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Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge com...
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... allows fast scalability of resources in the face of volatile workloads caused by, for example, flash crowds. Figure 2 depicts the simplest form of cloud-based deployment, where transcoding and packaging operations are carried out in a cloud data center and directly delivered to the audience from there. As a result, transcoding and packaging operations enjoy seemingly unlimited resource capacity and elasticity of cloud data center. ...
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Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cac...
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... However, none of these works considers the interference among co-located tasks or intermittent availability of a subset of devices. Interference-based scheduling: A few efforts study the availability of heterogeneous edge devices and interference among tasks on the same edge device [9], [26], [27]. LaTS [9] proposed to use a Latency-CPU usage model to address the interference among co-located tasks. ...
... It constantly monitors the CPU usage on each edge device and combines with the Latency-CPU usage model to schedule tasks to get the minimum predicted latency. Moreover, [27] proposed a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes, but the drawback of such algorithm is that it requires sharing of monitoring information across all devices which is infeasible in edge computing. Compared to those frameworks, IBDASH considers the heterogeneity in edge computing and requires much less information sharing among all edge devices. ...
As we increase the number of personal computing devices that we carry (mobile devices, tablets, e-readers, and laptops) and these come equipped with increasing resources, there is a vast potential computation power that can be utilized from those devices. Edge computing promises to exploit these underlying computation resources closer to users to help run latency-sensitive applications such as augmented reality and video analytics. However, one key missing piece has been how to incorporate personally owned unmanaged devices into a usable edge computing system. The primary challenges arise due to the heterogeneity, lack of interference management, and unpredictable availability of such devices. In this paper we propose an orchestration framework IBDASH, which orchestrates application tasks on an edge system that comprises a mix of commercial and personal edge devices. IBDASH targets reducing both end-to-end latency of execution and probability of failure for applications that have dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH takes memory constraints of each edge device and network bandwidth into consideration. To assess the effectiveness of IBDASH, we run real application tasks on real edge devices with widely varying capabilities.We feed these measurements into a simulator that runs IBDASH at scale. Compared to three state-of-the-art edge orchestration schemes, LAVEA, Petrel, and LaTS, and two intuitive baselines, IBDASH reduces the end-to-end latency and probability of failure, by 14% and 41% on average respectively. The main takeaway from our work is that it is feasible to combine personal and commercial devices into a usable edge computing platform, one that delivers low latency and predictable and high availability.
... The architecture divides delay-sensitive video processing tasks in several sub-tasks, preprocessed by multiple Fog devices in parallel aiming for lower completion time. Aral et al. [12] considered Fog computing characteristics to improve the user experience for latency-sensitive video encoding. The service placement considers the network connectivity path and available bandwidth between the user device and the Cloud or Fog instances. ...
Video streaming is the dominating traffic in today's data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed in seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%-50% lower completion time and 5%-60% reduced total cost.
... Edge infrastructure is more dynamic and unstable than that of Cloud data centers and is characterized by more frequent node failures and network partitions [13]. Establishing optimal placement for time critical applications such as Internet of Things (IoT), data analytics, augmented reality and video streaming services is a complex task, that cannot be solved without considering network and communication related aspects [17], [18]. ...
... These attributes help dynamic service placement solutions to categorise mobile users and discover the service demand pattern of each category. Users locations are the next group of attributes [14,82,113,206,204,208,207,209,210] that are used to enhance the prediction of service request loads and the target area of the users' movement. The location information can be the cell where the user is located, the GPS information, or a tuple of (Position, Position error, Speed, Speed error, Direction, Direction error). ...
... A few works use a specific mobility model; for instance, smooth random mobility model [124], random walk [209] and nomadic mobility model [167] are used in the literature. Real-world mobility traces are also considered in several works, for instance, Shenzhen city taxi and metro dataset in [14], mobile users of Twidere dataset in [82,204], Rome taxi dataset in [199], Shanghai taxi trajectory dataset in [166], real-world checkins in [207], and mobility of flights in [182]. Finally, the authors in [160] use a synthetic dataset generated according to the region around the city of Köln. ...
... Data mining: Frequent pattern mining is a data mining [83,152,207,191,159,193,96] 81,187,156,105,118] ✓ ✓ ✓ -- [85,183,94,184,172,208,155,161,116,188,196,192,157,210,119,45] 117,124,180,189,84,175] ✓ --✓ ✓ [174,182] ✓ -✓ ✓ - [14,238,197,163] ✓ ✓ --- [114,168,93,113,169,201,23,167,190,95,209,162,46] ✓ --✓ - 150,205,86,115,13,22,170,87] ✓ ---- [10,194,153] -✓ --technique used in [206] to discover frequent movement patterns of mobile users by tracking their location, speed, and directions. Fuzzy logic: This method is mainly used to cope with the rapidly changing uncertain systems. ...
The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.
... A common problem of Smart City services in Edge Computing is the low latency and consequently, the drop of the Quality of Service in Cloud scenario [6,34]: authors designed a new and diverse approach to improve efficiency in collaboration for Smart City services, minimizing response time while optimizing energy consumption, and they achieve it with the proposed Intelligent Offloading Method. ...
... Addressing application latency requirements through edge scheduling. [6] (2019) Design a new and diverse approach to improve efficiency in collaboration for Smart City services, minimizing response time while optimizing energy consumption, and they achieve it with the proposed Intelligent Offloading Method. ...
