Proactive Migration for Dynamic Computation Load in Edge Computing

Joint edge caching and dynamic service migration in SDN based mobile edge computing

IP over WDM has emerged as a winning network architecture to facilitate today's IP centric Internet. Although the total amount of bandwidth demands keeps increasing, there still exists a huge gap between the increasing full-wavelength capacity and the existing low-rate IP traffic. Efficiently grooming low-rate traffic stream to high-capacity lightpaths helps to alleviate the bandwidth mismatch, effectively improve the network resource utilization, and reduce the overall network cost. In this paper, we study traffic grooming problem in IP over WDM framework. The problem of minimizing the total number of transmitters and receivers in the networks is formulated as an integer linear programming (ILP) optimization problem. The ILP formulation produces optimal solutions for static traffic demands. However, applying the ILP solution to large and realistic networks is not practical due to its prohibitively large computation time. We therefore develop a heuristic algorithm, namely traffic aggregation algorithm. A significant improvement on wavelength utilization is observed in our experiments when we use our traffic aggregation algorithm. I. INTRODUCTION The popularity of the Internet and Internet protocol (IP)- based Intranet is promising enormous growth in data traffic originating from hosts that are IP endpoints. This growth is being fueled by various applications such as those driven by World-Wide-Web (WWW) and by the indirect impact of increased computing power and storage capacity at the end systems. The advent of new services with increasing intelligence and the corresponding bandwidth demands are further adding to the traffic growth. New access technologies such as Asymmetric Digital Subscriber Line (ADSL), High- bit-rate Digital Subscriber Line (HDSL), and fiber to the home (FTTH) would remove the access bottlenecks and enforce an even faster growth of demand on the backbone network. These changing trends have led to a fundamental shift in traffic patterns and the traffic is mostly due to data communications. In the past, the amount of data traffic on carrier networks was smaller than the voice-centric traffic. Therefore, the carrier networks were designed to primarily support voice traffic, and the data traffic was transmitted using the voice channels. Now, the core networks are being designed primarily for data traffic with voice support at the edges. The voice can be carried in the core networks using voice-over-IP or similar paradigms. To meet these growing demands, WDM will continue to emerge as a dominating trend for use in backbone networks. The research reported in this paper is funded in part by the National Science Foundation under grant ANI-0087746, grant ANI-0434872 and grant ANI- 0323374. Architectures will include to satisfy the need for better quality of service (QoS), protection, and availability guarantees in IP networks. WDM significantly increases the fiber capacity utilization by dividing the available bandwidth into non-overlapping wavelength channels, supporting connections between the two end nodes by establishing an all-optical channel, namely lightpath, which allows use of different formats, bit-rates, and protocol transparency. At the same time, most network designers believe that IP is going to be the common traffic convergence layer in communication networks. Consequently, IP over WDM has been envisioned as the winning combination of the network architecture.

Mobile edge computing (MEC) provides a promising approach to significantly reduce network operational cost and improve quality of service (QoS) of mobile users by pushing computation resources to the network edges, and enables a scalable Internet of Things (IoT) architecture for time-sensitive applications (e-healthcare, real-time monitoring, and so on.). However, the mobility of mobile users and the limited coverage of edge servers can result in significant network performance degradation, dramatic drop in QoS, and even interruption of ongoing edge services; therefore, it is difficult to ensure service continuity. Service migration has great potential to address the issues, which decides when or where these services are migrated following user mobility and the changes of demand. In this paper, two conceptions similar to service migration, i.e., live migration for data centers and handover in cellular networks, are first discussed. Next, the cutting-edge research efforts on service migration in MEC are reviewed, and a devisal of taxonomy based on various research directions for efficient service migration is presented. Subsequently, a summary of three technologies for hosting services on edge servers, i.e., virtual machine, container, and agent, is provided. At last, open research challenges in service migration are identified and discussed.

