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.

RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks

How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim to fully utilize cache resources by optimizing the location and quantity distribution of replicas in the network, thereby improving the performance of the cache system. However, existing research primarily focuses on optimizing the placement of replicas along the content delivery path, which cannot avoid the inherent drawback of not being able to leverage off-path cache resources. The proposals for off-path caching cannot effectively solve this problem as they introduce excessive complexity and cooperation costs. In this paper, we address the trade-off between cache resource utilization and cooperation costs by introducing a mechanism complementary to replica placement. Instead of redesigning a new caching strategy from scratch, we propose a proactive cooperative caching mechanism (called RMBCC) that involves an independent replica migration process, through which we proactively relocate replicas evicted from the local cache to neighboring nodes with sufficient cache resources. The cooperation costs are effectively controlled through migration replica filtering, migration distance limitation, as well as hop-by-hop migration request propagation. Extensive simulation experiments show that RMBCC can be efficiently integrated with different on-path caching strategies. Compared with representative caching schemes, RMBCC achieves significant improvements in evaluation metrics such as cache hit ratio and content retrieval time, while only introducing negligible cooperation overhead.

Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques

Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes.

Survey on data replication in cloud systems

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor’s performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues & limitations that might help researchers support better future work on pattern mining for data replication in the cloud.

Online two-timescale service placement for time-sensitive applications in MEC-assisted network: A TMAGRL approach

Mobile edge computing (MEC) integrated with the Network Functions Virtualization (NFV) technique has been regarded as a promising solution for flexible services provision and user service experience improvement. However, existing service placement in such systems still faces the challenge of satisfying computing tasks with strict latency requirements, especially when massive mobile users roam around different coverage areas of edge servers. For this purpose, we first adopt a novel service placement framework that combines proactive replicas pre-deployment and reactive service migration. Based on this, we investigate the dynamic placement problem of multiple types of services achieved by the various virtualized network functions (VNFs) to minimize long-term redeployment costs in MEC-assisted systems, subject to the completion deadline of tasks and limited computing resources of edge servers. Considering that the update timescale of VNF replicas pre-deployment is different, we design a novel two-timescale multi-agent graph convolutional network-based reinforcement learning algorithm (TMAGRL) by invoking a long-timescale training layer for proactive VNF replicas placement and a short-timescale training layer for reactive VNF migration. Extensive numerical results reveal that TMAGRL, based on the designed hybrid framework, can learn a VNF placement strategy to adapt to the dynamics of the system without any prior information. Moreover, we verify its superior performance in terms of average service response latency and overall redeployment cost by comparing it with baselines.

A hybrid chaos-based algorithm for data object replication in distributed systems

One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms’ reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm’s performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach’s success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.

A balanced and reliable data replica placement scheme based on reinforcement learning in edge–cloud environments

With the rapid development of edge–cloud computing, distributing resources to edge nodes and terminal devices to provide high-quality services for latency-sensitive applications and reduce network communication costs has become increasingly important. However, the complexity and heterogeneity of the edge–cloud environment pose significant challenges to the reliability of data storage and device load balancing. To address these issues, in this paper, we propose a Deep Reinforcement Learning (DRL)-based data replica placement scheme, BRPS. This scheme considers the different geographic locations of hardware devices, the heterogeneous storage capacity and reliability, and the different data requirements of varying user services in edge–cloud environments. Firstly, we constructed a model for data replica placement in the edge–cloud environment, addressing key factors such as latency, reliability, and load, focusing on the heterogeneity of device resources and reliability, and the diverse data needs of user services. Furthermore, we propose the BRPS scheme using the Double Deep Q-Network (DDQN) method of DRL, transforming the data replica placement issue into a multi-objective optimization problem. By placing the DRL-based decision process in a separate management edge node, the separation of decision and execution is achieved, which enhances the efficiency of data replica placement, ensures data reliability, reduces latency, and achieves system load balance. Experimental results demonstrate that BRPS significantly outperforms the existing comparison schemes while ensuring data reliability. The BRPS scheme reduces latency by 8.12% compared to the Random scheme and outperforms the best heuristic in system load balancing and memory utilization by 13.15% and 11.91%, respectively. Moreover, BRPS shows superior adaptability in extreme network congestion scenarios and effectively adapts to the dynamic changes of nodes in edge–cloud environments.

