model constructs an optimal recommendation sequence considering successful parking probability and time to reach the parking lot. Through the collection and analysis of realistic records from parking lots in Taipei city, a prediction algorithm is developed to estimate the successful parking probability by using current available space counts. Extensive experiments are conducted to demonstrate the effectiveness of the prediction algorithm.

Cost Modelling and Studies with Different Deployment Strategies for Wireless Multimedia Sensor Network Over Flat and Elevated Terrains

Wireless multimedia sensor networks (WMSNs) is widely used for surveillance application. These multimedia (audio and video) nodes are distributed according to different deployment strategies in a multi-tier heterogeneous architecture environment. In this paper we have modelled the deployment cost of WMSN considering the sensor type (audio or video), sensor configuration such as remaining energy of battery, deployment point, and terrain characteristics for surveillance applications. Using our proposed cost models we have studied the effects of different deployment strategies of WMSN over flat and elevated terrains. Our cost models helps in minimizing the cost of deployment while maintaining Quality-of-Service i.e., the coverage and connectivity of the audio and video sensors separately. We have formulated an integer linear program and proposed a heuristic solution to minimize the placement costs subject to network coverage requirements using our first cost model. Our second cost model is used to propose a scheme that will ensure connectivity of the network. We have done simulations with three network deployment strategies, namely deterministic, random and hybrid and show that the hybrid deployment of sensor nodes yields a balance of performance and cost as compared to the other two. Our study provides guidelines for the network architect to select a particular deployment strategy under performance and cost requirements.

Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm

PurposeThe objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment. MethodsThe proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on micro-space to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized single-objective method, is applied to find the Pareto front of total cost and makespan. ResultsIn the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C(IDEA, NSGA-II) of 0.3 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C(IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions. ConclusionsIn the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to make their decision when conflicting objectives are present.

Planning level tunnel cost estimation based on statistical analysis of historical data

Estimating the construction cost of a tunnel with certain diameter and length during the planning stage of a project is essential and a “must do” to justify the project, but it is a very difficult task. Numerous variables influence the cost of tunnel construction and limited information is available at early stages of design when the possible use of tunnels is being studied. Therefore, there is limited number of models at engineers’ disposal for development of a proper cost estimate in tunneling projects. The focus of this study was to offer a model for estimation of the construction cost of tunnels for various applications in the preliminary stage of a project. The work is based on study of nearly 270 projects and statistical analysis of the recoded construction costs, tunnel size, ground conditions, and tunnel applications. Several cost estimation models are introduced for various tunnel types including waste water, water, and transportation tunnels in soft ground and rock. The model uses commonly available cost indices (CCI and BCI) to adjust the estimated costs for the time and location of the project. The proposed cost models presented in this study can included in a simple program to help users make more accurate and quick cost estimates for their future projects. The models do not deal with construction risk which is an essential part of the study of various methods and should be done through developing a risk registry with related cost of each case before final selection, design, and cost estimation of any tunneling project.

Effect of change point and imperfect debugging in software reliability and its optimal release policy

This article presents a software reliability growth model based on non-homogeneous Poisson process. The main focus of this article is to deliver a method for software reliability modelling incorporating the concept of time-dependent fault introduction and fault removal rate with change point. Also in this article, a cost model with change point has been developed. Based on the cost model optimal release policy with change point has been discussed. Maximum likelihood technique has been applied to estimate the parameters of the model. The proposed model has been validated using some real software failure data. Comparison has been made with models incorporating change point and without change point. The application of the proposed cost model has been shown using some numerical examples.

A HYBRID SOFTWARE COST ESTIMATION APPROACH UTILIZING DECISION TREES AND FUZZY LOGIC

Software cost estimation (SCE) is one of the critical activities in software project management. During the past decades various models have been proposed for SCE. However, developing accurate and useful models is limited in practice despite the considerable financial gain they could offer to software stakeholders. Traditional techniques, such as regression, by-analogy and machine learning, face the difficulty of handling the dynamic nature of the software process and the problematic nature of the public data available. This paper addresses the issue of SCE proposing an alternative approach that combines robust decision tree structures with fuzzy logic. Fuzzy decision trees are generated using the CHAID and CART algorithms in a systematic manner, while development effort is treated as the dependent variable against two subsets of factors: The first contains selected attributes from the ISBSG, COCOMO and DESHARNAIS datasets and the second contains a subset of the available factors that can be measured early in the development cycle. The association rules obtained from the trees are then merged and defuzzified through a Fuzzy Implication System (FIS). The fuzzy framework is utilized to perform effort estimations. Experimental results indicate that the proposed approach is promising as it yields quite accurate estimations in most dataset cases considered. Finally, our evaluation suggests that accurate estimations may be produced, even when using only a small set of factors that can be measured early in the development cycle, thus increasing the practical value of the proposed cost model.

