Fig 2 - uploaded by Lauri Lovén
Content may be subject to copyright.
Connected users on the first week of Sept. 2014 at one of the panOULU access points.

Connected users on the first week of Sept. 2014 at one of the panOULU access points.

Source publication
Conference Paper
Full-text available
In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Expe...

Contexts in source publication

Context 1
... connected user is assumed to introduce a workload of one to an AP and the edge server that AP is connected to. The number of concurrently connected users for one busy AP of a local polytechnic on the first week of September is depicted in Fig. 2. To make sure the edge servers have capacity for even the peak hours, we choose the highest number of concurrent users in 2014 for each AP as the relative workload the APs introduce on the edge server. Fig. 3 shows that the AP workloads are distributed roughly exponentially, with a small number of high workloads and a fat tail of low ...
Context 2
... connected user is assumed to introduce a workload of one to an AP and the edge server that AP is connected to. The number of concurrently connected users for one busy AP of a local polytechnic on the first week of September is depicted in Fig. 2. To make sure the edge servers have capacity for even the peak hours, we choose the highest number of concurrent users in 2014 for each AP as the relative workload the APs introduce on the edge server. Fig. 3 shows that the AP workloads are distributed roughly exponentially, with a small number of high workloads and a fat tail of low ...

Similar publications

Conference Paper
Full-text available
Mobile devices are increasingly becoming an essential part of human life as the most effective and convenient communication tools not bounded by time and place. Mobile cloud Computing have emerged out in IT industry since last decade. Cloud Computing imitate vast experience of various services from mobile applications which run on devices and remot...

