Optimal Replica Placement Research Articles

Cloud Replica Management-Based Hybrid Optimization

This research addresses the challenges in cloud-based replica management by proposing a novel strategy employing a Genetically Implied Greywolf with Oppositional Learning (GIGOL) hybrid optimization technique. This approach optimizes multi-objectives such as response time, load balancing, availability, replication cost, and energy consumption, ensuring cost-effectiveness and energy efficiency. The GIGOL model integrates Genetic Algorithm, opposition learning, and Grey Wolf Optimization, aiming to achieve optimal replica placement. The study emphasizes resolving overhead issues through machine learning techniques for efficient cloud-based replica management. Performance evaluation showcases improvements in response time, load balancing, availability, replication cost, and energy consumption, highlighting the effectiveness of the proposed approach within budget constraints and management policies.

Algorithms for optimal replica placement under correlated failure in hierarchical failure domains

In data centers, data replication is the primary method used to ensure availability of customer data. To avoid correlated failure, cloud storage infrastructure providers model hierarchical failure domains using a tree, and avoid placing a large number of data replicas within the same failure domain (i.e. on the same branch of the tree). Typical best practices ensure that replicas are distributed across failure domains, but relatively little is known concerning optimization algorithms for distributing data replicas. Using a hierarchical model, we answer how to distribute replicas across failure domains optimally. We formulate a novel optimization problem for replica placement in data centers. As part of our problem, we formalize and present a new criterion for optimizing a replica placement. Our overall goal is to choose placements in which correlated failures disable as few replicas as possible.In this work, we provide two optimization algorithms for dependency models represented by trees. We first present an O(n+ρlog⁡ρ) time dynamic programming algorithm for optimally placing ρ replicas of a single block on the leaves (representing servers) of a tree with n vertices. We next consider the problem of optimally placing replicas of multiple blocks of data, where every block may have a different replication factor. For this problem, we give a dynamic programming algorithm that runs in O(nρmax3δ2mpoly(δ)), where m denotes the number of blocks, ρmax denotes the maximum replication factor of a block, and δ denotes the maximum difference in the replication factors of any two blocks. The running time of the algorithm is polynomial when the δ, which we refer to as the skew, is a constant.

Placement of Data Array Replicas in a Distributed System With Unreliable Communication Channels

Abstract The paper proposes an approach to solving the problem of optimal placement of data array replicas in a distributed system. It presents a formal model of a distributed system with unreliable communication channels. A system is represented as a graph with coloured arcs. The formulation of the problem of finding the optimal placement of replicas is given. As a criterion for optimization, the minimum response time to a data request is considered. The task solution heuristic algorithm is also proposed in the paper.

DARS: A dynamic adaptive replica strategy under high load Cloud-P2P

In Cloud-P2P system, replica strategy is utilized for obtaining low access delay and high load balance, which requires paying more attention to the time of replica creation. Most of current replica strategies usually utilize the method of afterwards adjustment fixed threshold. However, these strategies may increase a large number of overload nodes and then lead to aggregate effect of being overloaded, especially under high load condition. To deal with the problem, this paper proposes a Dynamic Adaptive Replica Strategy (DARS) based on the node’s overheating similarity. DARS addresses the problem of replica creation time, including obtaining the replica creation opportune moment and finding the optimal replica placement node, by a decentralized self-adaptive manner. Based on the overheating similarity, DARS applies fuzzy membership function to obtain the replica creation opportune moment, which enables the node to create replica before its overload. Meanwhile, DARS adopts the fuzzy clustering analysis method to find out the node with high node degree and low load which is used as optimal placement node to store replicas. Extensive experiments demonstrate that DARS obtains superior performances in access latency around 15%∼20% on average and better load balance than other similar methods under high load condition.

Cost Optimization of Replicas in Tree Network of Data Grid with QoS and Bandwidth Constraints

Data Grid provides resources for data-intensive scientific applications that need to access a huge amount of data around the world. Since data grid is built on a wide-area network, its latency prohibits efficient access to data. This latency can be decreased by data replication in the vicinity of users who request data. Data replication can also improve data availability and decreases network bandwidth usage. It can be influenced by two imperative constraints: Quality of Service (QoS) that is locally owned by a user and bandwidth constraint that globally affects on link that might be shared by multiple users. Guaranteeing both constraints and also minimizing replication cost consisting communication and storage costs is a challenging task. To address this problem, the authors propose to use a dynamic algorithm called Optimal Placement of Replicas to minimize replication cost and coupled with meeting both mentioned constraints. It is also designed as heuristic algorithms that are competitive with optimal algorithm in performance metrics such as replication cost, network bandwidth usage and data availability. Extensive simulations show that the Optimal algorithm saves 10% cost compared to heuristic algorithms and provides local responsiveness for half of the user requests.

On optimally reducing search costs for placing replicas in Symphony P2P networks

Peer-to-peer (P2P) overlay networks are widely employed in distributed systems. Furthermore, the number of hops required by a node for searching an object is the fundamental search cost of a P2P network. Hence, since placing replicas can efficiently reduce such a cost of the whole system, how to deploy replicas to reduce it as much as possible becomes a critical problem of P2P networks. In this study, the authors investigate this centre problem for the randomised P2P network ‘Symphony’. Particularly, the authors present a stochastic analysis on optimal replica placements, in terms of number of total hops, as well as proposing an optimal replica placement algorithm to perform a simulation study on this issue. Both the theoretical and experimental results show that locations of optimal replica placements in Symphony are not necessarily closest to the target node in the identifier space. Hence, based on the results, the authors evaluate one existing simple but experimentally efficient replica placement strategy, which exactly allocates replicas at nodes closest to the target node, to provide some guidelines on designing more efficient strategy applicable to the real-world Symphony.

