Service Placement Research Articles

Resource Orchestration at the Edge: Intelligent Management of mmWave RAN and Gaming Application QoE Enhancement

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers would impose a heavy burden on the management of the radio access network (RAN). This challenge increases the need for leveraging intelligent network management techniques. Thanks to edge computing, machine learning (ML) based network management algorithms and other delay-sensitive user applications can operate at the network edge. But, due to the limited resources on edge servers, developing an orchestration scheme for intelligent network management and user applications is necessary. In this paper, we provide an edge-centric resource management framework for intelligent RAN management and applications with the awareness of the users’ quality of experiences (QoE). Specifically, we consider the scenario of a mmWave communication system equipped with an ML-based mmWave beam tracking algorithm. The users under this system request mobile edge gaming services. We formulate a game QoE aware orchestration problem as a non-linear integer programming and prove its NP-hardness. To reduce the complexity, we decompose the original problem into two subproblems, the service placement problem for mobile edge gaming and the configuration selection and placement problem for mmWave beam tracking. Then, we solve the two subproblems consecutively with heuristic approaches. Simulation results demonstrate the effectiveness of the proposed orchestration scheme.

Security-Aware and Time-Guaranteed Service Placement in Edge Clouds

Most of the emerging applications such as the Deep Neural Networks (DNN) based smart Internet of Things (IoT) systems need intensive and high-performance computing, which is contradictory to the limited resources of IoT/terminal devices. It is a big challenge to offload all tasks to the cloud due to the bandwidth limitation, processing overhead, and transmission costs. Edge computing as an extension of cloud computing that can provide abundant computing resources near the edge of the network and thereby can potentially improve the QoS of applications. However, offloading tasks to the edge servers is liable to external security threats. How to balance the response time and the security of application services is a big challenge for realizing good application service placement. This paper proposes a time and security efficient task scheduling framework in the edge-cloud environment. The corresponding computing models are established, such as the security-related model, time model, and the risk probability model. Then, a time-guaranteed and security-aware task scheduling algorithm is proposed including the domain construction, the security-aware task ranking, and the task dispatching. Extensive simulation experiments have been conducted. Results show that the proposed method has better performance than the other four compared methods in general.

An efficient and autonomous scheme for solving IoT service placement problem using the improved Archimedes optimization algorithm

An efficient and autonomous scheme for solving IoT service placement problem using the improved Archimedes optimization algorithm

Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.

Lyapunov-guided optimal service placement in vehicular edge computing

Vehicular Edge Computing (VEC) brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles. However, the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests, thus restricting the performance of VEC system. Besides, a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server. To strengthen the performance of VEC, in this paper we propose to place services beforehand at the edge server, e.g., by deploying the services/tasks-oriented data (e.g., related libraries and databases) in advance at the network edge, instead of downloading them from the remote data center or offloading them from vehicles during the runtime. In this paper, we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline. Specifically, the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization. Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution. The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.

Resource Allocation in Fog Computing: A Systematic Review

Despite the importance of resource allocation issues, there is no systematic, comprehensive and detailed survey on resource allocation approaches in the fog-computing context. In this article, we provide a Systematic Literature Review (SLR) on the resource allocation approaches in fog environments in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and provide open issues. The presented taxonomy is categorized into three main fields: centralized allocation, decentralized allocation, and integrated allocation (published between 2017 and March 2022). According to what is known in fog computing, load balancing and service placement are among the most important basic parameters that ensure service quality. These fields are classified into four methods, approximate, exact, fundamental, and hybrid. In addition, this article investigates resource allocation metrics with all advantages and limitations related to chosen resource allocation mechanisms in networks.

VioLinn: Proximity-aware Edge Placementwith Dynamic and Elastic Resource Provisioning

Deciding where to handle services and tasks, as well as provisioning an adequate amount of computing resources for this handling, is a main challenge of edge computing systems. Moreover, latency-sensitive services constrain the type and location of edge devices that can provide the needed resources. When available resources are scarce there is a possibility that some resource allocation requests are denied. In this work, we propose the VioLinn system to tackle the joint problems of task placement, service placement, and edge device provisioning. Dealing with latency-sensitive services is achieved through proximity-aware algorithms that ensure the tasks are handled close to the end-user. Moreover, the concept of spare edge device is introduced to handle sudden load variations in time and space without having to continuously overprovision. Several spare device selection algorithms are proposed with different cost/performance tradeoffs. Evaluations are performed both in a Kubernetes-based testbed and using simulations and show the benefit of using spare devices for handling localized load spikes with higher quality of service (QoS) and lower computing resource usage. The study of the different algorithms shows that it is possible to achieve this increase in QoS with different tradeoffs against cost and performance.