Citations

... An edge server is a server on the edges of the network [29]; it is located where the corresponding function is required and distributed processing is performed [30]. The edge server performs compute offloading, data storage, caching, and processing. ...
Article
Full-text available
Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method.
... However, edge computing architectures come with several already envisioned challenges, including computational optimization and physical placement of the edge servers in dynamic scenarios with mobile users [5,6]. Particularly load balancing has seen as a mission-critical challenge for any computing service from cloud to local networking capabilities [7]. ...
... In edge computing, workload management must, however, deal with user mobility and higher variance in server and network topologies and capacities, thus making it a distinct research topic. Workload management on the edge can be handled with different strategies, such as the physical placement of edge servers [5,12,36] or reallocating services on the softwareside with different optimization algorithms [18,37,38]. Reallocation can rely on known edge server features, such as capacity, or their current state, such as load or even price [39]. ...
... While the study considered the Wi-Fi deployment of one geographical area, our earlier studies [5,12] have shown the deployment is representative of an edge deployment spanning urban areas with a high AP density as well as suburban areas with a low AP density. ...
Article
Full-text available
Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summertime (ca. 4 times the risk than on average) and on weekends (ca. 1.5 times the risk). Furthermore, mass events such as popular sports games incurred a high risk (nearly 10 times that of the average) of a reallocation storm in a MEC-based scenario.
... They formulated the edge server placement problem as a multi-objective optimization model and took into account the usage and characteristics of the in-place backhaul network. Lovén et al. studied the edge server scaling up problem in which new edge servers are deployed based on the initial deployment and formulated this problem as a NP-hard optimization problem [28]. They proposed a method that selects the optimal number of new edge servers and their placement, and reallocates APs optimally to the old and new edge servers. ...
Article
Full-text available
In the evolution of Internet of Things and 5G networks, edge computing, as an emerging computing paradigm, can effectively reduce the latency of accessing the cloud service and enhance the computing power for resource-constrained user devices. However, in existing communication scenarios, there are still situations where the infrastructure coverage is limited or devices are not covered. At the same time, device location changes constantly due to users’ uncertain mobility. In response to such situations, mobile and flexible equipment combined with cloudlet is used to achieve mobile deployment of cloudlets and provides computing power support for user devices. In this paper, a dynamic cloudlet deployment method based on clustering algorithm (DCDM-CA) is proposed to solve the problem of deploying mobile cloudlets for mobile applications. DCDM-CA determines the cloudlet deployment destination based on the geographic location of multiple devices and the number of tasks generated by multiple devices in a unit time period. In addition, the task offloading is optimized after deploying cloudlets to minimize the system response latency. Extensive simulations reveal that DCDM-CA can efficiently deploy mobile cloudlets, and the system response latency is minimized through optimizing task offloading.
... include user location, computational and communication resources, and application data. In essence, edge resources must be placed [58]- [60] and their resources allocated [61] in a way that considers such factors and their trade-offs. ...
Article
Full-text available
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.
... As an example, in [179] the authors consider the problem of scaling up an edge computing deployment by selecting the optimal number of new MEHs and their placement and reallocating access points optimally to the old and new MEHs. In this case, the considered performance is the Quality of Experience of users and the QoS of the network operator. ...
Preprint
The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New mission-critical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.
... In general, the more people downloaded the app, the better the data for contact tracing and at the time of writing, there have been almost six million downloads-of course, not every download means the app will be active, and so, notifications are sent as reminders to have the app turned on when leaving the home. The app is based on proximity sensing using Bluetooth Low Energy which is equipped in most smartphones on the market-the protocol is called BlueTrace 42 and is based on Singapore's TraceTogether app. 43 There is also MIT's SafePaths contact tracing app which uses GPS and Bluetooth 44 -in this app, the location log data collected on the smartphone is stored on the phone, and leaves the device only when the user sends the information to a public health authority-this is a similar design principle to BlueTrace which aims to defer sending of the data from the phone to the authority until there has been a diagnosis of a COVID-19 case. ...
... [last accessed: 30/6/2020]. 42 BlueTrace is an open source application protocol; https://bluetrace.io/ [last accessed: 30/6/2020]. ...
... 96 IoT is not only about sensor networks city-scale or over vast geographical areas, but also very much sensing and analytics in personal spaces. The quantified self movement 100 encourages self-tracking and analytics of the self [36], e.g., via wearable IoT devices, and one can even apply such ideas to understand the human driver [42]. ...
Chapter
As illustrations of what constitutes the Automated City, this chapter highlights (among many) three types of technologies: (1) automated vehicles, (2) robots in indoor public spaces and outdoors (on city streets, e.g., cleaning robots, delivery robots, and other applications), and (3) drones (Unmanned Aerial Vehicles) in urban environments, discussing their potential and specific issues. Existing advancements and current limitations are highlighted, including technical challenges, human-machine interaction, and socio-technical issues including governance and safety for these three types of technologies.
... In general, the more people downloaded the app, the better the data for contact tracing and at the time of writing, there have been almost six million downloads-of course, not every download means the app will be active, and so, notifications are sent as reminders to have the app turned on when leaving the home. The app is based on proximity sensing using Bluetooth Low Energy which is equipped in most smartphones on the market-the protocol is called BlueTrace 42 and is based on Singapore's TraceTogether app. 43 There is also MIT's SafePaths contact tracing app which uses GPS and Bluetooth 44 -in this app, the location log data collected on the smartphone is stored on the phone, and leaves the device only when the user sends the information to a public health authority-this is a similar design principle to BlueTrace which aims to defer sending of the data from the phone to the authority until there has been a diagnosis of a COVID-19 case. ...