In the last decade, the increasing number of devices connected to the internet has allowed the development of new technologies for distributed computing. IoT devices have reached computing capacities superior to personal computers of a few years ago, and their small size, low cost and lower energy consumption have made them fundamental for the new technological evolution. This paper analyzes and compares different solutions of serverless paradigms focused on dynamic behaviour adaptation, based on Function-as-a-Service (FaaS) framework. To allow a rapid interoperability of on-demand services, we propose a solution based on Blockchain, named BCB-FaaS, to ensure trustiness and accountability of function configuration, providing a strong barrier to well-known cyber-attacks. In addition, a cost analysis has been performed demonstrating how Blockchain can be an economic alternative to secure decentralized communications.
... is approach can help companies not only to consider costs as a whole, but also to reduce them significantly. By conducting an in-depth study of this, potential costs can be controlled more effectively [4]. e value chain can play a positive role in the sustainable operation of the enterprise, the market share of the enterprise, and the comprehensive strength of the enterprise. ...
Cost management is an important part of enterprise management and is fundamental to the survival and development of enterprises. Cost management is of great significance to promote production and cost-saving, strengthen cost accounting, improve management, and enhance the overall management level of enterprises. In this paper, different factors affecting mapping are combined with a group intelligence algorithm to study virtual network mapping, which dissects enterprise cost management in a new perspective and analyzes the relationship between enterprises in each supply chain and the association between the processes of each link in the supply chain. The algorithm uses node CPU, node degree, and neighboring bandwidth resources as advisors’ strategies, respectively, and customers consult multiple advisors in the process of constructing mapping solutions, and the advisors give suggestions to customers according to their strategies. CGS-VNE uses an advisor-guided search mechanism to ensure the diversity of mapping solutions, which can fully explore the solution space and derive high-quality enterprise costing solutions through iteration. Whether in evaluating enterprise cost control or enhancing enterprise cost control capability, the value chain theory can be practically applied to clarify the current situation of enterprise cost control under the premise of combining theory and practice and truly achieve the fundamental purpose of helping enterprises to fully implement cost control.
... In particular, the aim is to ensure that each scheduling decision is also made according to the topology of the application to be deployed, the communication patterns between microservices, the history related to the amount of traffic exchanged between them and the run-time network state, in terms of the communication latencies between cluster nodes. Run-time network conditions and application communication requirements are essential information to optimize container placement decisions in order to reduce end-to-end latencies experimented by end users (Aral et al., 2019). ...
... On the one hand, solutions to the so-called edge-cloud problem have been proposed: how to dynamically dispatch jobs to a network of edge nodes based on the application requirements and the current load on the servers doing the computations [35], [14], [1], [7], thus realizing vertical offloading (as defined in Sec. I). ...
Serverless computing is becoming widely adopted among cloud providers, thus making increasingly popular the Function-as-a-Service (FaaS) programming model, where the developers realize services by packaging sequences of stateless function calls. The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edge computing systems, which are expected to be typical for Internet of Things (IoT) applications. In this paper, we fill this gap by proposing a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize the response times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available. Our solution is shown to be simple enough to be installed on devices with limited computational capabilities, such as IoT gateways, especially when using a hierarchical forwarding extension. We evaluate the proposed platform by means of extensive emulation experiments with a prototype implementation in realistic conditions. The results show that it is able to smoothly adapt to the mobility of clients and to the variations of their service request patterns, while coping promptly with network congestion.
... Consistency of the wireless access. Many networked services (such as content delivery, live video analytics, etc.) care more about consistent than absolute latencies to deliver optimal quality-ofexperience to their users [6]. Most of these applications usually employ device buffers to handle long delays, which can react negatively to sudden latency peaks [54]. ...
Cloud computing has seen continuous growth over the last decade. The recent rise in popularity of next-generation applications brings forth the question: "Can current cloud infrastructure support the low latency requirements of such apps?" Specifically, the interplay of wireless last-mile and investments of cloud operators in setting up direct peering agreements with ISPs globally to current cloud reachability and latency has remained largely unexplored. This paper investigates the state of end-user to cloud connectivity over wireless media through extensive measurements over six months. We leverage 115,000 wireless probes on the Speedchecker platform and 195 cloud regions from 9 well-established cloud providers. We evaluate the suitability of current cloud infrastructure to meet the needs of emerging applications and highlight various hindering pressure points. We also compare our results to a previous study over RIPE Atlas. Our key findings are: (i) the most impact on latency comes from the geographical distance to the datacenter; (ii) the choice of a measurement platform can significantly influence the results; (iii) wireless last-mile access contributes significantly to the overall latency, almost surpassing the impact of the geographical distance in many cases. We also observe that cloud providers with their own private network backbone and direct peering agreements with serving ISPs offer noticeable improvements in latency, especially in its consistency over longer distances.
... A scheduler takes into account several factors such as resource availability and application priority when making placement decisions. In geo-distributed computing environments, a scheduler needs to consider the location of resources and network conditions such as inter-cluster latency in addition to the usual considerations such as resource availability [122], [156]. Moreover, as computing resources in one particular location are often constrained, a scheduler should consider offloading or bursting the application replicas to neighboring resources when those resources are fully used. ...
Geo-distributed computing environments such as hybrid cloud, multi-cloud and Fog Computing need to be managed autonomously at large scales to improve resource utilization, maximize performance, and save costs. However, resource management in these geo-distributed computing environments is difficult due to wide geographical distributions, poor network conditions, heterogeneity of resources, and limited capacity. In this thesis, we address some of the resource management challenges using container technology. First, we present an experimental analysis of autoscaling in Kubernetes clusters at the container and Virtual Machine levels. Second, we propose a proportional controller to dynamically improve the stability of geo-distributed deployments at run-time in Kubernetes Federations. Finally, we develop a container orchestration framework for geo-distributed environments that offers policy-rich placement, autoscaling, bursting, network routing, and dynamic resource provisioning capabilities.
