SaaS Applications Research Articles

An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

Big data storage and management in SaaS applications

As an important service model for advanced computing, SaaS uses a defined protocol that manages services and applications. The popularity of advanced computing has reached a level that has led to the generation of large data sets, which is also called Big data. Big data is evolving with great velocity, large volumes, and great diversity. Such an amplification of data has brought into question the existing database tools in terms of their capabilities. Previously, storage and processing of data were simple tasks; however, it is now one of the biggest challenges in the industry. Experts are paying close attention to big data. Designing a system capable of storing and analyzing such data in order to extract meaningful information for decision-making is a priority. The Apache Hadoop, Spark, and NoSQL databases are some of the core technologies that are being used to solve these issues. This paper contributes to the solutions to the issues of big data storage and processing. It presents an analysis of the current technologies in the industry that could be useful in this context. Efforts have been focused on implementing a novel Trinity model, which is built using the lambda architecture with the following technologies: Hadoop, Spark, Kafka, and MongoDB.

An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications

An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications

Performance analysis of multi-core VMs hosting cloud SaaS applications

Today's data centers are designed to scale up to respond to the offered workload in a rapid, efficient, and effective manner, and at the same time, they must satisfy the Service Level Agreement (SLA) requirements. This opens up many interesting and challenging research issues and opportunities. The Software-as-a-Service (SaaS) is the most popular cloud service model being used these days, in which multi-core VMs are allocated efficiently to meet the offered workload, and in a way to avoid any violations to the agreed SLA. This entails the need to model SaaS services to predict the performance and overall system cost, and to estimate the required number of VM resources and their respective multi-core capacity prior to the actual deployment. To this end, we present in this paper a queuing mathematical model to study and analyze the performance of multi-core VMs hosting cloud SaaS applications. Our analytical model estimates under any offered workload the number of required multi-core VM instances needed to satisfy the Quality of Service (QoS) parameters. Our mathematical model is validated using DES (Discrete Event Simulator) simulations. Results obtained from our analysis as well as simulation models show that the proposed model is powerful and able to correctly and effectively predict the system performance and cost, and also to determine the number of VMs cores needed for SaaS services in order to achieve QoS targets under different workload conditions.

Modelling, validating, and ranking of secure service compositions

SummaryIn the world of large‐scale applications, software as a service (SaaS) in general and use of microservices, in particular, is bringing service‐oriented architectures to a new level: Systems in general and systems that interact with human users (eg, sociotechnical systems) in particular are built by composing microservices that are developed independently and operated by different parties. At the same time, SaaS applications are used more and more widely by enterprises as well as public services for providing critical services, including those processing security or privacy of relevant data. Therefore, providing secure and reliable service compositions is increasingly needed to ensure the success of SaaS solutions. Building such service compositions securely is still an unsolved problem.In this paper, we present a framework for modelling, validating, and ranking secure service compositions that integrate both automated services as well as services that interact with humans. As a unique feature, our approach for ranking services integrates validated properties (eg, based on the result of formally analysing the source code of a service implementation) as well as contractual properties that are part of the service level agreement and, thus, not necessarily ensured on a technical level.

Camflow: Managed Data-Sharing for Cloud Services

A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government provides many related services for its citizens on a common platform. Similar considerations apply to the end-users of applications. But in particular, the incorporation of cloud services within `Internet of Things' architectures is driving the requirements for both protection and cross-application data sharing. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows; a crucial issue once data has left its owner's control by cloud-hosted applications and within cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners' dataflow policy with regard to protection and sharing, as well as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency, giving configurable system-wide visibility over data flows. [...]

Self-configuring cloud application mashup with goals and capabilities

Cloud mashup is a technique for the seamless composition of SaaS applications from several sources into a single integrated solution. This paper presents a general approach for automatically composing applications and services deployed over the Cloud. The proposed approach implies to encapsulate distributed processes into smart and autonomic entities, namely cloud capabilities. Despite the lack of a central mashup server, these processes are able to autonomously organize in order to establish different ways to address the desired result. The approach uses a couple of languages for describing respectively the mashup logic in terms of goals and the available functionalities in terms of capabilities. The explicit decoupling between user’s goals and capabilities provides the system the freedom to generate the orchestration plan at run-time, according to the contextual state. An industrial case study, conducted in for a scientific project, has provided the conditions for evaluating the running example of a B2B business process for a fashion enterprise.

Enhancing Elasticity of SaaS Applications using Queuing Theory

Elasticity is one of key features of cloud computing. Elasticity allows Software as a Service (SaaS) applications' provider to reduce cost of running applications. In large SaaS applications that are developed using service-oriented architecture model, each service is deployed in a separated virtual machine and may use one or more services to complete its task. Although, scaling service independently from its required services propagates scaling problem to other services, most of current elasticity approaches do not consider functional dependencies between services, which increases the probability of violating service level agreement. In this paper, architecture of SaaS application is modeled as multi-class M/M/m processor sharing queuing model with deadline to take into account functional dependencies between services during estimating required scaling resources. Experimental results show effectiveness of the proposed model in estimating required resources during scaling virtual resources.

