Offloading Resource Research Articles

Low-latency partial resource offloading in cloud-edge elastic optical networks

In the context of the rapid deployment of IoT, 5G, and cloud computing, numerous emerging applications demand efficient networked computing capacity for task offloading from mobile and IoT users. This paper focuses on the optimization of network resource allocation and reduction of end-to-end (E2E) latency through the strategic decision of whether and where to offload user requests in a cloud-edge elastic optical network (CE-EON). To address this problem, we first formulate the problem into an integer linear programming (ILP) model as an initial solution. Additionally, we introduce several heuristic approaches that leverage the concept of partial resource offloading, specifically based on proportional segmentation (PRO_PS), partial resource offloading based on average segmentation (PRO_AS), all resource offloading (ARO), and all local processing (ALP). Furthermore, we implement a collaborative cloud-edge (CCE) offloading approach as a baseline for comparison. Our results demonstrate that the PRO_PS approach closely approximates the optimal solutions obtained from the ILP model in static scenarios. Moreover, the PRO_PS approach achieves the lowest E2E latency, blocking probability, and optimized network resource allocation in dynamic scenarios. This highlights the effectiveness of the proposed approach in improving system performance and addressing the challenges of CE-EONs.

Differentiable Optimization for Orchestration: Resource Offloading for Vehicles in Smart Cities

Differentiable Optimization for Orchestration: Resource Offloading for Vehicles in Smart Cities

Selective Resource Offloading in Cloud-Edge Elastic Optical Networks

With the rapid development of Internet of Things (IoT), IoT mobile devices host more computation-intensive and real-time applications, such as face recognition, online gaming, and augmented reality/virtual reality services. This paper mainly addresses the problems of the selective resource offloading in order to achieve resource optimization in cloud-edge elastic optical networks (CE-EONs). We firstly propose two integer linear program (ILP) models to minimize both the end-to-end (E2E) latency and the total number of frequency slots by considering the latency sensitivity, followed by introducing three corresponding heuristic approaches, namely resource priority offloading (RPO), distance priority offloading (DPO), and coordinated distance and resource offloading (DRO). For comparison, we also introduced an existing resource offloading (ERO) approach in CE-EONs. On one hand, simulation results show that the DRO approach greatly approximated to the optimized solutions of ILP models in the static traffic scenario. Meanwhile, the DRO approach achieves the better performance in terms of average E2E latency. On the other hand, the proposed DRO approach can significantly reduce the blocking probability owing to much improved spectrum efficiency and achieves a graceful tradeoff between the computing resources and E2E latency compared to the RPO, DPO, and ERO approaches in the dynamic traffic scenario.

Deep reinforcement learning in NOMA-assisted UAV networks for path selection and resource offloading

This paper constructs a NOMA-based UAV-assisted Cellular Offloading (UACO) framework and designs a UAV path selection and resource offloading algorithm (UPRA) based on deep reinforcement learning. This paper focuses on the coupling relationship between path selection and resource offloading during the movement of UAVs. A joint optimization problem between UAV three-dimensional path design and resource offloading is proposed, considering the UAV’s autonomous obstacle avoidance in complex environments and the influence of obstacles in 3D space on the channel model. In particular, a constrained clustering-assignment algorithm is designed by improving the K-means algorithm and combining it with the assignment algorithm in order to achieve periodic clustering of random motion users and UAV task assignment. In addition, a semi-fixed hierarchical power allocation algorithm is embedded in the designed DQN algorithm to improve the convergence performance of the learning algorithm in this paper. Simulation results show that: the NOMA-based design is able to improve the spectrum utilization efficiency and communication throughput of the UAV network system. Compared with the artificial potential field, the proposed algorithm is able to solve the problem of falling into suboptimal solutions in path selection and improve the communication throughput. In addition, this paper explores the impact of the reward function on the training convergence and the results in deep reinforcement learning. The excellent adaptability of the designed algorithm in dynamic networks as well as in complex environments is demonstrated by random deployment of users and varying the maximum user movement speed.