Problem issues of normative definition facility of unvoluntarily leaving a military unit or place of service

The article is devoted to the peculiarities of the normative definition object of the criminal offense provided for in Art. 407 Criminal of the Code of Ukraine "Arbitrary abandonment of a military unit or place services". It is noted that the generic object of criminal offenses against the established order of placement of military service is provided for by law, the procedure for entering or completing military service.
 The multifacetedness of normative and legal acts that determine the procedure for military service, the rights and obligations of servicemen is emphasized: laws of Ukraine, regulations on military service by relevant categories of servicemen, other normative and legal acts. The essence of military service as a state service of a special nature is analyzed, which consists in the professional activity of citizens of Ukraine suitable for it in terms of health and age (with the exception of cases specified by law), foreigners and stateless persons, related to the defense of Ukraine, its independence and territorial integrity.
 On the basis of the analysis of normative legal acts, it was determined that military duty is established for the purpose of preparing citizens of Ukraine for the defense of the Motherland, providing personnel for the Armed Forces of Ukraine, other military formations formed in accordance with the laws of Ukraine, as well as special law enforcement agencies, positions in which are staffed by military personnel.
 It has been proven that conscripts and reservists have passed training (or inspection) meetings. The legally fixed moments of the beginning and end of military service are indicated.
 Attention is focused on the need to recognize as a generic object the voluntary abandonment of a military unit or place of service (Article 407 of the Criminal Code of Ukraine) and the order of passing and the order of military service, since these concepts are organically interconnected. It was concluded that the term "military service" is a narrower concept than "military service".

Resilient Service Provisioning for Edge Computing

We study the problem of resilient service provisioning for edge computing (RSPE), i.e., how to determine a service placement strategy to maximize the expected overall utility by service provisioning, in the presence of uncertain service failures. RSPE is extremely challenging to tackle, because the explicit expression of its objective function is difficult to obtain, and it is a resilient max–min problem subject to knapsack constraints, which is unexplored so far and cannot be addressed by existing resilient optimization techniques. We first explore the potential properties of the implicit objective function, and reveal that it is monotone submodular under certain conditions. We further prove that the knapsack constraints form a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -independence system constraint, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q&gt;0$ </tex-math></inline-formula> is a constant related to the constraints. We propose two novel solutions for the general RSPE and homogeneous case, respectively. First, for the general problem, we propose a “two-step greedy” algorithm achieving a constant approximation ratio within polynomial time. Second, for the homogeneous case where one of the knapsack constraints reduces to a matroid constraint, we propose an improved “first-greedy-then-local search” polynomial time algorithm achieving better approximation ratio than the previous one. Both extensive simulations and field experiments validate the effectiveness of our proposed algorithms.

A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks

At present, multi-access edge computing is applied to “cloud-edge-terminal” collaborative computing, in which the task of computing offloading is used to unload the computing tasks of “terminal” to “edge”, i. e. edge servers, but the placement of edge servers is often not considered, resulting in low efficiency of computing offloading. Therefore, this paper proposes a multi-edge collaborative task unloading strategy for the effective placement of edge servers in 5G metropolitan area networks (MAN). First, the strategy weights the average user delay, the average edge server load and the average edge server resource utilization, and improves the firefly algorithm (FA) to optimize the number and effective location of edge servers. Secondly, a multi-edge collaborative task unloading architecture is presented. The computing tasks in this architecture can be executed locally, on a local server, on a remote server or in the “cloud”. The delay and energy consumption of the four unloading modes are modeled respectively. Thirdly, a new variable, i. e. the maximum cooperation cost that the “terminal” can bear, is introduced to attract more remote edge servers to cooperate to complete the calculation of the “terminal” task. Furthermore, the immune particle swarm optimization (IPSO) algorithm was designed to solve the problems of the traditional PSO algorithm. The simulation results show that the improved firefly algorithm can find the optimal coordinates of the edge server, and then carry out the task unloading of the multi-edge server. Compared with the local offload strategy, immune algorithm (IA) and PSO, the total cost of the proposed IPSO algorithm is reduced by 66.7%, 54% and 45.5%, respectively. Therefore, the algorithm in this paper can improve the execution efficiency of computing tasks and effectively reduce the total cost of the whole system.