Software as a Service (SAAS), Future of Information Technology and Business

Software as a service, or 'SaaS', is a software application delivery model by which an enterprise vendor develops a web-based software application, and then hosts and operates that application over the Internet for use by its customers. Customers do not need to buy software licenses or additional infrastructure equipment, and typically only pay monthly fees for using the software. SaaS applications provide customers with centralized, network-based access to data with much less overhead as compared to using a locally-installed application.

Minimum Dependencies Energy-Efficient Scheduling in Data Centers

This work presents an on-line, energy- and communication-aware scheduling strategy for SaaS applications in data centers. The applications are composed of various services and represented as workflows. Each workflow consists of tasks related to each other by precedence constraints and represented by Directed Acyclic Graphs (DAGs). The proposed scheduling strategy combines advantages of state-of-the-art workflow scheduling strategies with energy-aware independent task scheduling approaches. The process of scheduling consists of two phases. In the first phase, virtual deadlines of individual tasks are set in the central scheduler. These deadlines are determined using a novel strategy that favors tasks which are less dependent on other tasks. During the second phase, tasks are dynamically assigned to computing servers based on the current load of network links and servers in a data center. The proposed approach, called Minimum Dependencies Energy-efficient DAG (MinD+ED) scheduling, has been implemented in the GreenCloud simulator. It outperforms other approaches in terms of energy efficiency, while keeping a satisfiable level of tardiness.

An Efficient Approach based on Genetic Algorithm for multi-tenant Resource Allocation in SaaS Applications

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

A Self-Provisioning Mechanism in OpenStack for IoT Devices

The aim of this paper is to introduce a plug-and-play mechanism for an Internet of Things (IoT) device to instantiate a Software as a Service (SaaS) application in a private cloud, built up with OpenStack. The SaaS application is the digital avatar of a physical object connected to Internet. As a proof of concept, a Vending Machine is retrofitted and connected to Internet with and Arduino Open Hardware device. Once the self-configuration mechanism is completed, it is possible to order a product from a mobile communication device.

A Proposed Software as a Service (SaaS) Toolkit for Cloud Multi- Tenancy

Cloud computing is a general term for anything that involves delivering hosted services over the Internet. It is a construct that allows you to access applications that actually reside at locations other than your computer or other Internet-connected devices. Software as a Service (SaaS) is a software delivery model in which software resources are accessed remotely by users over the internet. Enterprises prefer using SaaS because of its low cost. SaaS requires sharing of application servers among multiple tenants for low operational costs. Several SaaS Toolkits are available and used to develop cloud SaaS applications. However, most of these Toolkits are very complex. This paper aims to build a new simple and easy SaaS Toolkit to develop SaaS applications and to avoid the difficulties and complexity in the current cloud SaaS Toolkits. The proposed Toolkit is based on java virtual machine and the popular web programming languages HTML and PHP. To evaluate the proposed Toolkit an empirical study has been conducted. The result of the empirical study revealed that the proposed Toolkit outperforms the current Toolkits in terms of complexity, understandability and learnability.

A partition model and strategy based on the Stoer–Wagner algorithm for SaaS multi-tenant data

Partition technology is the key step to realize the extensional architecture in the cloud and support the data placement on multiple nodes. This paper proposes a multi-tenant data partition model and algorithm for SaaS (Software as a Service) application. It solves the problem that data partitions would produce lots of distributed transactions caused by the existing cloud data management. The management is unconscious of SaaS tenants during the transformation from a single node to multiple nodes in the cloud to obtain the dynamic extension of the system’s scale. With the increase of tenants and data, the single node becomes the bottleneck of the whole system. Fortunately, the scale of the whole system can be expanded by data partition. This paper puts forward a multi-tenant data partition model with three-layer structure: Tenant layer, Relevance, Group layer and Tenant Partition layer. Furthermore, we propose the concepts of Relevance, Relevance Value and Relevance Matrix. The customized tables for one tenant accessed by the same transactions can form a minimum high-relevance granularity based on the Relevance Group algorithm. Then we construct an abstracted graph, where group is the basic unit and transaction accessing is weight. Through the Stoer–Wagner algorithm, the multi-tenant partition with group as granularity is obtained. The partition algorithm proposed in this paper enables the greatest reduction of distributed transactions between partitions while realizing the dynamic extension on multiple nodes for multi-tenant data based on shared storage. Experiments show that the number of distributed transactions is reduced dramatically compared with other data partition techniques. We also prove that the SaaS applications run at high efficiency.

Towards Multivariable Architecture for SaaS Multi-tenant Applications

A multi-tenant Software as a Service (SaaS) application delivers customized run-time application to each tenant using a single application. Multi-tenancy offers several advantages including quick application deployment, reduced application maintenance, effective version control, and much more. However, handling large datacenter resources and providing better Quality of Service (QoS) is a major challenge for SaaS providers. SaaS application have unstable load based on tenant user’s demand. In a typical multitenant application security, reliability and energy efficiency play an important role. However, these aspects are largely ignored while focusing on the performance of the application. In this paper we propose a novel application’s component based multivariable architecture for multi-tenant Software as a Service (SaaS) application. Instances of application’s components are created at component level and dynamically scaled based on the component’s load considering security, reliability and energy based runtime factors applying multi-criteria Zionts–Wallenius optimization method. Our evaluation and discussions show that the proposed multi-instance Towards Multivariable Architecture (TMA) offers enhanced application load distribution and at the same time offers improved application response time and conserves energy in datacenter.

