Improve Performance by a Fuzzy-Based Dynamic Replication Algorithm in Grid, Cloud, and Fog

The efficiency of data-intensive applications in distributed environments such as Cloud, Fog, and Grid is directly related to data access delay. Delays caused by queue workload and delays caused by failure can decrease data access efficiency. Data replication is a critical technique in reducing access latency. In this paper, a fuzzy-based replication algorithm is proposed, which avoids the mentioned imposed delays by considering a comprehensive set of significant parameters to improve performance. The proposed algorithm selects the appropriate replica using a hierarchical method, taking into account the transmission cost, queue delay, and failure probability. The algorithm determines the best place for replication using a fuzzy inference system considering the queue workload, number of accesses in the future, last access time, and communication capacity. It uses the Simple Exponential Smoothing method to predict future file popularity. The OptorSim simulator evaluates the proposed algorithm in different access patterns. The results show that the algorithm improves performance in terms of the number of replications, the percentage of storage filled, and the mean job execution time. The proposed algorithm has the highest efficiency in random access patterns, especially random Zipf access patterns. It also has good performance when the number of jobs and file size are increased.

State Management for Cloud-Native Applications

The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

Research on the Optimization Management of Cloud Privacy Strategy Based on Evolution Game

Cloud computing services have great convenience, but privacy security is a big obstacle of popularity. In the process result of privacy protection of cloud computing, it is difficult to choose the optimal strategy. In order to solve this problem, we propose a quantitative weight model of privacy information, use evolutionary game theory to establish a game model of attack protection, design the optimal protection strategy selection algorithm, and make the evolutionary stable equilibrium solution method from the limited rational constraint. In order to study the strategic dependence of the same game group, the classical dynamic replication equation is improved by using the incentive coefficient, an improved evolutionary game model of attack protection is constructed, the stability of equilibrium point is further analyzed by Jacobian matrix method, and the optimal selection strategy is obtained under different conditions. Finally, the correctness and validity of the model are verified by experiments, different strategies of the same group have the dual effects of promotion and inhibition, and the advantages of this paper are shown by comparing with other articles.

Enrich multi-channel P2P VoD streaming based on dynamic replication strategy

Peer-to-Peer Video-on-Demand (VoD) is a favorable solution which compromises thousands of videos to millions of users with completeinteractive video watching stream. Most of the profitable P2P streaming groupsPPLive, PPStream and UUSee have announced a multi-channel P2P VoD system that approvals user to view extra one channel at a time. The present multiple channel P2P VoD system resonant a video at a low streaming rate due to the channel resource inequity and channel churn. In order to growth the streaming capacity, this paper highlights completely different effective helpers created resource balancing scheme that actively recognizes the supply-and-demand inequity in multiple channels. Moreover, peers in an extra channel help its unused bandwidth resources to peers in a shortage channel that minimizes the server bandwidth consumption. To provide a desired replication ratio for optimal caching, it develops a dynamic replication strategy that optimally tunes the number of replicas based on dynamic popularity in a distributed and dynamic routine. This work accurately forecasts the varying popularity over time using Auto-Regressive Integrated Moving Average (ARIMA) model, an effective time-series forecasting technique that supports dynamic environment. Experimental assessment displays that the offered dynamic replication strategy which should achieves high streaming capacity under reduced server workload when associated to existing replication algorithms.