Showing papers on "Software as a service published in 2020"

STAR: SLA-aware Autonomic Management of Cloud Resources

Abstract: Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

Factors Influencing SMEs’ Adoption of Cloud Computing Services in Lebanon: An Empirical Analysis Using TOE and Contextual Theory

Abstract: An increasingly important advancement in information and communication technologies is cloud computing, and a remarkably increasing trend is its adoption by various organizations. The trend is attributed to the potential of this growing computing paradigm to improve the scalability, efficiency, and reliability of IT systems. Diffusion of cloud computing innovation is changing the way business information systems are developed, paid for, and maintained Alshamaila et al. 2013, Low et al. 2011, thus contributing to efficiency and better value for enterprises. This not only applies to large organizations, but also progressively more to small and medium-sized enterprises (SMEs). However, little is known about the factors most likely to be associated to the adoption behavior of cloud computing services among small and medium enterprises operating in Lebanon. This study deploys the technology–organization–environment (TOE) framework and the Contextual Theory to empirically examine the determinants of cloud computing service adoption in a developing country, namely Lebanon. A model is proposed, and data collected from 139 respondents working in SMEs in Lebanon and analyzed using confirmatory factor analysis and logistic regression in SPSS provide strong support for the model. Results indicate that technological (i.e., complexity and security) and organizational (i.e., top management support and prior IT experience) factors are positively related to the decision to adopt cloud computing services. Moreover, one of the areas of potential interest is the effect of country-specific, or contextual factors, among those who intend to adopt cloud computing. The analysis shows that context-specific factors (i.e. poor infrastructure and lack of government initiatives) are negatively related to the adoption decision. Implications and limitations are discussed, and recommendations for future research are proposed.

Mining Domain Knowledge on Service Goals from Textual Service Descriptions

Abstract: With the rapid development of service-oriented computing, a large number of software applications have been developed based on the services computing framework. It is well known that software engineering is a knowledge-intensive activity, and thus the effective management of service-related knowledge facilitates service-oriented software development. Although many methodologies have been proposed for service-oriented knowledge management, little attention has been paid to mining knowledge (especially domain-specific functionalities) from service resources. To address this issue, we propose an approach to mine domain knowledge on service goals (i.e., service functionalities) from textual descriptions of services. The approach consists of two components: service goal extraction from textual service descriptions based on linguistic analysis and domain service goal construction that merges semantically similar service goals within a domain. The effectiveness of the proposed approach is validated by a series of experiments conducted on a real-world dataset crawled from the ProgrammableWeb.

epcAware: A Game-based, Energy, Performance and Cost Efficient Resource Management Technique for Multi-access Edge Computing

Abstract: Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC) The MEC setup involves three parties, ie service-providers (IaaS), application-providers (SaaS), network-providers (NaaS); which might have different goals, therefore, making resource management difficult Unlike existing literature, we consider resource management with-respect-to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications' performance Our empirical evaluation, using Google's workload traces, suggests that our approach could reduce up to 1195% energy consumption, and ~1786% user costs with negligible loss in performance Moreover, IaaS can reduce up-to 2027% energy bills and NaaS can increase their costs-savings up-to 1852% as compared to other methods

AI-Driven Data Monetization: The other Face of Data in IoT-based Smart and Connected Health

Abstract: As the trajectory of the Internet of Things (IoT) moving at a rapid pace and with the rapid worldwide development and public embracement of wearable sensors, these days, most companies and organizations are awash in massive amounts of data. Determining how to profit from data deluge can give companies an edge in the market because data has the potential to add tremendous value to many aspects of a business. The market has already seen a level of monetization across vertical domains in the form of layering connected devices with a variety of Software-as-a-Service (SaaS) choices, such as subscription plans or smart device insights. Out of this arena is evolving a “machine economy” in which the ability to correctly monetize data rather than simply hoard it, will provide a significant advantage in a competitive digital environment. The recent advent of the technological advances in the fields of Big Data, Analytics, and Artificial Intelligence (AI) have opened new avenues of competition, where data is utilized strategically and treated as a continuously changing asset able to unleash new revenue opportunities for monetization. Such growth has made room for an onslaught of new tools, architectures, business models, platforms, and marketplaces that enable organizations to successfully monetize data. In fact, emerging business models are striving to alter the power balance between users and companies that harvest information. Start-ups and organizations are offering to sell user data to data analytics companies and other businesses. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by (1) utilizing it to make better business decisions or improve processes, (2) surrounding flagship services or products with data, or (3) selling information to current or new markets. This paper will address all important aspects of IoT data monetization with more focus on the healthcare industry and discuss the corresponding challenges, such as data management, scalability, regulations, interoperability, security, and privacy. In addition, it presents a holistic reference architecture for the healthcare data economy with an in-depth case study on detection and prediction of cardiac anomalies using Multi-Party Computation (MPC) and Privacy-Preserving Machine Learning (PPML) techniques.

