Possibili soluzioni per garantire qos nelle comunicazioni inter-data centers in ambienti cloud computing.

Nel presente lavoro, partendo dalla definizione di alcuni punti chiavi del concetto di cloud computing, si è insistito molto sulle problematiche relative alle performance degli ambenti cloud, e alle diverse proposte attualmente presenti sul mercato con i relativi limiti. Dopo averle illustrate in modo dettagliato, le diverse proposte sono state tra loro messe a confronto al fine di evidenziare, per ciascuna di essa, tanto gli aspetti positivi quanto i punti di criticità.

A clusterized firewall framework for cloud computing

Cloud computing is becoming popular as the next infrastructure of computing platform. However, with data and business applications outsourced to a third party, how to protect cloud data centers from numerous attacks has become a critical concern. In this paper, we propose a clusterized framework of cloud firewall, which characters performance and cost evaluation. To provide quantitative performance analysis of the cloud firewall, a novel M/Geo/1 analytical model is established. The model allows cloud defenders to extract key system measures such as request response time, and determine how many resources are needed to guarantee quality of service (QoS). Moreover, we give an insight into financial cost of the proposed cloud firewall. Finally, our analytical results are verified by simulation experiments.

A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

Mutual protection in a cloud computing environment

The term “cloud computing” has emerged as a major ICT trend and has been acknowledged by respected industry survey organizations as a key technology and market development theme for the industry and ICT users in 2010. However, one of the major challenges that faces the cloud computing concept and its global acceptance is how to secure and protect the data and processes that are the property of the user. The security of the cloud computing environment is a new research area requiring further development by both the academic and industrial research communities. Today, there are many diverse and uncoordinated efforts underway to address security issues in cloud computing and, especially, the identity management issues. This paper introduces an architecture for a new approach to necessary “mutual protection” in the cloud computing environment, based upon a concept of mutual trust and the specification of definable profiles in vector matrix form. The architecture aims to achieve better, more generic and flexible authentication, authorization and control, based on a concept of mutuality, within that cloud computing environment.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.

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.

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.

A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing

The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generalization of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics. Also, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement. The comparison results show that the computation using our mapper/reducer placement is much cheaper while still satisfying the computation deadline.

A new grouping genetic algorithm for the MapReduce placement problem in cloud computing

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NPcomplete. Thus, in this paper we propose a new grouping genetic algorithm for the mappers/reducers placement problem in cloud computing. Compared with the original one, our grouping genetic algorithm uses an innovative coding scheme and also eliminates the inversion operator which is an essential operator in the original grouping genetic algorithm. The new grouping genetic algorithm is evaluated by experiments and the experimental results show that it is much more efficient than four popular algorithms for the problem, including the original grouping genetic algorithm.

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.

The role of institutional pressures and top management support in the intention to adopt cloud computing solutions

Purpose – While many studies have predominantly looked at the benefits and risks of cloud computing, little is known whether and to what extent institutional forces play a role in cloud computing adoption. The purpose of this paper is to explore the role of institutional factors in top management team’s (TMT’s) decision to adopt cloud computing services. Design/methodology/approach – A model is developed and tested with data from an Australian survey using the partial least squares modeling technique. Findings – The results suggest that mimetic and coercive pressures influence TMT’s beliefs in the benefits of cloud computing. The results also show that TMT’s beliefs drive TMT’s participation, which in turn affects the intention to increase the adoption of cloud computing solutions. Research limitations/implications – Future studies could incorporate the influences of local actors who might also press for innovation. Practical implications – Given the influence of institutional forces and the plethora of cloud-based solutions on the market, it is recommended that TMTs exercise a high degree of caution when deciding for the types of applications to be outsourced as organizational requirements in terms of performance and security will differ. Originality/value – The paper contributes to the growing empirical literature on cloud computing adoption and offers the institutional framework as an alternative lens with which to interpret cloud-based information technology outsourcing.

On reconsideration of IT governance structures to govern cloud computing services

There is an increase in the uptake of cloud computing services (CCS). CCS is adopted in the form of a utility, and it incorporates business risks of the service providers and intermediaries. Thus, the adoption of CCS will change the risk profile of an organization. In this situation, organisations need to develop competencies by reconsidering their IT governance structures to achieve a desired level of IT-business alignment and maintain their risk appetite to source business value from CCS. We use the resource-based theories to suggest that collaborative board oversight of CCS, competencies relating to CCS information and financial management, and a CCS-related continuous audit program can contribute to business process performance improvements and overall firm performance. Using survey data, we find evidence of a positive association between these IT governance considerations and business process performance. We also find evidence of positive association between business process performance improvements and overall firm performance. The results suggest that the suggested considerations on IT governance structures can contribute to CCS-related IT-business alignment and lead to anticipated business value from CCS. This study provides guidance to organizations on competencies required to secure business value from CCS.

Governing cloud computing services: Reconsideration of IT governance structures

There is an increase in the uptake of cloud computing services (CCS). CCS is adopted in the form of a utility, and it incorporates business risks of the service providers and intermediaries. Thus, the adoption of CCS will change the risk profile of an organization. In this situation, organizations need to develop competencies by reconsidering their IT governance structures to achieve a desired level of IT-business alignment and maintain their risk appetite to source business value from CCS. We use the resource-based theories to suggest that collaborative board oversight of CCS, competencies relating to CCS information and financial management, and a CCS-related continuous audit program can contribute to business process performance improvements and overall firm performance. Using survey data, we find evidence of a positive association between these IT governance considerations and business process performance. We also find evidence of positive association between business process performance improvements and overall firm performance. The results suggest that the suggested considerations on IT governance structures can contribute to CCS-related IT-business alignment and lead to anticipated business value from CCS. This study provides guidance to organizations on competencies required to secure business value from CCS.