A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud

Cloud computing is a latest new computing paradigm where applications, data and IT services are provided over the Internet. Cloud computing has become a main medium for Software as a Service (SaaS) providers to host their SaaS as it can provide the scalability a SaaS requires. The challenges in the composite SaaS placement process rely on several factors including the large size of the Cloud network, SaaS competing resource requirements, SaaS interactions between its components and SaaS interactions with its data components. However, existing applications’ placement methods in data centres are not concerned with the placement of the component’s data. In addition, a Cloud network is much larger than data center networks that have been discussed in existing studies. This paper proposes a penalty-based genetic algorithm (GA) to the composite SaaS placement problem in the Cloud. We believe this is the first attempt to the SaaS placement with its data in Cloud provider’s servers. Experimental results demonstrate the feasibility and the scalability of the GA.

A cooperative coevolutionary algorithm for the composite SaaS Placement Problem in the Cloud

Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. One of the challenges in hosting the SaaS is the placement process where the placement has to consider SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a classical genetic algorithm (GA) approach. This paper proposes a cooperative coevolutionary algorithm (CCEA) approach. The CCEA has been implemented and evaluated and the result has shown that the CCEA has produced higher quality solutions compared to the GA.

Composite SaaS placement and resource optimization in Cloud computing using evolutionary algorithms

Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Software as a service (SaaS) for small and medium enterprises (SMEs) : the role of intermediaries

Software as a Service (SaaS) is anticipated to provide significant benefits to small and medium enterprises (SMEs) due to ease of access to high-end applications, 7*24 availability, utility pricing, etc. However, underlying SaaS is the assumption that SMEs will directly interact with the SaaS vendor and use a self-service model. In practice, we see the rise of SaaS intermediaries who support SMEs with using SaaS. This paper reports on an empirical study of the role of intermediaries in terms of how they support SMEs in sourcing and leveraging SaaS for their business. The knowledge contributions of this paper are: (1) the identification and description of the role of SaaS intermediaries and (2) the specification of different roles of SaaS intermediaries, in particular a more basic role with technology orientation and operational alignment perspective and (3) a more added value role with customer orientation and strategic alignment perspective.

Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

Process virtualization theory (PVT) and institutional theory (INT) to explain SAAS adoption

Software as a service (SaaS) is a service model in which the applications are accessible from various client devices through internet. Several studies report possible factors driving the adoption of SaaS but none have considered the perception of the SaaS features and the pressures existing in the organization’s environment. We propose an integrated research model that combines the process virtualization theory (PVT) and the institutional theory (INT). PVT seeks to explain whether SaaS processes are suitable for migration into virtual environments via an information technology-based mechanism. INT seeks to explain the effects of the institutionalized environment on the structure and actions of the organization. The research makes three contributions. First, it addresses a gap in the SaaS adoption literature by studying the internal perception of the technical features of SaaS and external coercive, normative, and mimetic pressures faced by an organization. Second, it empirically tests many of the propositions of PVT and INT in the SaaS context, thereby helping to determine how the theory operates in practice. Third, the integration of PVT and INT contributes to the information system (IS) discipline, deepening the applicability and strengths of these theories.

Projecto de criação de metodologia de análise da Usabilidade para selecção de plataforma SaaS de Gestão de Projectos online CATCH, Global Marketing

O aparecimento de soluções de software baseadas na Cloud vieram democratizar o acesso a aplicações de suporte à actividade empresarial, permitindo a micro e pequenas empresas aceder a ferramentas que outrora apenas as grandes empresas poderiam financiar, dada a introdução de novas formas de pagamento mensais com base em contratos flexíveis, acesso via internet e ausência de instalação de hardware específico ou compra de licenças por utilizador – a verdadeira utilização de software como um serviço, vulgo SaaS (Software as a Service). As aplicações de tipo SaaS aportam inúmeros benefícios para as empresas e mesmo vantagens competitivas importantes, estando disponíveis soluções em diversas áreas, nomeadamente para a Gestão de Projectos, como ferramentas de CRM (Customer Relationship Management) e CMS (Content Management System), entre outros. Assim, as empresas de Marketing e Comunicação, caso da empresa em que se centra este Projecto, têm hoje em dia acesso a um conjunto de aplicações SaaS, que pelo seu custo acessível e fácil acesso online, permitem às empresas mais pequenas serem rapidamente tão competitivas quanto as maiores, por norma com processos mais pesados e tradicionais. Adicionalmente, assistimos também ao fenómeno da consumerização das TI, em que os consumidores passam a querer ter o mesmo tipo de User Experience (UX) de que usufruem na utilização de aplicações fora do seu trabalho, aplicadas à vida empresarial. Este Projecto argumenta que a Usabilidade deve ser um dos elementos chave para a selecção correcta de uma aplicação online de Gestão de Projectos (do tipo SaaS), algo que deveria ser facilitado pela aplicação de uma metodologia de teste da Usabilidade, disponível numa plataforma online de acesso livre. A metodologia deverá ser eficaz e passível de ser utilizada por colaboradores de uma micro ou pequena empresa, apoiando o seu processo decisório de investimento, sendo eles especialistas ou não na matéria. A metodologia proposta neste projecto exploratório pressupõe uma complementaridade entre a avaliação Heurística de Usabilidade pelo método de Nielsen e o Método de Purdue - Purdue Usability Testing Questionnaire (PUTQ).