Game-Theoretic Resource Pricing and Provisioning Strategies in Cloud Systems

Abstract: We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. For this scenario, we study the virtual machine provisioning and spot pricing strategies. We consider a two-stage provisioning scheme. In the first stage, the SaaS providers determine the optimal number of required flat and on demand instances. Then, in the second stage, the IaaS provider sells its unused capacity as spot instances for which the SaaS providers compete by submitting a bid. We study two different IaaS provider pricing strategies: the first assumes the IaaS provider sets a unique price; in the second, instead, the IaaS provider can set different prices for different customers. We model the resulting problem as a Stackelberg game. For each pricing scheme, we show the existence of the game equilibrium and provide the solution algorithms. Through numerical evaluation we compare the provisioning and spot price under the two different pricing strategies as function of the system parameters.

Enterprise Resource Planning: Past, Present, and Future

Abstract: The purpose of this article is to provide a broad overview of the history and development of ERPs and outline recent developments with the advent of digital innovations like cloud computing. The re...

Interference-aware SaaS User Allocation Game for Edge Computing

Abstract: Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor's perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users data rates. In this paper, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

Towards Process Improvement in DevOps: A Systematic Literature Review

Abstract: In recent years, the software release cost has been reduced dramatically due to the alteration from traditional shrink-wrapped software to software as a service. Organizations that can deliver their services continuously and with a high frequency have a higher ability to compete in the market. As a response to this, a substantial number of software companies acquired DevOps to establish a culture of effective communication and collaboration between development and operation teams and in order to enhance the production release frequency as well as to maintain the product quality. However, the DevOps environment requires a platform that aid in evaluating the performance of existing processes and provide improvement recommendations. On top of that, organizations can only achieve the perceived benefits of DevOps if their processes are mature and continuously measured. The objective of this research is to investigate the process improvement contributions made by researchers in the DevOps field. For this purpose, we performed a systematic literature review that resulted in several maturity models and best practices. Our ultimate aim is to develop a DevOps maturity model that can appraise and improve the processes in the DevOps environment.

Statistical Model Checking-Based Evaluation and Optimization for Cloud Workflow Resource Allocation

Abstract: Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations; ii) make quantitative and qualitative comparisons among resource allocation strategies; iii) enable the tuning of parameters to improve the overall QoS; and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.

Detecting SQL Injection Attacks in Cloud SaaS using Machine Learning

Abstract: Software as a Service (SaaS) has been adopted in a fast pace for applications and services to run on software cloud platform However, the success of SaaS in cloud computing cannot obscure the security challenges faced by the web applications deployed on cloud SaaS Like other web-based systems, cloud applications are prone to most of the common web attacks The SQL injection attack is one of the most potential threat to a SaaS application This may result in loss of sensitive and important data (eg, financial, personal) Through this kind of attacks, the attacker can steal critical and confidential information to a business or an organization leading to high impact on tangible (eg, data) and intangible (eg, reputation) assets The purpose of this research is to investigate the potential of using machine learning techniques for SQL injection detection on the application level The algorithms to be tested are classifiers trained on different malicious and benign payloads They take a payload as input and decide whether the input contains a malicious code or not The results show that these algorithms can distinguish normal payloads from malicious payloads with a detection rate higher than 98% The paper also compares the performance of different machine learning models in detecting SQL injection attacks

Performance Analysis of an Edge Computing SaaS System for Mobile Users

Abstract: Nowadays, the ever increasing demand of mobile computing applications has triggered the shift from the centralized mobile Cloud Computing toward mobile Edge Computing (MEC). The main feature of MEC is to move computing and storage to the network edges (e.g., radio access point/base stations), enabling resource-limited mobile devices to support computation-intensive and latency-critical applications. This paper deals with the performance evaluation of a MEC system providing computational capabilities to users within a limited area, according to the software as a service (SaaS) paradigm. In particular, a Markov multi-server queuing system model with requests reneging is proposed in order to accomplish the performance evaluation of the MEC system and derive the minimum number of processors to be allocated in order to fulfill specific service requirements in terms of resulting requests dropping probability. Finally, the pertinence of the proposed Markov queueing model is validated by comparing the obtained analytical predictions with numerical results derived by resorting to extensive computer simulations carried out under the assumption of realistic operating conditions.