TADRP: Toward Thermal-Aware Data Replica Placement in Data-Intensive Data Centers

With the mushrooming growth of data volumes, data replica placement plays a key role in promoting the energy efficiency and Quality-of-Service (QoS) of data-intensive data centers. The existing data placement strategies mainly focus on storage performance improvement or QoS enhancement in data centers, but ignore the indispensable factor -heat recirculation. To bridge this gap, we propose a thermal-aware data replica placement strategy called TADRP, aiming to improve cooling efficiency and minimize the total power consumption of data-intensive data centers. TADRP leverages an ant colony optimization (ACO) algorithm coupled with Laplacian probability distribution to find a quasi-optimal disk sequence (or Disk Sequence for short), which consists of disks selected from different rack servers to place data replicas. TADRP categorizes disks of Disk Sequence into active and inactive ones, by placing hot and cold replicas on active and inactive disks, respectively. We quantitatively evaluate TADRP in terms of cooling costs, total power consumption, number of power-state transitions, and execution time. We compare TADRP with four alternative solutions, namely, Random, Hadoop, SRS, and CDP-NSGAII. Experimental results show that TADRP can reduce the cooling costs and the total power of the existing solutions by 14.7% -61.7% and 19.2 %-55.1%, respectively, without undesirable I/O performance drops.

DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.

Task scheduling and data replication in cloud with improved correlation strategy

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness.

An indirect method of verifying passive fit of complete arch implant-supported frameworks fabricated from photogrammetry scans

The passive fit of frameworks on multi-unit abutments has been considered crucial for the long-term success of complete arch, implant-supported, fixed prostheses. 1 Jokstad A. Shokati B. New 3D technologies applied to assess the long-term clinical effects of misfit of the full jaw fixed prosthesis on dental implants. Clin Oral Implants Res. 2015; 26: 1129-1134 Crossref PubMed Google Scholar Photogrammetry has demonstrated better accuracy than intraoral scanning and conventional impression techniques to record the position of implants. 2 Sallorenzo A. Gómez-Polo M. Comparative study of the accuracy of an implant intraoral scanner and that of a conventional intraoral scanner for complete-arch fixed dental prostheses. J Prosthet Dent. 2022; 128: 1009-1016 Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar However, errors introduced during fabrication may still compromise the passive fit of the definitive prosthesis, even with accurate scanning using the photogrammetry technique. 3 Al-Meraikhi H. Yilmaz B. McGlumphy E. Brantley W. Johnston W.M. In vitro fit of CAD-CAM complete arch screw-retained titanium and zirconia implant prostheses fabricated on 4 implants. J Prosthet Dent. 2018; 119: 409-416 Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar The current intraoral methods of verifying passive fit, including visual inspection, tactile sensation, radiography, disclosing materials, screw resistance test, and the Sheffield test, are subjective and not capable of detecting lingual or subgingival misfit. 4 Abduo J. Bennani V. Waddell N. Lyons K. Swain M. Assessing the fit of implant fixed prostheses: a critical review. Int J Oral Maxillofac Implants. 2010; 25: 506-515 PubMed Google Scholar In the conventional protocol, the definitive cast made from a custom tray elastomeric impression can be verified with a dental gypsum device. 5 Manzella C. Burello V. Bignardi C. Carossa S. Schierano G. A method to improve passive fit of frameworks on implant-supported prostheses: an in vivo study. Int J Prosthodont. 2013; 26: 577-579 Crossref PubMed Google Scholar However, since the frameworks made from photogrammetry are manufactured without a definitive cast, verifying the passive fit of the frameworks is challenging. 6 Rutkunas V. Larsson C. Vult von Steyern P. Mangano F. Gedrimiene A. Clinical and laboratory passive fit assessment of implant-supported zirconia restorations fabricated using conventional and digital workflow. Clin Implant Dent Relat Res. 2020; 22: 237-245 Crossref PubMed Scopus (11) Google Scholar In the treatment of the first patient, misfits were found between the frameworks and the 3-dimensional printed cast; therefore, the accuracy of the frameworks needed to be verified to determine whether the distortion was introduced during printing of the cast (Fig. 1A-C). An inexpensive and user-friendly method of verifying the passive fit of frameworks is described. Figure 1A, Intraoral view after removal of interim prosthesis. B, Impression scanning using photogrammetry technology. C, Misfits between framework and printed cast. D, Placement of abutment replicas. E, Fixture of replicas with dental gypsum. F, Evaluating fit of framework. View Large Image Figure Viewer Download Hi-res image

A Hybrid Heuristic Algorithm Using Artificial Agents for Data Replication Problem in Distributed Systems

