SaaS Resource Research Articles

A Novel Meta-Information Management System for SaaS

Cloud hosts numerous services with various underlying business logic and data stores. In this multifaceted and distributed environment locating or updating SaaS services is a crucial task. Researchers proposed various mechanism for this purpose, which involves accessing or altering the meta-information of the services, its underlying business logics. Thus, management of meta-information is crucial for SaaS services. It demands efficient categorization and cataloguing mechanism by preserving semantic-relationships among interrelated services, business processes and data sources. This article proposes a flexible and scalable meta-information management system for SaaS, capable of maintaining dependencies among various SaaS resources. It is useful towards identification of interrelated business processes, services and data sources and facilitates incremental update of meta-information. The proposed system is implemented using Hadoop and a series of experiments have been carried out, which shows that it can efficiently scale and effectively categorize and catalogue different SaaS resources.

Modeling and deploying hybrid tenant requests with shared networklets

In recent years, IaaS providers are trying to cater to application layer development, PaaS providers are exposing more infrastructure APIs to customers, and SaaS providers start to allow tenants to build their apps within their software. All of these indicate that, the popular wisdom that cloud computing comes in three flavors—IaaS, PaaS, and SaaS—no longer describes reality. The lines between IaaS, PaaS, and SaaS are becoming blurred as datacenter providers seek to create cloud platforms that can satisfy the needs of enterprises and widen their appeal to developers. In hybrid datacenters, resource requests from tenants are increasingly transforming into hybrid requests that may simultaneously demand IaaS, PaaS, and SaaS resources. This paper tackles the challenge of modeling and deploying hybrid tenant resource requests in datacenter networks, for which we coin “networklet” to represent a set of VMs that collaboratively provide some PaaS or SaaS service. Through extracting networklets from tenant requests and thus sharing them between multiple tenants, we can achieve a win-win situation for datacenter providers and tenants. Extensive evaluations show that, the proposed model and deployment algorithm indeed reduce the completion time of tenant requests while maintaining performance guarantee.

Security‐aware SaaS placement using swarm intelligence

AbstractCloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on‐demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service models. To meet the increasing demands of users, SaaS can be offered in a composite form. Although this approach offers some advantages like flexibility and reusability, it raises a question about how to manage composite SaaS in the distributed and the highly dynamic cloud environment. In this paper, we address one of the major SaaS resource management issues referred to as SaaS placement problem. As existing efforts only focus on SaaS placement problem from the perspective of resources utilization to optimize SaaS performance and minimize resource usage, in this paper, we also incorporate security concerns in SaaS placement strategy. In fact, security risk is one of the major factors influencing the efficiency of the composite SaaS. We adopt a multi‐swarm variant of particle swarm optimization to propose a security‐aware SaaS placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. Experiments show that our solution outperforms existing SaaS placement approaches.

SaaS Resource Management Model and Architecture Research

Nowadays many enterprises have to face a real problem in their operation processes, which is mismatch be- tween active demands and resource. To solve this problem, traditional literatures employed Resource aggregation of Cloud Computing. However the most of these researches focus on computing resources but only few on application re- sources. And there does not exist a complete solution. In this paper, a resource service model RSM which is dynamic, cus- tomizable and fixable is presented. This model can be able to help enterprise add surplus resources into cloud resource pool. Then, a SaaS resource management architecture CARMA is built. The CARMA is used to solve the problems in sharing and management resource of enterprises. Meanwhile, resource optimization selection is taken into CARMA to re- duce resource waste and task delay. Applied in the transport service field, practical work proved that the proposed models are feasible and achieve the expected effect.