A Novel Model of Data Storage Service in the Architecture Cloud Storage

Cloud storage is the lower layer of the cloud computing system that supports other layers above it. Up to now the likes of Google, Microsoft, IBM, Amazon have been providing cloud storage services. Since it’s efficient way to store and manage important data, the offer of free storage attracts researchers. As a result, cloud storage research will not only track trends, but will also have high application value. Therefore, this paper, introduces a novel model of data storage and backup in cloud storage, that optimally combines customer storage resources with service providers, so that redundancy, storage strategy and configuration properties can be adjusted adequately to the needs of the storage service consumer. In this paper, we review two of the backup technologies (Snapshot and D2D), that are used in this model. And the first contribution is bound to both determining consumer requirements and choosing the provider. Next— life cycle and phases of preparation model for data storage services. Furthermore, we present place and form of the model in cloud storage architecture. The model aims to increase the availability of data and reduces the loss of data in storage environments

A Novel Model of Data Storage Service in the Architecture Cloud Storage

Cloud storage is the lower layer of the cloud computing system that supports other layers above it. Up to now the likes of Google, Microsoft, IBM, Amazon have been providing cloud storage services. Since it’s efficient way to store and manage important data, the offer of free storage attracts researchers. As a result, cloud storage research will not only track trends, but will also have high application value. Therefore, this paper, introduces a novel model of data storage and backup in cloud storage, that optimally combines customer storage resources with service providers, so that redundancy, storage strategy and configuration properties can be adjusted adequately to the needs of the storage service consumer. In this paper, we review two of the backup technologies (Snapshot and D2D), that are used in this model. And the first contribution is bound to both determining consumer requirements and choosing the provider. Next— life cycle and phases of preparation model for data storage services. Furthermore, we present place and form of the model in cloud storage architecture. The model aims to increase the availability of data and reduces the loss of data in storage environments

Lot-sizing for production planning in a recovery system with returns

This paper deals with the production planning and control problem of a single product involving combined manufacturing and remanufacturing operations. We investigate here a lot-sizing problem in which the demand for items can be satisfied by both the new and the remanufactured products. We assume that produced and recovered items are of the same quality. Two types of inventories are involved in this problem. The produced items are stored in the first inventory. The returned products are collected in the second inventory and then remanufactured. The objective of this study is to propose a manufacturing/remanufacturing policy that would minimize the holding, the set up and preparation costs. The decision variables are the manufacturing and the remanufacturing rates. The paper proposes an extension of the reverse Wagner/Whitin dynamic production planning and inventory control model, a Memetic Algorithm (MA) and a Hybrid Algorithm (HA). The HA was improved with a post-optimization procedure using Path Relinking. Numerical experiments were conducted on a set of 300 instances with up to 48 periods. The results show that both methods give high-quality solutions in moderate computational time

Lot-sizing for production planning in a recovery system with returns

This paper deals with the production planning and control problem of a single product involving combined manufacturing and remanufacturing operations. We investigate here a lot-sizing problem in which the demand for items can be satisfied by both the new and the remanufactured products. We assume that produced and recovered items are of the same quality. Two types of inventories are involved in this problem. The produced items are stored in the first inventory. The returned products are collected in the second inventory and then remanufactured. The objective of this study is to propose a manufacturing/remanufacturing policy that would minimize the holding, the set up and preparation costs. The decision variables are the manufacturing and the remanufacturing rates. The paper proposes an extension of the reverse Wagner/Whitin dynamic production planning and inventory control model, a Memetic Algorithm (MA) and a Hybrid Algorithm (HA). The HA was improved with a post-optimization procedure using Path Relinking. Numerical experiments were conducted on a set of 300 instances with up to 48 periods. The results show that both methods give high-quality solutions in moderate computational time