Optimal Service Placement Research Articles

Optimized dynamic service placement for enhanced scheduling in fog-edge computing environments

The traditional cloud computing model struggles to efficiently handle the vast number of Internet of Things (IoT) services due to its centralized nature and physical distance from end-users. In contrast, edge and fog computing have emerged as promising solutions for supporting latency-sensitive IoT applications by distributing computational resources closer to the data source. However, these technologies are limited by their size and computational capacities, making optimal service placement a critical challenge. This paper addresses this challenge by introducing a dynamic and distributed service placement policy tailored for edge and fog environments. By leveraging the inherent advantages of proximity in fog and edge nodes, the proposed policy seeks to enhance service delivery efficiency, reduce latency, and improve resource utilization. The proposed method focuses on optimizing the placement of high-demand services closer to the data generation sources to enhance scheduling efficiency in fog computing environments. Our method is divided into three interconnected modules. The first module is the service type estimator, which is responsible for distributing services to appropriate nodes. Here, we use the Political Optimizer (PO) as a new metaheuristic algorithm for deploying IoT services. The second module is service dependency estimator, which manages service dependencies. Here, we load dependent services near the data using a path matrix based on the Warshall algorithm. Finally, the third module is resource demand scheduling, which estimates resource demand to facilitate optimal scheduling. Here, we use a popularity-based policy to manage resource demand and service execution scheduling. Implementation results demonstrate significant improvements over existing state-of-the-art policies, highlighting the efficacy of the proposed policy in enhancing service delivery within fog-edge computing frameworks.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

On Attack-Resilient Service Placement and Availability in Edge-Enabled IoV Networks

Achieving network resilience in terms of attack tolerance and service availability is critically important for Internet of Vehicles (IoV) networks where vehicles require assistance in sensitive and safety-critical applications like driving. It is particularly challenging in time-varying conditions of IoV traffic. In this paper, we study an attack-resilient optimal service placement problem to ensure disruption-free service availability to the users in edge-enabled IoV network. Our work aims to improve the user experience while minimizing the delay and simultaneously considering efficient utilization of limited edge resources. First, an optimal service placement is performed while considering traffic dynamicity and meeting the service requirements with the use of a deep reinforcement learning (DRL) framework. Next, an optimal secondary mapping and service recovery placements are performed to account for the attacks/failures at the edge. The use of DRL framework helps to adapt to dynamically varying IoV traffic and service demands. In this work, we develop three ILP models and use them in the DRL-based framework to provide attack-resilient service placement and ensure service availability with efficient network performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed approach.

An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms

Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time.

Survey on Service Placement, Provisioning, and Composition for Fog-Based IoT Systems

In the modern IoT era, time and resource critical applications are more prevalent. Though cloud computing enables fast processing of data and producing results, the time and network overhead are huge. Fog based solutions have proved to eliminate this overhead. However, establishing a scalable fog based architecture for IoT applications involves a number of challenges such as guaranteeing QoS, reducing operational costs etc. Hence a viable fog based architecture must involve optimal service placement facilitated by suitable schemes for resource estimation and reservation, QoS monitoring, data interchange and integration. This paper surveys various service placement and optimization approaches proposed in the literature and compares them. This paper also explores various components that are required for composing a viable fog based architecture and presents a representative fog provisioning framework. Relevance of modern technologies and tools such as RESTful architecture, SDN, Kafka, RabbitMQ, Zookeeper in establishing an efficient framework for fog based IoT applications is discussed

Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems

Leveraging recent advances on mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at the network edge. In this paper, we consider the AI service placement problem in a multi-user MEC system, where the access point (AP) places the most up-to-date AI program at user devices to enable local computing/task execution at the user side. To fully utilize the stringent wireless spectrum and edge computing resources, the AP sends the AI service program to a user only when enabling local computing at the user yields a better system performance. We formulate a mixed-integer non-linear programming (MINLP) problem to minimize the total computation time and energy consumption of all users by jointly optimizing the service placement (i.e., which users to receive the program) and resource allocation (on local CPU frequencies, uplink bandwidth, and edge CPU frequency). To tackle the MINLP problem, we derive analytical expressions to calculate the optimal resource allocation decisions with low complexity. This allows us to efficiently obtain the optimal service placement solution by search-based algorithms such as meta-heuristic or greedy search algorithms. To enhance the algorithm scalability in large-sized networks, we further propose an ADMM (alternating direction method of multipliers) based method to decompose the optimization problem into parallel tractable MINLP subproblems. The ADMM method eliminates the need of searching in a high-dimensional space for service placement decisions and thus has a low computational complexity that grows linearly with the number of users. Simulation results show that the proposed algorithms perform extremely close to the optimum and significantly outperform the other representative benchmark algorithms.