Identification and Prioritization of Cloud Based Global Software Development Best Practices

Abstract: The cloud based global software development (CGSD) is the most widely adopted development paradigm in software industry. The CGSD offers significant economic and strategic benefits; besides, various complexities are faced by the practitioners while deploying CGSD. Hence, this study aims to identify and prioritize the best practices that are important for the success and progression of CGSD paradigm. Using the systematic literature review a total of 30 best practices were identified and were further verified with industry experts using questionnaire survey study. The identified best practices were further prioritize using fuzzy-AHP approach. The fuzzy-AHP is novel in this domain as it successfully applied in other engineering domain to address the multicriteria decision making problems. The findings of this study will provide a prioritization-based taxonomy of the investigated best practices which assists the academic researchers and industry experts to develop and revise the strategies of CGSD.

ProFIPy: Programmable Software Fault Injection as-a-Service

Abstract: In this paper, we present a new fault injection tool (ProFIPy) for Python software. The tool is designed to be programmable, in order to enable users to specify their software fault model, using a domain-specific language (DSL) for fault injection. Moreover, to achieve better usability, ProFIPy is provided as software-as-a-service and supports the user through the configuration of the faultload and workload, failure data analysis, and full automation of the experiments using container- based virtualization and parallelization.

SaaS - Microservices-Based Scalable Smart Contract Architecture

Abstract: Existing blockchain smart contract platforms are designed as monolithic architectures Even though there are multiple smart contracts with fully independent business logic, they run on a single monolithic container This dependence on a monolithic container can be a performance bottleneck during the processing of a large number of transactions To address this challenge, microservice-based architecture is adopted in the blockchain smart contracts by introducing a novel architecture to run independently on separate microservices The new smart contract architecture is built on top of Mystiko blockchain, a functional programming and actor-based “Aplos” concurrent smart contract platform Aplos is identified as a “Smart Actor” platform since it is built using Actor-based concurrency handling Based on the philosophy of microservices, the Aplos Smart Actor platform on Mystiko blockchain is redesigned This architecture is introduced as “SaaS - Smart actors as a service” With SaaS, different Aplos smart actors in the blockchain are deployed as separate independent services (eg docker containers) instead of a single monolith service This ensures different smart actors can execute transactions independently An additional benefits to SaaS is that the architecture increases the scalability by guaranteeing concurrent execution of transactions, producing high transaction throughput on the blockchain

Scheduling real‐time bag‐of‐tasks applications with approximate computations in SaaS clouds

Abstract: Summary Software as a Service (SaaS) cloud computing has emerged as an attractive platform to tackle various problems of the traditional software distribution model, such as the requirement to acquire and maintain expensive hardware and software infrastructure. SaaS, however, involves many challenges, mainly due to the heterogeneity and multitenancy of the underlying host environment, as well as the nature of the applications executed on such platforms. Applications are usually bags-of-tasks, consisting of independent component tasks that can be executed in any order, featuring different degrees of variability in their computational demands. Furthermore, according to the service level agreement between the cloud provider and the end-users, the execution of such applications must typically complete within a deadline, providing results of acceptable quality. Consequently, one of the most important aspects of SaaS cloud computing is the effective scheduling of multiple parallel applications, avoiding any service level agreement violations. Towards this direction, our contribution in this paper is twofold: (1) We enhance some of the most commonly used scheduling algorithms for bag-of-tasks applications, by utilizing approximate computations, and (2) we investigate the impact of different levels of variability in the computational demands of the applications on the performance of the examined heuristics.

Tapis API Development with Python: Best Practices In Scientific REST API Implementation: Experience implementing a distributed Stream API

Abstract: In the last decade, the rise of hosted Software-as-a-Service (SaaS) application programming interfaces (APIs) across both academia and industry has exploded, and simultaneously, microservice architectures have replaced monolithic application platforms for the flexibility and maintainability they offer. These SaaS APIs rely on small, independent and reusable microservices that can be assembled relatively easily into more complex applications. As a result, developers can focus on their own unique functionality and surround it with fully functional, distributed processes developed by other specialists, which they access through APIs. The Tapis framework, a NSF funded project, provides SaaS APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. In this paper, we describe the best practices followed to create Tapis APIs using Python and the Stream API as an example implementation illustrating authorization and authentication with the Tapis Security Kernel, Tenants and Tokens APIs, leveraging OpenAPI v3 specification for the API definitions and docker containerization. Finally, we discuss our deployment strategy with Kubernetes, which is an emerging orchestration technology and the early adopter use cases of the Streams API service.

