Resource Storage Management Model for Ensuring Quality of Service in the Cloud Archive Systems

Nowadays, service providers offer a lot of IT services in the public or private cloud. The client can buy various kinds of services like SaaS, PaaS, etc. Recently there was introduced Backup as a Service (BaaS) as a variety of SaaS. At the moment there are available several different BaaSes for archiving the data in the cloud, but they provide only a basic level of service quality. In the paper we propose a model which ensures QoS for BaaS and some  methods for management of storage resources aimed at achieving the required SLA. This model introduces a set of parameters responsible for SLA level which can be offered on the basic or higher level of quality. The storage systems (typically HSM), which are distributed between several Data Centres,  are built based on disk arrays, VTLs, and tape libraries. The RSMM model does not assume bandwidth reservation or control, but is rather focused on the management of storage resources

Semantic-Based Storage QoS Management Methodology -- Case Study for Distributed Environments

The distributed computing environments, e.g. clouds, often deal with huge amounts of data, which constantly increase. The global growth of data is caused by ubiquitous personal devices, enterprise and scientific applications, etc. As the size of data grows new challenges are emerging in the context of storage management. Modern data and storage resource management systems need to face wide range of problems -- minimizing energy consumption (green data centers), optimizing resource usage, throughput and capacity, data availability, security and legal issues, scalability. In addition users or their applications can have QoS (Quality of Service) requirements concerning the storage access, which further complicates the management. To cope with this problem a common mass storage system model taking into account the performance aspects of a storage system becomes a necessity. The model described with semantic technologies brings a semantic interoperability between the system components. In this paper we describe our approach at data management with QoS based on the developed models as a case study for distributed environments

Agent-Based Monitoring Using Fuzzy Logic and Rules

In this paper we present two solutions of monitoring automation for distributed systems. We develop this system to automate monitoring of distributes systems. Both solutions are aimed to monitor data storage and web services like web page servers. The first solution implemented in a system called Saude-Net, is an rule-based top level monitoring tool. In this system there are implemented rules which provide conditions which refer to one or more measured values. This system is able to choose the best action for an observed situation, e.g. a failure. It is possible to define more than one rule which relate to the same monitoring resource. The second concept presented in this paper refers to a fuzzy logic agent based approach to network monitoring. It is called SAMM compliant Agent. It is an extension to the Semantic-based Autonomous Monitoring and Management system (SAMM). On the one hand, it uses rules to define simple actions, based on a simple condition and an action description. On the other hand the main knowledge of this solution is defined by fuzzy logic. This system is able to manage and modify its knowledge to better fit to monitored resources. The knowledge in this concept is distributed among all the agents. The agents residing on a different hosts handle their parts of the knowledge and are capable to share/exchange them