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.

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.

Business Models for Cloud Computing: Experiences from Developing Modeling & Simulation as a Service Applications in Industry

The potential of cloud computing is gaining significant interest in Modeling & Simulation (M&S). The underlying concept of using computing power as a utility is very attractive to users that can access state-of-the-art hardware and software without capital investment. Moreover, the cloud computing characteristics of rapid elasticity and the ability to scale up or down according to workload make it very attractive to numerous applications including M&S. Research and development work typically focuses on the implementation of cloud-based systems supporting M&S as a Service (MSaaS). Such systems are typically composed of a supply chain of technology services. How is the payment collected from the end-user and distributed to the stakeholders in the supply chain? We discuss the business aspects of developing a cloud platform for various M&S applications. Business models from the perspectives of the stakeholders involved in providing and using MSaaS and cloud computing are investigated and presented.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

The new cloud computing paradigm: the way to IT seen as a utility

In the present competitive environment, companies are wondering how to reduce their IT costs while increasing their efficiency and agility to react when changes in the business processes are required. Cloud Computing is the latest paradigm to optimize the use of IT resources considering ?everything as a service? and receiving these services from the Cloud (Internet) instead of owning and managing hardware and software assets. The benefits from the model are clear. However, there are also concerns and issues to be solved before Cloud Computing spreads across the different industries. This model will allow a pay-per-use model for the IT services and many benefits like cost savings, agility to react when business demands changes and simplicity because there will not be any infrastructure to operate and administrate. It will be comparable to the well known utilities like electricity, water or gas companies. However, this paper underlines several risk factors of the model. Leading technology companies should research on solutions to minimize the risks described in this article. Keywords - Cloud Computing, Utility Computing, Elastic Computing, Enterprise Agility

The Application of Cloud Computing in Education

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Cloud computing adoption in Australia : the benefits, risks and implications for accounting

The purpose of this paper is to empirically examine the state of cloud computing adoption in Australia. I specifically focus on the drivers, risks, and benefits of cloud computing from the perspective of IT experts and forensic accountants. I use thematic analysis of interview data to answer the research questions of the study. The findings suggest that cloud computing is increasingly gaining foothold in many sectors due to its advantages such as flexibility and the speed of deployment. However, security remains an issue and therefore its adoption is likely to be selective and phased. Of particular concern are the involvement of third parties and foreign jurisdictions, which in the event of damage may complicate litigation and forensic investigations. This is one of the first empirical studies that reports on cloud computing adoption and experiences in Australia.

On cloud computing service considerations for the small and medium enterprises

The concept of cloud computing services is appealing to the small and medium enterprises (SMEs), with the opportunity to acquire modern information technology resources as a utility and avoid costly capital investments in technology resources. However, the adoption of the cloud computing services presents significant challenges to the SMEs. The SMEs need to determine a path to adopting the cloud computing services that would ensure their sustainable presence in the cloud computing environment. Information about approaches to adopting the cloud computing services by the SMEs is fragmented. Through an interpretive design, we suggest that the SMEs need to have a strategic and incremental intent, understand their organizational structure, understand the external factors, consider the human resource capacity, and understand the value expectations from the cloud computing services to forge a successful path to adopting the cloud computing services. These factors would contribute to a model of cloud services for SMEs.

External auditors’ perceptions of cloud computing adoption in Australia

Adopting a multi-theoretical approach, I examine external auditors’ perceptions of the reasons why organizations do or do not adopt cloud computing. I interview forensic accountants and IT experts about the adoption, acceptance, institutional motives, and risks of cloud computing. Although the medium to large accounting firms where the external auditors worked almost exclusively used private clouds, both private and public cloud services were gaining a foothold among many of their clients. Despite the advantages of cloud computing, data confidentiality and the involvement of foreign jurisdictions remain a concern, particularly if the data are moved outside Australia. Additionally, some organizations seem to understand neither the technology itself nor their own requirements, which may lead to poorly negotiated contracts and service agreements. To minimize the risks associated with cloud computing, many organizations turn to hybrid solutions or private clouds that include national or dedicated data centers. To the best of my knowledge, this is the first empirical study that reports on cloud computing adoption from the perspectives of external auditors.