Growth hacking model for B2B saas startups

The following project introduces a model of Growth Hacking strategies for business-tobusiness Software-as-a-Service startups that was developed in collaboration with and applied to a Portuguese startup called Liquid. The work addresses digital marketing channels such as content marketing, email marketing, social marketing and selling. Further, the company’s product, pricing strategy, partnerships and website communication are examined. Applying best case practices, competitor benchmarks and interview insights from numerous industry influencers and experts, areas for improvement are deduced and procedures for each of those channels recommended.

Evaluación de herramientas en entorno SAAS, para optimizar la gestión de las cooperativas pequeñas en el Ecuador

Los avances e innovaciones tecnológicas en la actualidad han provocado significativos cambios sociales y empresariales que por su rapidez de actualización, obligan a las entidades del mundo entero a modernizar su infraestructura tecnológica, para evitar perder su presencia en el mercado competitivo en el que se desenvuelven, sin dejar de lado que los clientes también exigen estos cambios como parte de un servicio de mejor calidad. El principal enfoque de las organizaciones es el crecimiento constante de sus ingresos, respaldados en la eficiencia de sus diferentes procesos productivos, de manera tal que la revisión constante de estos, es vital para controlar los niveles de competitividad que requiere mantener la organización, sin descartar que las decisiones de negocios serán más eficientes si se cuenta con la información necesaria el momento oportuno. Esto se consigue principalmente con el soporte de los diferentes sistemas e infraestructuras tecnológicas que permiten acceder a indicadores, reportes o resultados en tiempo real. El contar con tecnología de punta es uno de los valores agregados más importantes de las grandes entidades financieras en el país y el mundo, y con esta necesidad latente los diferentes proveedores de software han diversificado sus productos y soluciones, por lo que han propuesto el uso de sus sistemas vía SAAS -software como servicio-, es decir acceder a la tecnología según el tamaño de la organización, mediante una conexión a internet y pago por el servicio requerido, sin desperdiciar recursos económicos, humanos o tecnológicos. Teniendo en cuenta lo mencionado, el acceso a esta nueva tecnología posibilitará a las reguladas entidades del sector financiero, puntualmente cooperativas, mejorar sus niveles de competitividad en el mercado, al contar con soluciones de vanguardia, acceder a tecnología de punta, diversificar su cartera de productos y gestionar la información relevante para la toma oportuna de decisiones, en función de los objetivos y estrategias de la entidad. Motivo por lo cual esta tesis, tiene la finalidad de proponer el uso de las herramientas tecnológicas de Internet denominadas SAAS, para aprovechar las ventajas y oportunidades que estas ofrecen y así conseguir una mejor administración y dirección de las cooperativas reguladas –SBS- y generar importantes ventajas competitivas en el mercado local.

A design of evaluation method for SaaS in cloud computing

Purpose: This paper aims to design an evaluation method that enables an organization to assess its current IT landscape and provide readiness assessment prior to Software as a Service (SaaS) adoption. Design/methodology/approach: The research employs a mixed of quantitative and qualitative approaches for conducting an IT application assessment. Quantitative data such as end user’s feedback on the IT applications contribute to the technical impact on efficiency and productivity. Qualitative data such as business domain, business services and IT application cost drivers are used to determine the business value of the IT applications in an organization. Findings: The assessment of IT applications leads to decisions on suitability of each IT application that can be migrated to cloud environment. Research limitations/implications: The evaluation of how a particular IT application impacts on a business service is done based on the logical interpretation. Data mining method is suggested in order to derive the patterns of the IT application capabilities. Practical implications: This method has been applied in a local council in UK. This helps the council to decide the future status of the IT applications for cost saving purpose.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.