Cost-intelligent application-specific data layout optimization for parallel file systems

Parallel file systems have been developed in recent years to ease the I/O bottleneck of high-end computing system. These advanced file systems offer several data layout strategies in order to meet the performance goals of specific I/O workloads. However, while a layout policy may perform well on some I/O workload, it may not perform as well for another. Peak I/O performance is rarely achieved due to the complex data access patterns. Data access is application dependent. In this study, a cost-intelligent data access strategy based on the application-specific optimization principle is proposed. This strategy improves the I/O performance of parallel file systems. We first present examples to illustrate the difference of performance under different data layouts. By developing a cost model which estimates the completion time of data accesses in various data layouts, the layout can better match the application. Static layout optimization can be used for applications with dominant data access patterns, and dynamic layout selection with hybrid replications can be used for applications with complex I/O patterns. Theoretical analysis and experimental testing have been conducted to verify the proposed cost-intelligent layout approach. Analytical and experimental results show that the proposed cost model is effective and the application-specific data layout approach can provide up to a 74% performance improvement for data-intensive applications.

Developing an economic cost model for quantifying supply chain costs

Supply Chain Management (SCM) has become a very powerful tool for any business because it aims to provide a win-win situation for all members by maximising the profitability of the whole chain. However, some benefits from joining the chain are difficult to quantify in monetary terms. If these benefits and savings of supply chain coordination cannot be detected, it would result in a productivity paradox and failure to justify the benefits of building trust and coordination in the supply chain. The proposed classification of supply chain costs with an economic cost model expands the cost concept beyond typical logistics costs by breaking overhead costs according to their cost centre and introducing opportunity cost to estimate the neglected cost of non-value added activities. With this proposed cost model, companies would be able to quantify hidden benefits and their savings as well as to realise the true costs if they choose to join and cooperate in the chain, whereas these savings would be ignored and overlooked by the traditional costing methods, resulting in the possibility of collaboration in such projects (of building the chain) being turned down.

Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements

This work concerns a machine tool selection problem, which consists of selecting the most suitable machine to satisfy manufacturing company requirements. The main goal of this work is to develop a cost estimation support tool for vertical high speed machining centres based on final part and productivity requirements of the company linked with machine tool characteristics available in the catalogues in order to apply the cost model and to calculate machine tool cost estimations. The cost model presented is based on multiple regression analyses and provides reasonably accurate market cost predictions. Applying the proposed cost model will help the user to determine the approximate market cost of the machine and can be especially interesting for decision makers in the preliminary stages of a selection process because it avoids long and costly studies.

A New Proposed Cost Model for List Accessing Problem using Buffering

There are many existing well known cost models for the list accessing problem. The standard cost model developed by Sleator and Tarjan is most widely used. In this paper, we have made a comprehensive study of the existing cost models and proposed a new cost model for the list accessing problem. In our proposed cost model, for calculating the processing cost of request sequence using a singly linked list, we consider the access cost, matching cost and replacement cost. The cost of processing a request sequence is the sum of access cost, matching cost and replacement cost. We have proposed a novel method for processing the request sequence which does not consider the rearrangement of the list and uses the concept of buffering, matching, look ahead and flag bit.

LABILE: link quAlity-based lexIcaL routing mEtric for reactive routing protocols in IEEE 802.15.4 networks

In this paper, we propose a lexical routing metric to enable path-wise link quality-aware routing in Wireless Sensor Networks (WSNs). The realization of this routing metric is achieved by applying the indexing techniques of formal language processing in multi-metric route classification and cost evaluation for WSNs. In particular, the metric is motivated from the fact that IEEE 802.15.4 networks are formed on links with highly variable quality, and selection of poor quality links degrades the data delivery considerably. Although IEEE 802.15.4 supports link-level awareness through LQI, (a) it remains unbeknownst at the path level, and therefore, (b) its processing is link-local in scope. We propose LABILE, a composite routing metric, that is implemented through modifying RREQ and RREP structures of AODV to capture and convey two-state link information to destination which in turn is processed through an easy to compute routing lexicon and a corresponding lexical algorithm for path selection in case of availability of multiple paths. Multiple paths are represented in a path space through a proposed cost model that encompasses hop-count, weak links and a quantity weakness factor of each link, depending upon a thresholding mechanism which in turn declares a link to be either healthy (aka usable) or weak (aka unusable). Using the weakness factor, the success probability of data delivery over weak link turns out to be a fraction of success probability at healthy link. The mathematical model along with the experiments done for LABILE show that proposed composite metric and its parsing scheme together achieve link robustness consistently by evading the link failures, as the number of weak links is fairly reduced in lexical path as compared to the hop-count-based metric. Increased data delivery is obtained through preventing retransmissions as on failure-prone links.

Case-Based Reasoning Cost Estimation Model Using Two-Step Retrieval Method

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

Study on Cost Minimization of Auto Engine Product Warranty Chain

Aiming at how to optimize auto engine warranty cost, some hot issues such as what kind of factors auto engine warranty cost includes was studied deeply from the standpoint of reverse supply chain in this paper. First, the definition of auto engine warranty supply chain (AEWSC) was explained; then, under two-dimensional renewable free warranty policy, the components of AEWSC cost were analyzed for repairable engines. AEWSC cost covers not only traditional product warranty cost, but also potential cost resulting from re-failed engines; thirdly, an AEWSC Cost model was built based on cost analysis; finally, numeral analysis was developed through using the proposed cost model. The analysis results help engine manufacturer reduce AEWSC cost.