Envisioned low-latency services in 5G, like automated\n\t\t\t\t driving, will rely mainly on Multi-access Edge Computing\n\t\t\t\t (MEC) to reduce the distance, and hence latency, between users\n\t\t\t\t and the remote applications. MEC hosts will be deployed close to\n\t\t\t\t mobile base stations, constituting a highly distributed computing\n\t\t\t\t platform. However, user mobility may raise the need to migrate a\n\t\t\t\t MEC application among MEC hosts to ensure always connecting\n\t\t\t\t users to the optimal server, in terms of geographical proximity,\n\t\t\t\t Quality of Service (QoS), etc. However, service migration may\n\t\t\t\t introduce: (i) latency for users due to the downtime duration;\n\t\t\t\t (ii) cost for the network operator as it consumes bandwidth\n\t\t\t\t to migrate services. One solution could be the use of service\n\t\t\t\t replication, which pro-actively replicates the service to avoid service\n\t\t\t\t migration and ensure low latency access. Service replication\n\t\t\t\t induces cost in terms of storage, though, requiring a careful\n\t\t\t\t study on the number of service to replicate and distribute in\n\t\t\t\t MEC. In this paper, we propose to compare service migration\n\t\t\t\t and service replication via an analytical model. The proposed\n\t\t\t\t model captures the relation between user mobility and service\n\t\t\t\t duration on service replication as well as service migration costs.\n\t\t\t\t The obtained results allow to propose recommendations between\n\t\t\t\t using service migration or service replication according to user\n\t\t\t\t mobility and the number of replicates to use for two types of\n\t\t\t\t service.

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict quality of service (QoS) requirements. The passive optical network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, far-sighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. To this purpose, first we design a delay-tracking mechanism, which allows us to model the resource allocation problem from a control-theoretic viewpoint as a model predictive control (MPC) problem. MPC helps in making far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delay-constrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.

With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC–edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU’s utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

Recently, multimedia streaming applications are deployed anywhere (homes, campuses, etc.). Delivering these applications over mobile ad hoc networks is a challenging task: first, mobile ad hoc networks are infrastructure less, with dynamic topology, and limited resources. Secondly these applications have strict quality of service (QoS) requirements given with delay, jitter, bandwidth, loss rate, etc. Several architectures have been developed. However, an important part like QoS in routing level is still omitted. In this paper, many different coding techniques for video streaming are reviewed and two routing protocols (reactive and proactive) are used to evaluate which of them can improve QoS for multimedia streaming applications. Results show that reactive protocol performs better than proactive protocol in terms of throughput and network load in high mobility, but roles are reversed in terms of loss rate and network load for large-scale networks. PNSR and Jitter are influenced by nodes numbers independently of used protocol.

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

ABSTRACTWith the rapid development of intelligent scenarios such as intelligent transportation and urban perception, delay‐sensitive and computationally intensive applications continue to grow, especially in tasks such as anomaly detection based on machine learning, which puts higher requirements on the real‐time processing capabilities and resource scheduling efficiency of edge computing systems. Mobile edge computing (MEC), as a key supporting architecture, plays a core role in ensuring quality of service (QoS). On one hand, the occupation and release of server resources during task processing lead to dynamic changes in system resources within edge computing networks. Since system resources are often difficult to effectively replenish, services that are well adapted to the current time point may fail to accommodate new service requests at subsequent time points. On the other hand, due to the heterogeneous nature of tasks, resource consumption varies significantly across different task processing, causing some servers to easily become overloaded and unable to meet the processing demands of new tasks continuously. To tackle the challenges presented by the intensified dynamic changes in edge server resources due to task heterogeneity and the difficulty of processing new task requests under high‐load conditions in edge computing scenarios, we first devise a collaborative scheduling and offloading strategy for heterogeneous tasks across multiple edge servers. A task sorting mechanism and priority algorithm based on time groups and score values are designed. Then, with the optimization objective of minimizing task processing latency, a regional resource optimization algorithm based on Deep‐Q‐Network (DQN) is proposed to enable the effective processing of tasks. Finally, extensive experimental results show that this strategy can effectively achieve edge node load balancing, significantly reduce system processing delay, improve overall resource utilization, and has good heterogeneous task adaptability, which is suitable for multiple intelligent scene requirements including anomaly detection.