Optimal service placement using Pseudo service chaining mechanism for cloud-based multimedia services

Network function virtualization based service chaining has recently received significant attention. In cloud environment, virtual machine typically be scheduled to load balancing among computing nodes. Therefore, the relation between virtual machines is not considered when it is scheduled. However, the location of virtual machines be able to affects network performance for intra virtual machines. Video/image processing and transmission are key processes for cloud-based over-the-top content provider. In this paper, we propose a pseudo service chaining mechanism for efficiently allocating computing nodes in a service chain to guarantee quality of services. The mechanism defines a pseudo service chain to reduce the network overhead which mainly causes performance degradation and places highly correlated virtual machines on the nearest computing node. The performance of proposed mechanism was evaluated on real cloud testbed.

A Pilot Study Using Mixed GPS/Narrative Interview Methods to Understand Geospatial Behavior in Homeless Populations.

Tracking the movements of homeless populations presents methodological difficulties, but understanding their movements in space and time is needed to inform optimal placement of services. This pilot study developed, tested, and refined methods to apply global positioning systems (GPS) technology paired with individual narratives to chronicle the movements of homeless populations. Detail of methods development and difficulties encountered and addressed, and geospatial findings are provided. A pilot sample of 29 adults was recruited from a low-demand homeless shelter in the downtown area of Fort Worth, Texas. Pre- and post-deployment interviews provided participant characteristics and planned and retrospectively-reported travels. Only one of the first eight deployments returned with sufficient usable data. Ultimately 19 participants returned the GPS device with >20h of usable data. Protocol adjustments addressing methodological difficulties achieved 81 % of subsequent participants returning with sufficient usable data. This study established methods and demonstrated feasibility for tracking homeless population travels.

Optimal Service Placement in Distributed Networks

Optimal Service Placement in Distributed Networks

Evaluating the McDonald's business model for HIV prevention among truckers to improve program coverage and service utilization in India, 2004–2010

BackgroundThis study describes the experiences and results of a large-scale human immunodeficiency virus (HIV) prevention intervention for long-distance truck drivers operating on the national highways of India.MethodsThe intervention for long-distance truckers started in 2004 across 34 trans-shipment locations. However, due to poor coverage and utilization of services by truckers in the initial 18-month period, the intervention was redesigned to focus on only 17 trans-shipment locations. The redesigned intervention model was based on the McDonald’s business franchise model where the focus is on optimal placement of services, supported with branding and standardization of services offered, and a surround sound communication approach. Program output indicators were assessed using program monitoring data over 7 years (2004–2010) and two rounds of cross-sectional behavioral surveys conducted in January 2008 (n = 1402) and July 2009 (n = 1407).ResultsThe number of truckers contacted per month per site increased from 374 in 2004 to 4327 in 2010. Analysis of survey data showed a seven-fold increase in clinic visits in the past 12 months from 2008 to 2009 (21% versus 63%, P < 0.001). A significant increase was also observed in the percentage of truckers who watched street plays (10% to 56%, P < 0.001), and participated in health exhibitions (6% to 35%, P < 0.001). Furthermore, an increase from round 1 to round 2 was observed in the percentage who received condoms (13% to 22%, P < 0.001), and attended one-one counseling (15% to 21%, P < 0.01). Treatment-seeking from program clinics for symptoms related to sexually transmitted infections increased six-fold during this period (16% versus 50%, P < 0.001).ConclusionAdoption of a business model for HIV prevention helped to increase program coverage and service utilization among long-distance truckers. Implementing HIV prevention programs in a highly mobile population such as truckers, in a limited number of high-impact locations, supported by branding of services, could help in saturating coverage and optimum utilization of available resources.