A Hub System for Cloud-Computing Based Business-Collaboration: Automating Ontology-Enabled Electronic Business-Service Discovery

The management and coordination of business-process collaboration experiences changes because of globalization, specialization, and innovation. Service-oriented computing (SOC) is a means towards businessprocess automation and recently, many industry standards emerged to become part of the service-oriented architecture (SOA) stack. In a globalized world, organizations face new challenges for setting up and carrying out collaborations in semi-automating ecosystems for business services. For being efficient and effective, many companies express their services electronically in what we term business-process as a service (BPaaS). Companies then source BPaaS on the fly from third parties if they are not able to create all service-value inhouse because of reasons such as lack of reasoures, lack of know-how, cost- and time-reduction needs. Thus, a need emerges for BPaaS-HUBs that not only store service offers and requests together with information about their issuing organizations and assigned owners, but that also allow an evaluation of trust and reputation in an anonymized electronic service marketplace. In this paper, we analyze the requirements, design architecture and system behavior of such a BPaaS-HUB to enable a fast setup and enactment of business-process collaboration. Moving into a cloud-computing setting, the results of this paper allow system designers to quickly evaluate which services they need for instantiationg the BPaaS-HUB architecture. Furthermore, the results also show what the protocol of a backbone service bus is that allows a communication between services that implement the BPaaS-HUB. Finally, the paper analyzes where an instantiation must assign additional computing resources vor the avoidance of performance bottlenecks.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.

The Impact of office productivity cloud computing on energy consumption and greenhouse gas emissions

Cloud computing is usually regarded as being energy efficient and thus emitting less greenhouse gases (GHG) than traditional forms of computing. When the energy consumption of Microsoft’s cloud computing Office 365 (O365) and traditional Office 2010 (O2010) software suites were tested and modeled, some cloud services were found to consume more energy than the traditional form. The developed model in this research took into consideration the energy consumption at the three main stages of data transmission; data center, network, and end user device. Comparable products from each suite were selected and activities were defined for each product to represent a different computing type. Microsoft provided highly confidential data for the data center stage, while the networking and user device stages were measured directly. A new measurement and software apportionment approach was defined and utilized allowing the power consumption of cloud services to be directly measured for the user device stage. Results indicated that cloud computing is more energy efficient for Excel and Outlook which consumed less energy and emitted less GHG than the standalone counterpart. The power consumption of the cloud based Outlook (8%) and Excel (17%) was lower than their traditional counterparts. However, the power consumption of the cloud version of Word was 17% higher than its traditional equivalent. A third mixed access method was also measured for Word which emitted 5% more GHG than the traditional version. It is evident that cloud computing may not provide a unified way forward to reduce energy consumption and GHG. Direct conversion from the standalone package into the cloud provision platform can now consider energy and GHG emissions at the software development and cloud service design stage using the methods described in this research.

Sviluppo storico ed economico del cloud computing: Cloud foundry come esempio di platform as a service

Il Cloud Computing è una realtà sempre più diffusa e discussa nel nostro periodo storico, ma probabilmente non è ancora chiaro a tutti di cosa si tratta e le potenzialità che possiede. Infatti, non esiste ancora una definizione univoca e condivisa e questo può creare confusione. Oggi le grandi compagnie nella comunità informatica spingono sempre di più per affermare i servizi Cloud a livello mondiale, non solo per le aziende del settore, ma anche per tutte le altre. Ed è così che le aziende di tutto il mondo si muovono per imparare e adottare questa nuova tecnologia, per spostare i loro centri dati e le loro applicazioni nel Cloud. Ma dove e quando nasce il Cloud Computing? Quali sono realmente i benefici per le aziende che adottano questa tecnologia? Questo è l'obiettivo della mia tesi: cercare di far chiarezza sulla sua definizione, indagare sulla sua nascita e fare un quadro economico del suo sviluppo, analizzando i benefici per le aziende e le opportunità offerte. Come caso di studio ho scelto la piattaforma Cloud Foundry perchè in questo momento è in forte espansione e sta facendo un grosso lavoro per cercare di rendere il suo prodotto uno standard per il Cloud Computing. Come esempio particolare di piattaforma basata su Cloud Foundry si parlerà di Bluemix, la piattaforma Cloud offerta da IBM, una delle più grandi aziende nel settore informatico.

Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.