SafeProtect: Controlled Data Sharing With User-Defined Policies in Cloud-Based Collaborative Environment

There are many cloud-based applications consumed by users, which encourage data sharing with not only peers, but also new friends and collaborators. Data are increasingly being stored outside the confines of the data owner’s machine with little knowledge to the data owner, how and where the data are being stored and used. Hence, there is a strong need for the data owner to have a stronger control over their data, similar to the level of control they possess when the data are stored on their own machine. For instance, when a data owner shares a secret file with a friend, he cannot guarantee what his friend will do with the data. In this paper, we attempt to address this problem by monitoring and preventing unauthorized operations by the data consumer. We present a solution called SafeProtect, which bundles the data owner’s data and policy, based on XACML, in an object. SafeProtect enforces the policies set out by the data owner by communicating with the SaaS applications to disable certain commands and/or run a background process monitor for auditability/accountability purposes. We define a protocol that will enable secure data sharing in the cloud and leverage the use of the trusted extension device for authentication purposes.

SaaS-based enterprise application integration approach and case study

Software-as-a-Service (SaaS) has been well studied, and it is being adopted at a very fast pace. Enterprise application integration (EAI) is the key factor in many enterprises. Traditionally, SOA technique can be applied to EAI, especially the application of Web Service. In this paper, we propose that SaaS can also be applied to the field of EAI. Based on this, we propose the SaaS-based EAI approach which rebuilds one analog of legacy applications into SaaS architecture makes the rest applications be configured, and further solves the EAI problem. Besides, we also update the traditional SaaS maturity model, discuss relationships between SOA and SaaS in EAI, and explain how to further integrate SaaS applications by SOA. We implement an Eclipse plug-in which can introduce SaaS capabilities into traditional Web application automatically. Finally, the proposed approach is proved to be effective through a case study.

A scalable component model for multi-tenant SaaS application

A multi-tenant software as a service SaaS application provides a customised run-time application for each tenant using a single application. Multi-tenancy offers various advantages including reduced software development time, lower maintenance cost, effective version control and much more. However, managing large scale resources and providing superior user experience using the underlying resources is a key challenge in the management of SaaS application. Moreover, SaaS applications have fluctuating load based on tenant's users demand. Scalability of application in cloud is one of the foremost requirements in designing and implementing SaaS-based applications. In this paper, we propose an application's component-based scalable component model SCM, instances of application are created at component level and dynamically scaled based on the component's load considering various factors including tenant users attributes. Our evaluation and discussions show that the proposed multi-instance SCM offers enhanced application load distribution and offers improved application response time and effective utilisation of component instances.

Revisiting Asymmetric Classifier Fuzzy Keyword Search with Performance Isolated Intertenant Storage Clusters in Public Cloud

Background/Objectives: Virtual Clusters enabled users across globe to be able to access virtual machines, transact and deploy applications. Intertenant cloud customers demand availability and performance isolation from the cloud. Methods/Statistical Analysis: Though fair allocation of resources guarantees performance isolation of intra-tenants in SaaS applications, it is still a challenging task in intertenant IaaS cloud. Findings: Existing Universal keyword classifier is revisited to suggest a new scheme named Refined Asymmetric Classifier Fuzzy Keyword Search. The Classifier Search server utilizes universal keyword classifier for the search keyword request to greatly reduce the searching time by learning the search path pattern for all the keywords in the Fuzzy keyword set. In addition to this, the cloud sevice providers of our proposed scheme utilizes Performance Isolated Intertenant Storage Cluster to provide availability and performance isolation. Our implementation outcome have shown that our proposed searching scheme minimizes searching time on the cloud storage server by utilizing the data effectively with high availability and predictable performance isolation. Applications/Improvements: This facilitates the cloud service provider to hold back the time to serve multiple customers as the searching time is absolutely diminished. Keywords: Asymmetric Encryption, Intertenancy, Keyword Classifier, Performance Isolation, Storage Clusters

A Model Driven Framework for Portable Cloud Services: Proof of Concept Implementation

Rapid development of Cloud Computing and its increasing popularity in recent years has driven many commercial cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures and APIs and methods to access the cloud resources. Lack of standards has caused the collaboration and portability of cloud services a very complex task. In this paper we have identified the challenges involved in portability of cloud apps and analyzed the existing techniques for portability at platform level. In this paper, we propose an approach using Model Driven Engineering to develop SaaS applications in a cloud-agnostic way. We introduce DSkyL, an eclipse plugin for cloud application development using feature models and domain model analysis, which would support construction, customization, development and deployment of cloud application components across multiple clouds. It also reduces the application development time drastically. This paper aims to sketch the architecture of DSkyL and the major steps involved in the process.