Commitment and Replacement of Existing SaaS-Delivered Applications: A Mixed-Methods Investigation

Abstract: As the highest level of cloud computing delivery model, Software-as-a-Service (SaaS) has gained considerable popularity in the industry as a new way of deploying IT solutions, due to its low cost and high elasticity. However, the new business model associated with SaaS highlights the importance for SaaS vendors to understand how to retain customers in a hyper-competitive market. In particular, increasing customer retention and preventing customers from replacing the adopted SaaS applications has become a crucial task for all SaaS vendors. In this study, using a mixed-methods approach, and drawing on the cognitive–affective–conative– action (CACA) framework, we investigate the IS replacement phenomenon in the context of SaaS-delivered applications. Our qualitative study allows us to develop an IS-centric view of customer commitment by differentiating between organizations’ commitment to the SaaS application and to the cloud computing technology in general, while the subsequent quantitative study validates the difference between the two types of commitment and helps understand how they together influence organizations’ intentions to replace a SaaS application. Our results generate important theoretical implications for research on IS replacement and clarifies the concept of customer commitment. We also offer practical guidelines to SaaS vendors on how to retain customers so as to survive/thrive in this competitive market.

A behavioral intention model for SaaS-based collaboration services in higher education

Abstract: Despite numerous potential benefits of cloud computing usage, there are still some users reluctant to adopt this technology. This study aims to investigate the factors that influence student adoption of cloud computing in higher education settings and to generate a set of decision rules to guide through a series of critical decisions needed in this adoption process. Accordingly, a two-stage Structural Equation Modelling (SEM)-Classification and Regression Trees (CART) methodology is applied in order to test the overall research model and related hypotheses as well as to generate decision rules to predict behavioural intention towards adoption. Using survey questionnaire method, a total of 418 valid questionnaires are collected from students of top-ranked Malaysian universities. The results show that task-technology fit, performance expectancy, effort expectancy, social influence, self-efficacy, collaboration technology experience, peer and superior influence and familiarity with group members are significant predictors of intention to adopt cloud computing. The findings of this study can serve as a guideline for the ministry of education, university administrators, and cloud service providers to manage the successful adoption of cloud computing in the education sector.

Building Explanations for Fuzzy Decision Trees with the ExpliClas Software

Abstract: Fairness, Accountability, Transparency and Explainability have become strong requirements in most practical applications of Artificial Intelligence (AI). Fuzzy sets and systems are recognized world-wide because of their outstanding contribution to model AI systems with a good interpretability-accuracy trade-off. Accordingly, fuzzy sets and systems are at the core of the so-called Explainable AI. ExpliClas is a software as a service which paves the way for interpretable and self-explainable intelligent systems. Namely, this software provides users with both graphical visualizations and textual explanations associated with intelligent classifiers automatically learned from data. This paper presents the new functionality of ExpliClas regarding the generation, evaluation and explanation of fuzzy decision trees along with fuzzy inference-grams. This new functionality is validated with two well-known classification datasets (i.e., Wine and Pima), but also with a real-world beer-style classifier.

Cloud Computing: Analysis of Top 5 CSPs in SaaS, PaaS and IaaS Platforms

Abstract: The Cloud computing refers to manipulating, configuring and accessing the applications as utilities over the internet It involves online data computation, storation, infrastructure and application, and hence it is highly essential to make a smart decision, when and how computing, storage and network resources be distributed and allocated to users to utilize, manage and consume them In this paper first architecture of cloud computing, characteristics of service models and services provided by CSPs are discussed, than after analyzed growth rate and market share of top five CSPs in cloud service models, ie Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS)

