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{"work":{"id":126959405,"created_at":"2025-01-11T06:32:38.652-08:00","from_world_paper_id":262537268,"updated_at":"2025-01-30T00:57:21.301-08:00","_data":{"publisher":"IEEE Computer Society","grobid_abstract":"All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to enrich demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.","publication_date":"2020,3,1","publication_name":"IEEE Transactions on Big Data","grobid_abstract_attachment_id":"120761994"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"From Megabits to CPU Ticks: Enriching a Demand Trace in the Age of MEC","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [32298185]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":120761994,"attachmentType":"pdf"}"><img alt="First page of “From Megabits to CPU Ticks: Enriching a Demand Trace in the Age of MEC”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/120761994/mini_magick20250111-1-xzrto8.png?1736605962" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">From Megabits to CPU Ticks: Enriching a Demand Trace in the Age of MEC</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="32298185" href="https://huji.academia.edu/ScottKirkpatrick"><img alt="Profile image of Scott Kirkpatrick" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Scott Kirkpatrick</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2020, IEEE Transactions on Big Data</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 126959405; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to enrich demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":120761994,"attachmentType":"pdf","workUrl":"https://www.academia.edu/126959405/From_Megabits_to_CPU_Ticks_Enriching_a_Demand_Trace_in_the_Age_of_MEC"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":120761994,"attachmentType":"pdf","workUrl":"https://www.academia.edu/126959405/From_Megabits_to_CPU_Ticks_Enriching_a_Demand_Trace_in_the_Age_of_MEC"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="126959405" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"MECPerf: An Application-Level Tool for Estimating the Network Performance in Edge Computing Environments","attachmentId":91692143,"attachmentType":"pdf","work_url":"https://www.academia.edu/87502118/MECPerf_An_Application_Level_Tool_for_Estimating_the_Network_Performance_in_Edge_Computing_Environments","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87502118/MECPerf_An_Application_Level_Tool_for_Estimating_the_Network_Performance_in_Edge_Computing_Environments"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="112502926" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/112502926/Performance_and_Data_Traffic_Analysis_of_Mobile_Cloud_Environments">Performance and Data Traffic Analysis of Mobile Cloud Environments</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="857655" href="https://modcs.academia.edu/PauloMaciel">Paulo Maciel</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)</p><p class="ds-related-work--abstract ds2-5-body-sm">Mobile Cloud Computing (MCC) is a technique for increasing the performance of mobile apps and reducing their energy consumption through code and data offloading. Building an MCC infrastructure is a difficult task due to its inherent complexity and the involvement of different components. This paper proposes an approach for estimating applications' performance and data traffic volume generated by tasks offloading. This work proposes a Stochastic Petri Net (SPN)-based formal framework to represent the partitioning of applications in a method-call level. Our framework considers the available network bandwidth to send and receive tasks to the cloud. The modeling strategy represents the use and sharing of the actual available bandwidth for offloading operations. The approach enables designers to plan and tune MCC architectures based on Mean Time to Execute (MTTE) and Throughput estimation. Using our strategy it is possible to estimate the impact of the bandwidth variation on the application's MTTE and Throughput. In addition, the strategies proposed in this work may be adapted to support MCC applications in real time providing on-thefly probabilistic performance predictions. One case study was performed to evaluate the approach. Our proposed approach has proven to be feasible and it highlights the most appropriate strategies for offloading.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Performance and Data Traffic Analysis of Mobile Cloud Environments","attachmentId":109713668,"attachmentType":"pdf","work_url":"https://www.academia.edu/112502926/Performance_and_Data_Traffic_Analysis_of_Mobile_Cloud_Environments","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/112502926/Performance_and_Data_Traffic_Analysis_of_Mobile_Cloud_Environments"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="127213015" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/127213015/Offloading_computing_tasks_beyond_the_edge_A_data_driven_analysis">Offloading computing tasks beyond the edge: A data-driven analysis</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="330299645" href="https://independent.academia.edu/SadiaKhizar1">Sadia Khizar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021 13th IFIP Wireless and Mobile Networking Conference (WMNC), 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Mobile devices have always been left out of the network infrastructure due to their limited capacities. However, it turns out that they are becoming more and more sophisticated and, above all, increasingly numerous. Given their ubiquity, they offer untapped resources to extend the computing capacity of the MEC (multi-access edge computing). Nevertheless, such resources vary over time due to the dynamics of the network. In this paper, we investigate how the mobility of nodes impacts the task offloading process. To this end, we use traces of actual user equipment (UE) mobility from a cellular operator. We also quantify the impact of task duration and completion delay. Our results show that mobile nodes' offloading potential beyond the edge is significant, even for short delays.