Service Function Chains multi-resource orchestration in Virtual Mobile Edge Computing

Next-Generation Networks (NGNs) provide efficient services to improve the overall network performance, especially in Internet of Things (IoT) networks. However, service placement requires a significant cost in terms of multi-resource chaining guarantees. Therefore, the balance between improving network performance and optimizing resources orchestration is still a fundamental issue to assure the benefits of NGNs with optimal resources’ utilization. In this paper, we present new approaches for Service Function Chain (SFC) orchestration in Virtual Mobile Edge Computing (VMEC) environments, where IoT devices are used as on-demand virtual edge servers. We propose Optimal SFC Placement in VMEC over AI-IoT (OSPV) algorithm to select the optimal virtual mobile edge (VME) according to heterogeneous computing resources constraints. Moreover, to deal with OSPV complexity issues in dense IoT networks, we propose an Efficient SFC Placement in VMEC over AI-IoT (ESPV) algorithm that selects sufficient VME nodes for services operations and deployments. Furthermore, recent Deep Learning (DL) techniques such LSTM and GRU are implied to predict mobility and energy consumption sequences of IoT devices. These instances introduce feasible sets of IoT nodes, where the optimization algorithms should operate. Results show the efficiency of the DL instances and prove that the prediction awareness prevents the selected VME from failure and disconnection during the communication. Moreover, the proposed optimization algorithms are implemented and evaluated under different computing scenarios. Then, they are compared according to total allocated servers and SFC placement time. Optimization results show that both algorithms are efficient in terms of predetermined key performance indicators compared to the state of the art.

Mobile Edge Computing Architecture Challenges, Applications, and Future Directions

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 processing.

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multiaccess Edge Computing

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.

Гидравлический расчёт регулирующего сооружения, расположенного в ильменной части Прикаспийской низменности

Purpose: to solve the problem of conservation and reproduction of fish stocks of valuable commercial fish species in natural reservoirs, on the example of performing one of the tasks of developing a facility for regulating water regime of the ilmen part of the Volga delta. Using the well-known formulas of hydraulics, scientific developments of scientists in the field of hydraulic engineering and a priori information, it is necessary to determine the main parameters of a control structure located on a fishery reservoir in Astrakhan region. Materials and methods: a stage-by-stage hydraulic calculation of a tubular control structure that performs the functions of a water outlet was performed: the carrying capacity of the facility during operation in a pressure mode was determined; the main loads influencing the structure underground contour were determined by filtration calculation; a strength calculation of the floor for shear was performed. Results. A facility to regulate the water regime of the ilmen area of the Volga delta, ilmen, which are places of natural spawning grounds for commercial fish in Astrakhan region has been developed. The task of hydraulic calculation is to determine the conditions for the advancement of the filtration flow under the structure determining the pressure at characteristic points and to define the minimum dimensions of the floor that perceive stably the loads created by this flow. Conclusions. The calculation determined the main dimensions of the floor (length, width of the structure – 46.0; 4.65 m; length, thickness of the apron – 4.54; 0.3 m; length, thickness of the water apron – 4.8; 0.9 m; depth, water apron teeth width – 1.19; 0.40 m; length, thickness of the rear apron – 20.36; 0.6 m; length of the tubular part of the regulator – 16.3 m) taking into account the placement of gates, service and road bridges. The shear stability of the floor and the structure as a whole was determined.

DEVELOPMENT OF A CORPORATE EDUCATIONAL INFORMATION SYSTEM WITH THE HELP OF MACHINE LEARNING METHODS AND METHODS OF ENSURING INFORMATION SECURITY