Adoption, diffusion and consumer behavior in technopreneurship

Abstract: The purpose of this paper is to study the adoption and diffusion of technology including SAAS software and cloud computing for facilitating knowledge management (KM) in product innovation based on understanding of consumer behavior. Technopreneurship can drive sustainable product innovation by studying the patterns of consumer behavior. Sharing of consumer intelligence on cloud using SAAS is being used by several companies to drive innovation such as call centers in South Asia. However, there is no understanding role of knowledge management for understanding consumer behavior for product innovation.,The methodology uses case method of action research technique coupled with grounded theory development. Further, the study uses interpretive structural modelling (ISM) technique for interpreting the results for understanding consumer behavior patterns for enabling product innovation.,The findings suggest that enhancement of creative design based on consumer's study can lead to sustainable product development. The findings revealed that consumer behavior patterns embedded in the firm's intelligence captured in KM portal including customers' preferences and choices that can be developed into products. Knowledge management facilitated flexible manufacturing process, optimized capital expenditure using agility principles as per the study. Techniques and processes such as reactive scaling top down and bottom up and applying flexible APIs (Application Programming Interface) allowed the efficient automation of infrastructure orchestration and resource allocation. The involvement of vendors’ knowledge base facilitated creation of market ready product offers leading to sustainability.,The implications include the adoption of inter-disciplinary and inter country understanding of knowledge management application for understanding consumer behavior to lead to sustainable product development.,The scope and scale of technology entrepreneurship include the application of knowledge management for consumer behavioral studies that have huge contributions to make product development sustainable using greener planet, purpose and product (3P model).

A game-based resource pricing and allocation mechanism for profit maximization in cloud computing

Abstract: In cloud computing environment, Software as a Service (SaaS) providers offer diverse software services to customers and commonly host their applications and data on the infrastructures supplied by Infrastructure as a Service (IaaS) providers. From the perspective of economics, the basic challenges for both SaaS and IaaS providers are to design resource pricing and allocation policies to maximize their own final revenue. However, IaaS providers seek an optimal price policy of virtual machines to generate more revenue, while SaaS providers want to minimize the cost of using infrastructure resources, and comply with service-level agreement contracts with users at the same time. In this situation, there exists conflict in maximizing revenue of both IaaS and SaaS providers simultaneously. In this paper, we model this revenue maximization problem as the Stackelberg game and analyze the existence and uniqueness of the game equilibrium. Moreover, considering the impact of resource price on users’ willing to access service, we propose a dynamic pricing mechanism to maximize the revenue of both SaaS and IaaS providers. The simulation results demonstrate that, compared to fixed pricing and auction-based pricing mechanisms, the proposed mechanism is superior in the revenue maximization and resource utilization.

A Survey on Cloud Computing Security Issues and Cryptographic Techniques

Abstract: Cloud computing is an Internet-based computing model, having various resources used by distinct users in a concurrent manner. Apart from all of its advantages, it faces a major setback due to various data security issues. To overcome these issues, various security mechanisms have been proposed, such as cryptography and authentication. Cryptography can be used to provide data integrity, authorization for data manipulation, and also making the data unreadable to an interceptor through encryption. There are various classifications of models in cloud computing. The service models are classified as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). There are several deployment models mainly distinguished by ownership which consists of public cloud, private cloud, and hybrid cloud. This survey mainly focuses on security issues in cloud service models and cloud deployment models along with various cryptographic mechanisms of data protection, such as symmetric key cryptography, asymmetric key cryptography, and their encryption algorithms.

Impact of software as a service (SaaS) on software acquisition process

Abstract: Organizations worldwide are adopting software as a service (SaaS) applications, where they pay a subscription fee to gain access rather than buying the software. The extant models on software acquisition processes, several of which are based on organizational buying behavior, do not sufficiently explain how SaaS application acquisition decisions are made. This study aims to investigate the acquisition process organizations follow for SaaS software, the changes to the roles of the Chief Information Officer (CIO) and the business user and also looks at the impact of SaaS on the proliferation of unauthorized software systems.,The authors used exploratory research using the grounded theory approach based on 18 in-depth interviews conducted with respondents who have studied with enterprise software delivered on-premise and as SaaS in different roles such as sales, consulting, CIO, information technology (IT) management and product development.,The authors identified a need to classify the SaaS software and developed a framework that uses software specificity and its strategic importance to the organization to classify SaaS applications. The aforementioned framework is used to explain how software evaluation processes have changed for different kinds of SaaS applications. The authors also found that the CIO’s and the business users’ have changed substantially in SaaS application evaluations and found evidence to show that shadow IT will be restricted to some classes of SaaS applications.,By focusing on the changes to the roles and responsibilities of the members of the buying center, this paper provides unique insights into how the acquisition process of SaaS is different from the extant models used to explain enterprise software acquisitions. An understanding of how information search is conducted by the business users will help software vendors to target business users better.