... [last accessed: 30/6/2020]. 42 BlueTrace is an open source application protocol; https://bluetrace.io/ [last accessed: 30/6/2020]. ...
... 96 IoT is not only about sensor networks city-scale or over vast geographical areas, but also very much sensing and analytics in personal spaces. The quantified self movement 100 encourages self-tracking and analytics of the self [36], e.g., via wearable IoT devices, and one can even apply such ideas to understand the human driver [42]. ...
Chapter
The previous chapter discussed particular issues in relation to Automated Vehicles, urban robots and urban drones. This chapter discusses visions, perspectives and challenges of the Automated City more generally, including aspirational visions of future cities, what must be overcome or addressed towards a favourable notion of the Automated City, and issues of governance, new business models, city transportation, sustainability, real-time tracking, urban edge computing, blockchain, technical challenges of cooperation, and trust, fairness and ethics in relation to AI and algorithms in the city—we elaborate on the last two aspects in more detail.
... In general, the more people downloaded the app, the better the data for contact tracing and at the time of writing, there have been almost six million downloads-of course, not every download means the app will be active, and so, notifications are sent as reminders to have the app turned on when leaving the home. The app is based on proximity sensing using Bluetooth Low Energy which is equipped in most smartphones on the market-the protocol is called BlueTrace 42 and is based on Singapore's TraceTogether app. 43 There is also MIT's SafePaths contact tracing app which uses GPS and Bluetooth 44 -in this app, the location log data collected on the smartphone is stored on the phone, and leaves the device only when the user sends the information to a public health authority-this is a similar design principle to BlueTrace which aims to defer sending of the data from the phone to the authority until there has been a diagnosis of a COVID-19 case. ...
... [last accessed: 30/6/2020]. 42 BlueTrace is an open source application protocol; https://bluetrace.io/ [last accessed: 30/6/2020]. ...
... 96 IoT is not only about sensor networks city-scale or over vast geographical areas, but also very much sensing and analytics in personal spaces. The quantified self movement 100 encourages self-tracking and analytics of the self [36], e.g., via wearable IoT devices, and one can even apply such ideas to understand the human driver [42]. ...
Chapter
This chapter reviews the notion (and visions) of the Automated City in popular press, and in research publications, and then attempts to outline a conceptualisation of the Automated City. We first discuss what form the Automated City can take, from a mainly technological perspective. But a city is really constituted by its human inhabitants. We then discuss the Automated City in relation to its inhabitants via metaphors as guiding lenses through which one can view and shape developments towards a vision of the humane Automated City.
... We employed a real-world large-scale Wi-Fi connection dataset for the simulation of workload in a number of edge computing scenarios. While the study considered the Wi-Fi deployment of one geographical area, earlier studies [11], [12] have shown the deployment is representative of an edge deployment spanning urban areas with a high AP density as well as sub-urban areas with a low AP density. Further, while the studied period was as early as 2013-2014, we augmented the data to accommodate potential future edge use cases with usage patterns ranging from predominantly short to predominantly long, sampling the actual task execution times and workloads from realistic distributions. ...
Conference Paper
Full-text available
Efficient service placement and workload allocation methods are necessary enablers for the actively studied topic of edge computing. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks – a phenomenon we present as an allocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based ona large real-world data set of city-wide Wi-Fi network connections in 2013–2014, with more than 47M connections over ca. 800 access points. We identify the conditions for avoiding the reallocation storm for three common edge-based reallocation strategies, and study the latency–workload trade-off related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of top ES workload. Further, while a reallocation strategy aiming to minimize reallocation distance consistently resulted in the worst reallocation storms, the two other strategies, namely, a random reallocation strategy, and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments.
... In comparison, hierarchical clustering aims to form a hierarchy between clusters, often of an agglomerative or a divisive type (Xiao and Yu, 2012). Partitional clustering can be used, for example, in image and signal segmentation (Das, Abraham and Konar, 2007), grouping customers in marketing research (MacGregor and Hodgkinson, 2006) and the optimal placement of edge servers in a city region Lovén et al., 2020;Ruha et al., 2021). Various other examples can be found in Nanda and Panda (2014) and Banerjee and Ghosh (2006). ...
... In addition, we thoroughly examine the effects of different extensions and their combinations using simulated data as well as two real world examples. We utilize the PACK method (PlAcement with Capacitated K-family) to implement these extensions in each example (Lähderanta et al., 2019;Lovén et al., 2020;Lähderanta et al., 2021). The first real world example is related to the placement of edge computing servers in Shanghai, a topic currently under active research, while the other is related to location intelligence, placing recycling centers in a town in Slovakia. ...
... In location-allocation, this is a typical setting, with new facilities being added to an existing network (see e.g. Rahman and Smith, 2000;Lovén et al., 2020). For clustering, such a variant may correspond e.g. to a setting where, based on prior information, it is known that there are clusters with centers in specific locations. ...
Preprint
Full-text available
Location-allocation and partitional spatial clustering both deal with spatial data, seemingly from different viewpoints. Partitional clustering analyses data points by partitioning them into separate groups, while location-allocation places facilities in locations that best meet the needs of demand points. However, both partitional clustering and location-allocation can be formulated as optimization problems minimizing the distances of (demand) points from their associated centers (facilities). Further, both techniques consider certain extensions such as weighted data points, different distance metrics, capacity constraints, different membership types, outliers, and selecting the number of clusters or facilities. In this article, we highlight and review the similarities and differences of these techniques, compare them with model-based clustering for further insight, and provide a unified theoretical framework covering both approaches. We look at a number of extensions common for both approaches, and consider their treatment under the framework. Finally, we provide a number of detailed examples highlighting the effects of extensions, using a clustering method proposed in our earlier work and the corresponding open-source tool which combines and implements the extensions as a publicly available R package, rpack.