One of the key issues with large distributed systems, such as IoT platforms, is gaining timely access to data objects. As a result, decreasing the operation time of reading and writing data in distributed communication systems become essential demands for asymmetric system. A common method is to replicate the data objects across multiple servers. Replica placement, which can be performed statically or dynamically, is critical to the effectiveness of distributed systems in general. Replication and placing them on the best available data servers in an optimal manner is an NP-complete optimization problem. As a result, several heuristic strategies for replica placement in distributed systems have been presented. The primary goals of this research are to reduce the cost of data access time, reduce the number of replicas, and increase the reliability of the algorithms for placing replicas. In this paper, a discretized heuristic algorithm with artificial individuals and a hybrid imitation method were developed. In the proposed method, particle and gray-wolf-based individuals use a local memory and velocity to search for optimal solutions. The proposed method includes symmetry in both local and global searches. Another contribution of this research is the development of the proposed optimization algorithm for solving the data object replication problem in distributed systems. Regarding the results of simulations on the standard benchmark, the suggested method gives a 35% reduction in data access time with about six replicates. Furthermore, the standard deviation among the results obtained by the proposed method is about 0.015 which is lower than the other methods in the same experiments; hence, the method is more stable than the previous methods during different executions.

TS-REPLICA: A novel replica placement algorithm based on the entropy weight TOPSIS method in spark for multimedia data analysis

Performance optimization based on node attributes is of profound significance in the replica placement algorithm used in Hadoop distributed file system (HDFS). Currently, most researchers studying replica placement algorithms consider only a single attribute or a few multiple attributes. However, a single attribute cannot accurately express the performance of a node. Therefore, this paper proposes a replica placement algorithm based on the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method, called TS-REPLICA. First, a multi-attribute matrix that comprehensively reflects the performance and load of nodes is defined. Then, a TOPSIS-based algorithm is proposed to calculate the performance score of each data node. In addition, the entropy weight method is introduced to derive the weights of attributes for balancing the influence of weights of multiple attributes. Next, the comprehensive load score of each data node in the Spark cluster, the average comprehensive load score of each rack, and the average comprehensive load score of the entire cluster are calculated, and replica placement is performed based on the obtained scores. Finally, the effectiveness of the proposed algorithm is verified on various datasets and test cases. The experimental results show that the TS-REPLICA algorithm outperforms the better comparison algorithm in execution number in Spark cluster.

A dynamic decentralized strategy of replica placement on edge computing

Smart phone and its applications are used more and more extensively in our daily life. Short delay of arriving data is important to these applications, especially to some time-sensitive ones. To reduce transmission latency and improve user experience, a dynamic decentralized data replica placement and management strategy which works in edge nodes is proposed in this article. It studies the location, access frequency, latency improvement, and cost spent on placing replicas on edge nodes to seek a balance between cost spent for storage and reduced latency. Specifically, dynamic and decentralized replica placement strategy algorithm has load guarantee for edge nodes to avoid overload; it dynamically create or delete data replicas on edge nodes according to the request frequency. Dynamic and decentralized replica placement strategy is decentralized to relieve transmission cost. Experiment results show that dynamic and decentralized replica placement strategy algorithm in edge computing environments can greatly reduce transmission latency, balance edge nodes load, and improve system performance. Dynamic and decentralized replica placement strategy also considers the cost spent for storage, and it pursues a balance between many factors.

Self-Stabilizing Capacitated Vertex Cover Algorithms for Internet-of-Things-Enabled Wireless Sensor Networks.

Wireless sensor networks (WSNs) achieving environmental sensing are fundamental communication layer technologies in the Internet of Things. Battery-powered sensor nodes may face many problems, such as battery drain and software problems. Therefore, the utilization of self-stabilization, which is one of the fault-tolerance techniques, brings the network back to its legitimate state when the topology is changed due to node leaves. In this technique, a scheduler decides on which nodes could execute their rules regarding spatial and temporal properties. A useful graph theoretical structure is the vertex cover that can be utilized in various WSN applications such as routing, clustering, replica placement and link monitoring. A capacitated vertex cover is the generalized version of the problem which restricts the number of edges covered by a vertex by applying a capacity constraint to limit the covered edge count. In this paper, we propose two self-stabilizing capacitated vertex cover algorithms for WSNs. To the best of our knowledge, these algorithms are the first attempts in this manner. The first algorithm is stabilized under an unfair distributed scheduler (that is, the scheduler which does not grant all enabled nodes to make their moves but guarantees the global progress of the system) at most step, where n is the count of nodes. The second algorithm assumes 2-hop (degree 2) knowledge about the network and runs under the unfair scheduler, which subsumes the synchronous and distributed fair scheduler and stabilizes itself after moves in step, which is acceptable for most WSN setups. We theoretically analyze the algorithms to provide proof of correctness and their step complexities. Moreover, we provide simulation setups by applying IRIS sensor node parameters and compare our algorithms with their counterparts. The gathered measurements from the simulations revealed that the proposed algorithms are faster than their competitors, use less energy and offer better vertex cover solutions.