The article presents the concept and technical aspects of the development of a corporate educational information system using machine learning methods and modern approaches to ensuring information security. The proposed solution is aimed at educational institutions and provides adaptive management of the educational process, optimization of resources and increased information security. The main emphasis is on the development of the system architecture, which includes the efficient placement of servers, the use of containerization technology, system monitoring, version control, and the application of security protocols to protect data and the system as a whole. The use of two servers is proposed: one for basic computing tasks, data storage and support of basic services, and the other for backup, monitoring and protection against unauthorized access. To increase the flexibility and scalability of the system, Docker containerization technology is used, which allows isolating processes and simplifies integration with cloud services. An important component is the integration of external monitoring, which provides control over the availability of services in real time. Emphasis is also placed on the use of reinforced firewalls and backup Internet connections to increase the system's resistance to cyber threats and cyber attacks. Functionality of the system includes an intuitive interface with a number of interactive widgets, such as information about recently registered courses, uploading learning materials, an integrated calendar, displaying performance graphs and task lists. Users can quickly receive information about their progress and plan the next steps in the educational process. An important decision was to abandon the creation of our own corporate chat in favor of implementing ready-made solutions with a high level of security and support for teamwork. This allows you to significantly optimize the use of resources and focus on the development of the main functions of the system. In addition, the use of version control systems for effective project management is proposed, which allows isolating different stages of development and minimizing the risk of errors. The proposed concept of the corporate educational information system provides a comprehensive approach to the management of the educational process taking into account the requirements of modern technologies, increases the effectiveness of education and guarantees a high level of data security.

Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

Communication-Affinity Aware Colocation and Merging of Containers

Microservice architecture relies on message passing between services. Inter-service communication introduces an overhead to the applications’ overall performance. This overhead depends on the runtime placement of the services bundled in containers and it can be reduced by intelligently deploying the containers by considering the communication affinities between services. Researchers have attempted to colocate microservices and merge containers based on affinities. However, container merging has not been considered up to now. This study shows that the problem of service placement in a microservice application considering communication affinities, constrained by computational resources, can be mapped to an instance of the Binary Knapsack Problem. We propose a container colocation and merging mechanism based on a heuristic solution to the Binary Knapsack Problem. The proposed approach reduced the communication overhead of a benchmark application by 58.5% and as a result, execution time was reduced by approximately 13.4% as well.

Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm

The number of Internet of Things (IoT)-based applications is constantly increasing, and transferring all their associated data to a remote centralized cloud requires more latency, energy, bandwidth, and cost. In such cases, the fog layer as a new computing paradigm supports resource-constrained IoT devices. Fog computing equipment at the network edge can allocate its resources to process real-time IoT applications. IoT application placement mechanisms in the fog environment have been developed to address these issues. According to microservice architecture, subordinate services of IoT applications can be independently deployed on fog servers. Hence, optimal utilization of fog resources is of great importance to satisfy the Quality of Service (QoS) and requires a distributed and autonomous mechanism to solve the Service Placement Problem (SPP) in fog. Motivated by the generalizability shortcomings of existing approaches, we use the Asynchronous Advantage Actor-Critic (A3C) algorithm as a new Deep Reinforcement Learning (DRL) approach to solve SPP. The proposed scheme focuses on the placement of IoT services with the objectives of minimizing cost and latency under deadline and resource constraints. According to these objectives, A3C seeks to maximize the long-term cumulative reward for improving QoS. We perform placement on local fog domains and use neighboring fog domains when needed to improve fog utilization. In addition, a resource distribution extraction technique over time is considered to save more resources to handle future requests. The simulation results show that our mechanism significantly improves the cost and latency compared to its counterparts such as DDQL and IMPALA.

Multimodal Optimization of Edge Server Placement Considering System Response Time

Mobile edge computing (MEC)deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study themultimodal optimization problem of edge server placement (MESP)with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

Edge server placement for offloading real‐time statistics tasks of bus passenger

AbstractWith the continuous development of smart bus systems, higher requirements are expected for the accuracy and timeliness of the real‐time statistics of bus passengers. Although the statistical method of image and video processing based on deep learning has higher accuracy, it has higher requirements on the computing power and hardware equipment of the computer. A traditional solution is cloud computing, but cloud computing cannot meet real‐time requirements due to long‐distance transmission. In order to meet the real‐time demand, it can be offloaded to the edge of the network and processed by edge servers. In edge computing, the location of the edge server will have a great impact on the access delay and the traffic load in the edge network. Currently, few people optimize the traffic load in the edge network during the placement process. In view of this, an edge server placement algorithm for task offloading, named ESPTO, is designed to balance the average delay and traffic load under the control of each edge server while minimizing the average delay and traffic load in the edge network. First, a decomposition‐based multi‐objective evolutionary algorithm (MOEA/D) is used to find a better set of placement strategies, and then the optimal placement strategy is obtained through TOPSIS. Experimental results based on the Hangzhou bus station dataset prove the effectiveness of ESPTO.