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Offloading computing tasks beyond the edge: A data-driven analysis","attachmentId":120979829,"attachmentType":"pdf","work_url":"https://www.academia.edu/127213015/Offloading_computing_tasks_beyond_the_edge_A_data_driven_analysis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/127213015/Offloading_computing_tasks_beyond_the_edge_A_data_driven_analysis"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="104613128" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/104613128/Mobile_Edge_Computing_Architecture_Challenges_Applications_and_Future_Directions">Mobile Edge Computing Architecture Challenges, Applications, and Future Directions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="148992202" href="https://independent.academia.edu/HemalathaIndukuri">Hemalatha Indukuri</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Grid and High Performance Computing</p><p class="ds-related-work--abstract ds2-5-body-sm">In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and pr...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Mobile Edge Computing Architecture Challenges, Applications, and Future Directions","attachmentId":104295371,"attachmentType":"pdf","work_url":"https://www.academia.edu/104613128/Mobile_Edge_Computing_Architecture_Challenges_Applications_and_Future_Directions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/104613128/Mobile_Edge_Computing_Architecture_Challenges_Applications_and_Future_Directions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="22180688" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22180688/Dynamic_Edge_Caching_for_Mobile_Users_Minimising_Inter_AS_traffic_by_Moving_Cloud_Services_and_VMs">Dynamic Edge-Caching for Mobile Users: Minimising Inter-AS traffic by Moving Cloud Services and VMs</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6418784" href="https://independent.academia.edu/JonathanLoo12">Jonathan Loo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014 28th International Conference on Advanced Information Networking and Applications Workshops, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, Cloud technology has revolutionized the way services are delivered to end-users. The advent of truly mobile computing in the form of smartphones and tablets has also driven the demand for Cloud resources in order to compensate for the inherent lack of local resources on these devices. Furthermore, modern mobile devices are equipped with multiple network interfaces and in combination with the rapid deployment of wireless networks, it is expected that they will always have Internet connectivity and access to Cloud resources. In this paper we will focus on traffic management for interactive multimedia services accessed by a mobile user by means of dynamic migration of a Virtual</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Dynamic Edge-Caching for Mobile Users: Minimising Inter-AS traffic by Moving Cloud Services and VMs","attachmentId":42839474,"attachmentType":"pdf","work_url":"https://www.academia.edu/22180688/Dynamic_Edge_Caching_for_Mobile_Users_Minimising_Inter_AS_traffic_by_Moving_Cloud_Services_and_VMs","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22180688/Dynamic_Edge_Caching_for_Mobile_Users_Minimising_Inter_AS_traffic_by_Moving_Cloud_Services_and_VMs"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="81759017" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/81759017/A_Comprehensive_Utility_Function_for_Resource_Allocation_in_Mobile_Edge_Computing">A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="101203888" href="https://giki.academia.edu/ZaiwarAli">Zaiwar Ali</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computers, Materials & Continua, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing","attachmentId":87692441,"attachmentType":"pdf","work_url":"https://www.academia.edu/81759017/A_Comprehensive_Utility_Function_for_Resource_Allocation_in_Mobile_Edge_Computing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/81759017/A_Comprehensive_Utility_Function_for_Resource_Allocation_in_Mobile_Edge_Computing"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="90709899" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/90709899/Dynamic_Allocation_of_Computing_and_Communication_Resources_in_Multi_Access_Edge_Computing_for_Mobile_Users">Dynamic Allocation of Computing and Communication Resources in Multi-Access Edge Computing for Mobile Users</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="54215021" href="https://independent.academia.edu/EmilioCalvaneseStrinati">Emilio Calvanese Strinati</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Network and Service Management, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users' movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Dynamic Allocation of Computing and Communication Resources in Multi-Access Edge Computing for Mobile Users","attachmentId":94197492,"attachmentType":"pdf","work_url":"https://www.academia.edu/90709899/Dynamic_Allocation_of_Computing_and_Communication_Resources_in_Multi_Access_Edge_Computing_for_Mobile_Users","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/90709899/Dynamic_Allocation_of_Computing_and_Communication_Resources_in_Multi_Access_Edge_Computing_for_Mobile_Users"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="93015254" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/93015254/LightEdge_Mapping_the_Evolution_of_Multi_Access_Edge_Computing_in_Cellular_Networks">LightEdge: Mapping the Evolution of Multi-Access Edge Computing in Cellular Networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="250139612" href="https://independent.academia.edu/YousafZarrar">Zarrar Yousaf</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Communications Magazine, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">The Multi-access Edge Computing (MEC) paradigm calls for a distribution of computational capacity at the network's edges. Albeit MEC will play a key role in future 5G deployments, it will take some time until the existing 4G networks evolve into a full 5G system. A challenge exists to devise a transition mechanism that allows MEC features to be seamlessly integrated in the current 4G networks. This article introduces a lightweight, ETSI-compliant MEC solution for 4G and 5G networks. The proposed solution, which we name LightEdge, has the main goal of immediately making available the features and capabilities of edge clouds to the mobile users. This article reports on the design and implementation of LightEdge and on its evaluation in a practical latency-sensitive use case.