Adaptive data placement in the Fog infrastructure of IoT applications with dynamic changes

Time-sensitive Internet of Things (IoT) applications need to provide instant responses to end users. Seeking to reduce the latency of IoT applications is challenging task. Due to the dynamic changes that can occur over time such as failure of IoT devices or integration of new services and devices, the IoT application constantly requires new storage resources. To ensure the high performance of an IoT application, it is common today to place its produced data in the Fog infrastructure while satisfying its requirements over time. The main challenge in this regard is to ensure optimal data placement in this heterogeneous and distributed infrastructure with low latency. In fact, with continuous changes, new data assignments to target storage nodes can lead to data migration and increased latency. In this paper, we propose an adaptive approach to place the IoT data with multiple replicas in the Fog infrastructure while estimating and optimizing the overall latency. To cope with the IoT application changes, we establish a ℓ-backtracking greedy algorithm called adaptive multiple data replicas placement of IoT applications with external event changes in the Fog infrastructure (iFogDPE). It is a stable many-to-many matching algorithm that minimizes the write, read and migration latency using a few number of data replicas. Then, it assigns each data consumer with its appropriate data replica. To model and to simulate the IoT application and Fog infrastructure enactment as well as to test and to evaluate our solution we used iFogSim simulator. Our experimental results highlight the efficiency of our approach in reducing the storage and access latency with fewer data replicas while maintaining low data migration latency over time.

Fast Data Access through Nearest Location-Based Replica Placement

Nowadays, millions of telecommunication devices, IoT sensors, and web services, especially social media sites, are producing big data every second. Such applications with massive data-generating capabilities need to access these data quickly. Among other approaches, cloud computing provides content delivery networks, which utilize data replication for better latency to such real-time applications. Fast processing, storage, and timely analysis of these data are the challenge for most of these future Internet applications. However, cloud computing is the ultimate storage and processing paradigm to resolve such issues and to deal with big data, with the same speed in which data are being produced. Moreover, cloud computing technology has been evolved with the tag line of “Everything as A Service” now, and for powering all these services, provision of data is a compulsory and significant task. To provide end user with easy and fast access to data, the cloud maintains backup and replicates the copies in multiple data centers. The geographical locations of these data centers where data are being placed have a profound impact on data access time. To deal with the challenge of effective data replication and to reduce data access time to a minimum, we propose a genetic algorithm (GA)-based technique to suggest and store data on nearly located data centers. The proposed algorithm improves the access time, and thus, the efficiency of cloud servers by providing quality of service (QoS) to end user.

Random Versus Copyset Placement: Data-Loss Models for Proactive-Tolerance Replica-Based Data Storage

In a distributed storage system, proactive fault tolerance is used to provide an extra layer of data protection over traditional reactive fault tolerance. The placement of replicas across storage servers in a replication-based system affects both rebuild times and the vulnerability of data to device failures, and therefore influences system reliability. The effects of proactive fault tolerance and data placement on the reliability of distributed storage systems are intricate and understudied. This article proposes reliability equations for predicting the number of data loss in proactive replication-based systems using random and copyset placement schemes, over a period of time. The reliability equations represent the effect of proactive fault tolerance, disk-operation failures, server-operation failures, rebuild bandwidths, and replica placement schemes upon the systems reliability, and take time-based Weibull distributions to model failure and repair processes. Moreover, a Monte-Carlo based method is designed to simulate the operation of proactive distributed storage systems. The results of the reliability equations are in good accord with those of the simulations, which verifies the correctness of the equations. The proposed equations can help system designers readily optimize tradeoffs and compare schemes, facilitating distributed storage systems design.

Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks

Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios.

A Comparative Study of Services Placement Algorithms in Future Networks

The study aimed to analyze and compare several algorithms in the context of networks services placement, and then proposed a self-organized dynamic heuristic algorithm adaptable to continually changing network conditions in order to achieve the ideal placement of services replicas in future networks. It is known that future networks demand a high degree of self-organization to keep pace with ongoing changes while maintaining performance optimized. One of the important challenges in this context is the services placement problem. Service placement issue refers to the selection of the most appropriate network node for hosting a service. The ideal placement of services replicas reduces the cost of serving customers, improves connectivity between clients and servers as well as the use of available resources. The study summarized the results of qualitative comparison between several placement algorithms and refers to the most important requirements to be taken into account when implementing the placement algorithm. Generally, each service has its own placement technique, and the action taken by a specific service may affect other services decisions and force them to adapt. There is an urgent need to a management service for managing services replicas to make the optimal placement decision. This service should work in a distributed manner and does not require comprehensive knowledge about the network. It is also characterized by its ability to adapt to changing network conditions in terms of load and topology. Other services coordinate with the management service about replicating or migrating actions, thus services will be offered at a minimized cost.