Similarity Measure for Interval Neutrosophic Sets and Its Decision Application in Resource Offloading of Edge Computing

Interval neutrosophic sets (INSs), characterized by truth, indeterminacy and falsity membership degrees, handle the uncertain and inconsistent information that commonly exists in real-life systems, and constitute an extension of the interval valued fuzzy set and interval valued intuitionistic fuzzy set. The existing works on similarity measures for INSs are mostly constructed by distance measures and entropies. Meanwhile, the degree of similarity is expressed as a single number, even if the interval-valued information is considered. This may lead to a loss of interval-valued information. In order to cope with these issues, in this paper, we introduce a new approach to constructing the similarity measures for INSs using fuzzy equivalencies. First, based on fuzzy equivalencies and aggregation operators, the definition of interval-valued fuzzy equivalence is generalized to interval neutrosophic values. Then, based on the framework of INSs, we propose the definition and construction method of the similarity measure using the interval neutrosophic fuzzy equivalence. The similarity degree is expressed as an interval and could retain more information than ever before. In addition, according to practical situations, one can obtain different similarities by selecting the parameters in fuzzy equivalence. Due to the increase in edge computing, it is necessary to reasonably offload the client’s resource and assign them to the edge server to balance the resource usage. The Similarity measure is conductive to select and match the client and edge server. Finally, an illustrative example verifies that the proposed method can find a reasonable client and edge server, as well as effectiveness in the edge computing application.

Efficient computation offloading for ethereum DApps

This paper covers a novel solution to reduce the gas consumed by Decentralized Applications (DApps) running on Ethereum Virtual Machine (EVM). Smart contracts extend the capabilities of the Blockchain, but the strict gas policy of the EVM limits the implementation of various DApps. On the other hand, Oracle provides a means to use an external execution environment for smart contracts, but its functional limitations and additional costs limit the growth of the DApp market to DApp users. To address the gas consumption crisis of DApps, we designed an event-based resource offloading solution that allows DApp developers to run their own compute servers. By adopting our solution, DApp users only pay the constant and minimized amount of gas fee, while the external server pays highly reduced amount of gas fee instead. We describe the solution in detail with a sample DApp model and mathematically demonstrate the effectiveness of the solution compared to an Oracle-based implementation. Furthermore, we conducted empirical experiment of actual DApp using EVM and verified the reduction of the gas consumption.

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm’s feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network.Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner.

Adaptive Job Load Balancing Scheme on Mobile Cloud Computing with Collaborative Architecture

The adaptive mobile resource offloading (AMRO) proposed in this paper is a load balancing scheme for processing large-scale jobs using mobile resources without a cloud server. AMRO is applied in a mobile cloud computing environment based on collaborative architecture. A load balancing scheme with efficient job division and optimized job allocation is needed because the resources for mobile devices will not always be provided consistently in this environment. Therefore, a job load balancing scheme is proposed that considers personal usage patterns and the dynamic resource state of the mobile devices. The delay time for computer job processing is minimized through dynamic job reallocation and adaptive job allocation in the disability state that occurs due to unexpected problems and to excessive job allocations by the mobile devices providing the resources for the mobile cloud computing. In order to validate the proposed load balancing scheme, an adaptive mobile resource management without cloud server (AMRM) protocol was designed and implemented, and the improved processing speed was verified in comparison with the existing offloading method. The improved job processing speed in the mobile cloud environment is demonstrated through job allocation based on AMRM and by taking into consideration the idle resources of the mobile devices. Furthermore, the resource waste of the mobile devices is minimized through adaptive offloading and consideration of both insufficient and idle resources.

Virtual MAC concept and its protocol design in virtualised heterogeneous wireless network

As a prevailing concept in 5G, virtualisation provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers to share different RATs’ physical infrastructures. This study proposes a generic framework for virtualising heterogeneous networks with different RATs. A novel virtual medium access control concept is introduced to realise resource offloading (allocation) and mobility management of the framework. The offloading and handover protocols are designed in detail. A novel resource offloading strategy is devised: First, to model the fact that different RATs possess different adaptability to different services, an ‘adaptability ratio’ concept is introduced and calculated using grey relational analysis. After that, a matching theory based framework is proposed to formulate the resource offloading problem and a ‘deferred acceptance’ algorithm is introduced to solve it. Through simulation, it is proved that the proposed protocols can efficiently reduce the service interruptions and improve the resource usage.