Next-generation high-speed Internet backbone networks will be required to support a broad range of emerging applications which may not only require significant bandwidth, but may also have strict quality of service (QoS) requirements. Furthermore, the traffic from such applications are expected to be highly bursty in nature. For such traffic, the allocation of static fixed-bandwidth circuits may lead to the over-provisioning of bandwidth resources in order to meet QoS requirements. Optical burst switching (OBS) is a promising new technique which attempts to address the problem of efficiently allocating resources for bursty traffic. In OBS, incoming data is assembled into bursts at the edges of the network, and when the burst is ready to be sent, resources in the network are reserved only for the duration of the burst. The reservation of resources is typically made by an out-of-band one-way control message which precedes the burst by some offset time. By reserving resources only for the duration of the burst, a greater degree of utilization may be achieved in the network. This book provides an overview of optical burst switching. Design and research issues involved in the development of OBS networks are discussed, and approaches to providing QoS in OBS networks are presented. Topics include: - Optical burst switching node and network architectures - Burst assembly - Signaling protocols - Contention resolution - Burst scheduling - Quality of service in OBS networks

The transmission of video on demand (VoD) will become one of the most successful services on the Internet. This implies the playback of stored video over a high-speed network with a strict quality of service (QoS). Guaranteeing this QoS requires a very demanding reservation of network resources. That makes optimisation of network resources a key issue. The paper introduces a fast method to optimise network reservations based on the concept of empirical envelope. Previous works face this issue either by using smoothing techniques to reduce peak rates, or in a costly iterative way based on jointly adjusting encoding and channel rates that cannot be efficiently used at transmission time. The proposed method is based on generating a set of points from the stored video with an offline analysis of its empirical envelope, and then using these points to efficiently calculate the optimal reservation for a given channel, at channel establishment time. The paper shows that the number of points generated is very low, making this approach very efficient.

Applying multimedia to Wireless Sensor Network (WSN) adds more challenges due to WSN resource constraints and the strict Quality of Service (QoS) requirements for multimedia transmission. Different multimedia applications may have different QoS requirements, so routing protocols designed for Wireless Multimedia Sensor Network (WMSN) should be conversant of these requirements and challenges in order to ensure the efficient use of resources to transfer multimedia packets in an utmost manner. The majority of solutions proposed for WMSN depends on traditional layered based mechanisms which are inefficient for multimedia transmission. In this paper we propose a cross-layer Ant based Routing Protocol for WMSN (CARPM) by using modified ant colony optimization (ACO) technique to enhance the routing efficiency. The proposed protocol uses an improved ACO to search for the best path that are satisfied with the multimedia traffic requirements. While making best decision the weightage is given to energy consumption, and queuing delay. The proposed cross layer scheme works between the routing, MAC, and physical layers. Since, the remaining power and timestamp metrics are exchanged from physical layer to network layer. Dynamics duty cycle assignment is proposed at MAC layer which changes according to traffic rate. The presented algorithm is simulated using NS2 and is proven to satisfy its goals through a series of simulations.

QoS and robustness of priority-based MAC protocols for the in-car power line communication

Mobile edge computing (MEC) can supplement cloud computing by helping to overcome the limitations of long physical transmission distances and accelerating the responsiveness of edge computing servers. In 5G (fifth generation) cellular networks, adopting MEC can guarantee ultralow latency. To enhance the MEC quality, optimization of the user service profile migration according to the user mobility is essential. However, this optimization establishes an NP-hard problem. Moreover, high-speed 5G base stations with MEC servers often experience high energy consumption. As conventional service migration algorithms such as those based on profile tracking and game theory tend to fall in local optima and neglect energy consumption constraints, we propose a memetic algorithm based on community detection local search (MA-CDLS) to continuously optimize the service migration in 5G MEC scenarios. During busy periods or in crowded areas, MA-CDLS adopts a single-objective optimization of user-perceived latency to achieve high-performance 5G services. During light-load periods or in uncrowded areas, MA-CDLS uses two measures, namely the user-perceived latency and energy consumption, to realize energy-efficient 5G services. MA-CDLS effectively reduces the search space and speeds up the elite selection in the meme operator. Experiments in simulated scenarios show that MA-CDLS achieves a lower user-perceived latency and energy consumption, than the traditional profile tracking and game theory methods, especially during congestion.