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"LightEdge: Mapping the Evolution of Multi-Access Edge Computing in Cellular Networks","attachmentId":95870246,"attachmentType":"pdf","work_url":"https://www.academia.edu/93015254/LightEdge_Mapping_the_Evolution_of_Multi_Access_Edge_Computing_in_Cellular_Networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/93015254/LightEdge_Mapping_the_Evolution_of_Multi_Access_Edge_Computing_in_Cellular_Networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="94557385" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/94557385/Orchestration_of_MEC_Computation_Jobs_and_Energy_Consumption_Challenges_in_5G_and_Beyond">Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2926392" href="https://nti.academia.edu/DrHeshamElBadawy">Dr.Hesham ElBadawy</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Access, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and non-collaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers' workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers ES s. We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost. INDEX TERMS MEC, IT, 5G, OPEX (operating expense), edge servers.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond","attachmentId":96980617,"attachmentType":"pdf","work_url":"https://www.academia.edu/94557385/Orchestration_of_MEC_Computation_Jobs_and_Energy_Consumption_Challenges_in_5G_and_Beyond","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/94557385/Orchestration_of_MEC_Computation_Jobs_and_Energy_Consumption_Challenges_in_5G_and_Beyond"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="83203498" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83203498/Edge_Capacity_Planning_for_Real_Time_Compute_Intensive_Applications">Edge Capacity Planning for Real Time Compute-Intensive Applications</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42165766" href="https://independent.academia.edu/YumingJiang">Yuming Jiang</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019 IEEE International Conference on Fog Computing (ICFC), 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Cloud computing is a major breakthrough in enabling multiuser scalable web services, process offloading and infrastructure cost savings. However, public clouds impose high network latency which became a bottleneck for real time applications, such as mobile augmented reality applications. A widely accepted solution is to move latency sensitive services from the centralized cloud to the edge of the internet, close to service users. An important prerequisite for deploying applications at the edge is determining initial required edge capacity. However, little has been done to provide reliable estimates of required computing capacity under Quality-of-Service (QoS) constraints. Differently from previous works that focus only on applications' CPU usage, in this paper, we propose a novel, queuing theory based edge capacity planning solution for real-time computeintensive applications that takes into account usage of both CPU and GPU. Our solution satisfies the QoS requirements in terms of response delays, while minimizing the number of required edge computing nodes, assuming that the nodes are with fixed CPU/GPU capacity. We demonstrate the applicability and accuracy of our solution through extensive evaluation, including a case study using real-life applications. The results show that our solution maximizes the resource utilization through intelligent combinations of service requests, and can accurately estimate the minimal amount of CPU and GPU capacity required for satisfying the QoS requirements.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Edge Capacity Planning for Real Time Compute-Intensive Applications","attachmentId":88630113,"attachmentType":"pdf","work_url":"https://www.academia.edu/83203498/Edge_Capacity_Planning_for_Real_Time_Compute_Intensive_Applications","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83203498/Edge_Capacity_Planning_for_Real_Time_Compute_Intensive_Applications"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":120761994,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":120761994,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_120761994" style="display: none"><div class="scribd--being-converted-container">This document is currently being converted. Please check back in a few minutes.</div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="126959408" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/126959408/How_Close_to_the_Edge_Delay_utilization_tradeoffs_in_MEC">How Close to the Edge? 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applications","attachmentId":110966277,"attachmentType":"pdf","work_url":"https://www.academia.edu/114208362/Towards_the_optimization_of_power_and_bandwidth_consumption_in_mobile_cloud_hybrid_applications","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/114208362/Towards_the_optimization_of_power_and_bandwidth_consumption_in_mobile_cloud_hybrid_applications"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="110372791" data-sort-order="default"><a class="ds-related-work--title 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