scholarly journals Local Recovery for High Availability in Strongly Consistent Cloud Services

2017 ◽  
Vol 14 (2) ◽  
pp. 172-184 ◽  
Author(s):  
James W. Anderson ◽  
Hein Meling ◽  
Alexander Rasmussen ◽  
Amin Vahdat ◽  
Keith Marzullo
Keyword(s):  
High Availability ◽  
Cloud Services ◽  
Local Recovery ◽  
Strongly Consistent

Local and Remote Recovery of Cloud Services Using Backward Atomic Backup Recovery Technique for High Availability in Strongly Consistent Cloud Service

2019 ◽  
Vol 11 (4) ◽  
pp. 16-33
Author(s):  
Praveen Shivashankrappa Challagidad ◽  
Mahantesh N. Birje
Keyword(s):  
Time Window ◽  
Cloud Service ◽  
High Availability ◽  
Cloud Services ◽  
Recovery Technique ◽  
Recovery Models ◽  
Window Of Vulnerability ◽  
Replicated Services ◽  
Cloud Infrastructures ◽  
Strongly Consistent

Data loss occurs due to crashing, correlated failure, logical failure, power outages and security threats. Several techniques (e.g. NoBackup, WARBackup and LocalRecovery) are being used to recover data locally. And, strongly consistent Cloud services (SCCS) must provide good performance and high availability. However, conventional strong consistency replication methods have the limitation of availability of replicated services when recovering huge amount of data across wide area links. There is a need for remote recovery mechanisms for high availability of service/data, because distributed nature of cloud infrastructures. To address these issues, the article proposes a hierarchical system architecture for replication across a data center, and employs the backward atomic backup recovery technique (BABRT) for local recovery and remote recovery for high availability of the cloud services/data. A mathematical model for BABRT is described. Simulation results show that BABRT reduces the storage consumption, recovery time, window of vulnerability and failure rates, compared to other recovery models.

scholarly journals Dynamic High Availability Architecture Framework for SOA Computing

2018 ◽  
Vol 7 (3.12) ◽  
pp. 740
Author(s):  
S Kumaresan ◽  
Sumithra Devi.K.A
Keyword(s):  
Real Time ◽  
Cloud Service ◽  
High Availability ◽  
Cloud Services ◽  
Loose Coupling ◽  
Cloud Environment ◽  
Architecture Framework ◽  
Generic Architecture ◽  
Multiple Platform ◽  
Complicated Situation

In Software technology stackCloud services provides easy coupling implementation to enhance encapsulation data between multiple platform data exchanges. My finding towards  introducing High Availability Architecture for cloud environment which covers Load Balancing, Failover, High Availability Resources. To achieve thisfeatures it’s identified framework architecture which is called as Dynamic High Availability Architecture Framework for SOA Computing which increase cloud services standard inhigh witheasy adaptable security. Even though cloud service supports loose coupling and isolation business logics. At current cloud service provide wants to launch new web service request on fly same service will notnotified into client in real-time scenario.  To overcome this complicated situation we have introduced (GHAFC) Generic Architecture Framework in Cloud Computing. Which will support data exchanges between producer and consumer onthe fly with real time scenario.

Oracle High Availability, Disaster Recovery, and Cloud Services

2019 ◽  
Author(s):  
YV Ravi Kumar ◽  
Nassyam Basha ◽  
Krishna Kumar K M ◽  
Bal Mukund Sharma ◽  
Konstantin Kerekovski
Keyword(s):  
Disaster Recovery ◽  
High Availability ◽  
Cloud Services

Elasticity in Cloud Databases and Their Query Processing

2013 ◽  
Vol 9 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Goetz Graefe ◽  
Anisoara Nica ◽  
Knut Stolze ◽  
Thomas Neumann ◽  
Todd Eavis ◽  
...  
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
High Availability ◽  
Optimization Techniques ◽  
Cloud Services ◽  
Materialized Views ◽  
Short Period ◽  
Cloud Databases ◽  
Elastic Data ◽  
Entire Dataset

A central promise of cloud services is elastic, on-demand provisioning. The provisioning of data on temporarily available nodes is what makes elastic database services a hard problem. The essential task that enables elastic data services is bringing a node and its data up-to-date. Strategies for high availability do not satisfy the need in this context because they bring nodes online and up-to-date by repeating history, e.g., by log shipping. Nodes must become up-to-date and useful for query processing incrementally by key range. What is wanted is a technique such that in a newly added node, during each short period of time, an additional small key range becomes up-to-date, until eventually the entire dataset becomes up-to-date and useful for query processing, with overall update performance comparable to a traditional high-availability strategy that carries the entire dataset forward without regard to key ranges. Even without the entire dataset being available, the node is productive and participates in query processing tasks. The authors’ proposed solution relies on techniques from partitioned B-trees, adaptive merging, deferred maintenance of secondary indexes and of materialized views, and query optimization using materialized views. The paper introduces a family of maintenance strategies for temporarily available copies, the space of possible query execution plans and their cost functions, as well as appropriate query optimization techniques.

scholarly journals Design and Implementation of High-Availability Architecture for IoT-Cloud Services

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3276 ◽  
Author(s):  
Hyunsik Yang ◽  
Younghan Kim
Keyword(s):  
Internet Of Things ◽  
Fault Detection ◽  
High Availability ◽  
Cloud Services ◽  
Fault Recovery ◽  
Cloud Systems ◽  
Service Characteristics ◽  
Design And Implementation ◽  
Iot Applications ◽  
Recovery Mechanisms

For many vertical Internet of Things (IoT) applications, the high availability is very important. In traditional cloud systems, services are usually implemented with the same level of availability in which the fault detection and fault recovery mechanisms are not aware of service characteristics. In IoT-cloud, various services are provided with different service characteristics and availability requirements. Therefore, the existing cloud system is inefficient to optimize the availability method and resources to meet service requirements. To address this issue, this paper proposes a high availability architecture that is capable of dynamically optimizing the availability method based on service characteristics. The proposed architecture was verified through an implementation system based on OpenStack, and it was demonstrated that the system was able to achieve the target availability while optimizing resources, in contrast with existing architectures that use predefined availability methods.

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