A Data Replica Placement Scheme for Cloud Storage under Healthcare IoT Environment

In this paper, a multiple data replica placement scheme for cloud storage under health-care environment is proposed. It is based on p-center model with minimizing user access cost as its optimization objective. It uses GA (Genetic Algorithm) to find the optimal data replica placement solution. Simulation results have shown that it can not only improve user access time and promote load balance but also enhance system scalability and reliability.

Fast Optimal Replica Placement with Exhaustive Search Using Dynamically Reconfigurable Processor

This paper proposes a new replica placement algorithm that expands the exhaustive search limit with reasonable calculation time. It combines a new type of parallel data-flow processor with an architecture tuned for fast calculation. The replica placement problem is to find a replica-server set satisfying service constraints in a content delivery network (CDN). It is derived from the set cover problem which is known to be NP-hard. It is impractical to use exhaustive search to obtain optimal replica placement in large-scale networks, because calculation time increases with the number of combinations. To reduce calculation time, heuristic algorithms have been proposed, but it is known that no heuristic algorithm is assured of finding the optimal solution. The proposed algorithm suits parallel processing and pipeline execution and is implemented on DAPDNA-2, a dynamically reconfigurable processor. Experiments show that the proposed algorithm expands the exhaustive search limit by the factor of 18.8 compared to the conventional algorithm search limit running on a Neumann-type processor.

Optimal replica placement in hierarchical Data Grids with locality assurance

In this paper, we address three issues concerning data replica placement in hierarchical Data Grids that can be presented as tree structures. The first is how to ensure load balance among replicas. To achieve this, we propose a placement algorithm that finds the optimal locations for replicas so that their workload is balanced. The second issue is how to minimize the number of replicas. To solve this problem, we propose an algorithm that determines the minimum number of replicas required when the maximum workload capacity of each replica server is known. Finally, we address the issue of service quality by proposing a new model in which each request must be given a quality-of-service guarantee. We describe new algorithms that ensure both workload balance and quality of service simultaneously. We conduct extensive simulation experiments to evaluate the effectiveness of our algorithms. The comparison with the previous Affinity Replica Location Policy demonstrates that our algorithms consistently outperform the heuristic algorithm both in terms of minimum number of replicas used and the actual data transmission time.

Optimal Replica Placement under TTL-Based Consistency

Geographically replicating popular objects in the Internet speeds up content distribution at the cost of keeping the replicas consistent and up-to-date. The overall effectiveness of replication can be measured by the total communication cost consisting of client accesses and consistency management, both of which depend on the locations of the replicas. This paper investigates the problem of placing replicas under the widely used TTL-based consistency scheme. A polynomial-time algorithm is proposed to compute the optimal placement of a given number of replicas in a network. The new replica placement scheme is compared, using real Internet topologies and Web traces, against two existing approaches which do not consider consistency management or assume invalidation-based consistency scheme. The factors affecting their performance are identified and discussed

시간 복잡도를 개선한 웹 서버 배치 알고리즘

Recently, contents distribution technologies have been used to cope with the explosive demand for Web services. In this paper, we addressed the issue of the optimal placement of replicas in the environment where Web contents are replicated. We placed replicas so that clients can have access to replicas with the proper delay and bandwidth. We attempted to solve the problem via dynamic programming considering cost of delay and traffic We have come up with time complexity that is less than <TEX>$O(n^2)$</TEX>. We defined the threshold and proved that our algorithm guarantees the reliable services.

The Replica Location Problem and Chebyshev Polynomials of the Second Kind

We study the optimal placement of replicas of data objects in a connected network with the topology of a straight-line segment. This special case of a NP-complete location problem has a remarkably attractive algebraic solution. The minimum cost problem gives rise to tridiagonal matrices that are both persymmetric and symmetric and these are used to prove the symmetry of the optimal solution. The eigenvalues and eigenvectors of these matrices are completely described by Chebyshev polynomials of the second kind to give a complete solution to the replica location problem. We denote the k th Chebyshev polynomials of the second kind by U k . The Chebyshev identity arises naturally in examining the norms of the eigenvectors that occur.

Optimal placement of replicas in trees with read, write, and storage costs

We consider the problem of placing copies of objects in a tree network in order to minimize the cost of servicing read and write requests to objects when the tree nodes have limited storage and the number of copies permitted is limited. The set of nodes that have a copy of the object is the residence set of the object. A node wishing to read the object will read the object from the closest node in the residence set. A node wishing to update the object will update the copy of the object at all the nodes in the residence set. Updates are propagated over a certain minimum spanning tree. The cost associated with a residence set equals the cost of servicing all the read and write requests and the storage costs for those copies. We describe an O(n/sup 3/p/sup 2/)-time algorithm for finding an optimal residence set of size p for an object in a tree with n nodes, taking into consideration the read, write, and storage costs. Furthermore, we describe a O(n/sup 3/p/sup 2//spl Lambda//sub max//sup 2/)-time algorithm for finding a minimum cost normal p-residence set for an object in a tree, this time also taking into account the load imposed by the nodes of the tree on the nodes in a residence set and their capacity constraints, where /spl Lambda//sub max/ is an upper bound on the capacity of each node of the tree.

Optimal placement of data replicas in distributed database with majority voting protocol

In a distributed database system, data replicas are placed at different locations of a network to achieve the high data availability in presence of link failure. Optimal placements of data replicas have been studied extensively in the literature for various protocol. In this paper, we present a sufficient and necessary condition for optimality of a placement of odd number of data replicas in a ring network with majority voting protocol. As a corollary, we give a proof of a recent conjecture of Hu et al. (manuscript, 1999). Moreover, we also give a simple algorithm to find optimal placements in tree network with majority voting protocol.