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In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv)(More)
Effective fault-handling in emerging complex applications in large-scale MAS (Multi-agent Systems) requires the ability to dynamically adapt resource allocation and fault tolerance policies in response to changes in environment, user or application requirements, and available resources. This adaptation process incorporates an observation mechanism that(More)
Effective fault-handling in emerging complex applications in large-scale MAS (Multi-agent Systems) requires the ability to dynamically adapt resource allocation and fault tolerance policies in response to changes in environment, user or application requirements, and available resources. This adaptation process incorporates an observation mechanism that(More)
In today's world, tenants of cloud systems expect timely responses to queries that process ever-increasing sizes of data. However, most cloud providers offer their services without any performance guarantees to their tenants. In this paper we propose a data replication strategy that aims to satisfy performance guarantees for the tenant while ensuring(More)
Adaptive replication increases the system's response time due to the need for monitoring in fault tolerant multi-agent systems. The sampling period is one of the key factors that could influence the cost of adaptive replication. In this paper, we show how to select an appropriate sampling period in a heuristic manner to decrease the cost incurred by(More)
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