[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128539-en":3,"doc-seo-128539-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128539,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning methods for service placement - a systematic review","Machine learning methods for service placement address the limits of relying on cloud computing alone for real-time, latency-sensitive Internet of Everything (IoE) applications. The review contrasts optimization and machine learning as cost-function minimizers, explaining why the NP-hard nature of service placement requires heuristic and metaheuristic approaches. It also provides a taxonomy of ML methods, reporting dominant use of distributed microservices and highlighting trends such as reinforcement learning and resource estimation.","Machine learning methods for service placement: a systematic review  \nParviz Keshavarz Haddadha1 · Mohammad Hossein Rezvani1 · Mahdi MollaMotalebi1 · Achyut Shankar2,3,4  \nAccepted: 20 December 2023 / Published online: 17 February 2024 © The Author(s) 2024  \nAbstract  \nWith the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research.  \nKeywords Internet of Everything (IoE) · Computing paradigms · Distributed microservices · Application perspective · Resource estimation  \nExtended author information available on the last page of the article  \n1 Introduction  \nService placement is the selection of an appropriate execution zone for service instances. In this regard, the service instances are mounted on the underlying computing resources of the network (Taheri-abed et al. 2023) . This is done according to various performance criteria such as Quality of Service (QoS), energy consumption, latency, availability, etc. Today, with the advent of the Internet of Everything (IoE), most services include latency-sensitive and computation-sensitive components (Zabihi et al. 2023) . Some of these important services include virtual reality, augmented reality, healthcare, museum monitoring, smart transportation, weather monitoring, e-health, and the Internet of Vehicles (IoV) (Gasmi et al. 2022) . A huge amount of data is expected to be collected by sensors/connected objects, which have previously been processed centrally by large data centers in traditional ways. These new services cause more latency and energy consumption than before (Alenazi et al. 2022) . The Mobile Cloud Computing (MCC) paradigm that emerged nearly two decades ago is not sufficient to fulfill these applications alone. The most important problem with MCC is the long delays due to the remoteness of the cloud data centers from the end devices. In response to this need,","cbCaikMd5CR09zSJ","https://ap.wps.com/l/cbCaikMd5CR09zSJ","pdf",6650463,3,1,64,"English","en",105,"# Abstract\n# Introduction\n## Motivation","[{\"question\":\"What is service placement in IoE-based networks?\",\"answer\":\"Service placement selects appropriate execution zones for service instances, based on criteria such as QoS, energy consumption, and latency. In IoE settings, it extends to placing service function chains (SFCs) across edge and cloud resources.\"},{\"question\":\"Why can classical optimization methods be insufficient for service placement?\",\"answer\":\"The document explains that service placement is NP-hard, making classical optimization inadequate under dynamic, complex conditions and ever-changing big data in IoE environments.\"},{\"question\":\"What machine learning trends and usage patterns are reported in the review?\",\"answer\":\"The systematic review reports that 96% of applications use distributed microservice architectures, while 51% of studies rely on on-demand resource estimation and 81% are multi-objective. Reinforcement learning is identified as a major trend, with a 56% research share.\"}]","Machine learning methods for service placement - a systematic review | PDF",1786001623,161,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-methods-for-service-placement-a-systematic-review","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-methods-for-service-placement-a-systematic-review/128539/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is service placement in IoE-based networks?","Question",{"text":76,"@type":77},"Service placement selects appropriate execution zones for service instances, based on criteria such as QoS, energy consumption, and latency. In IoE settings, it extends to placing service function chains (SFCs) across edge and cloud resources.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why can classical optimization methods be insufficient for service placement?",{"text":81,"@type":77},"The document explains that service placement is NP-hard, making classical optimization inadequate under dynamic, complex conditions and ever-changing big data in IoE environments.",{"name":83,"@type":74,"acceptedAnswer":84},"What machine learning trends and usage patterns are reported in the review?",{"text":85,"@type":77},"The systematic review reports that 96% of applications use distributed microservice architectures, while 51% of studies rely on on-demand resource estimation and 81% are multi-objective. 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