Cloud Offloading Research Articles

An Efficient Edge–Cloud Partitioning of Random Forests for Distributed Sensor Networks

Intelligent edge sensors that augment legacy “unintelligent” manufacturing systems provides cost-effective functional upgrades. However, the limited compute at these edge devices requires trade-offs in efficient edge-cloud partitioning and raises data privacy issues. This work explores policies for partitioning random forest approaches, which are widely used for inference tasks in smart manufacturing, among sets of devices with different resources and data visibility. We demonstrate, using both publicly available datasets and a real-world grinding machine deployment, that our privacy-preserving approach to partitioning and training offers superior latency-accuracy tradeoffs to purely on-edge computation while still achieving much of the benefits from data-sharing cloud offload strategies.

Primal-Dual-Based Computation Offloading Method for Energy-Aware Cloud-Edge Collaboration

In the context of the Internet of Things (IoT), resource-constrained mobile edge computing (MEC) can no longer fully meet the needs of the rapidly growing number of mobile users; hence, cloud-edge collaborative computing has been developed. This paper focuses on the total energy consumption of the system and heterogeneity of scenarios, and a collaborative cloud-edge computation offloading approach with near real-time decision making is proposed. First, a general cloud-edge collaborative computation offloading model is abstracted from typical applications, and the energy consumption for edge and cloud offloading is calculated separately by considering both transmission and computational energy consumption. The problem is formulated as an integer linear program (ILP) with multidimensional resource constraints and is proven to be NP-hard. Then, a novel primal-dual computation offloading (PDCO) algorithm is designed to make near real-time offloading decisions one by one based on the sequential arrival of task requests. The approximation ratio of PDCO is derived through the weak duality property and the price-resource increment relationship. The experimental results show that under the guidance of total cost influenced by marginal prices, PDCO not only avoids blindly making offloading decisions but also effectively alleviates the shortage of resources on edge servers (ESs), approaching the optimal performance in terms of total energy consumption and resource utilization.

Load balance -aware dynamic cloud-edge-end collaborative offloading strategy.

Cloud-edge-end (CEE) computing is a hybrid computing paradigm that converges the principles of edge and cloud computing. In the design of CEE systems, a crucial challenge is to develop efficient offloading strategies to achieve the collaboration of edge and cloud offloading. Although CEE offloading problems have been widely studied under various backgrounds and methodologies, load balance, which is an indispensable scheme in CEE systems to ensure the full utilization of edge resources, is still a factor that has not yet been accounted for. To fill this research gap, we are devoted to developing a dynamic load balance -aware CEE offloading strategy. First, we propose a load evolution model to characterize the influences of offloading strategies on the system load dynamics and, on this basis, establish a latency model as a performance metric of different offloading strategies. Then, we formulate an optimal control model to seek the optimal offloading strategy that minimizes the latency. Second, we analyze the feasibility of typical optimal control numerical methods in solving our proposed model, and develop a numerical method based on the framework of genetic algorithm. Third, through a series of numerical experiments, we verify our proposed method. Results show that our method is effective.

Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems

Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.

DRL-Based Service Function Chain Edge-to-Edge and Edge-to-Cloud Joint Offloading in Edge-Cloud Network

In this paper, we study service function chain (SFC) offloading in the edge-cloud network. Two offloading options are available for fully-loaded edge nodes: edge to edge (E2E) offloading and edge to cloud (E2C) offloading. Both E2E offloading and E2C offloading have been optimized in existing research, and Deep Reinforcement Learning (DRL) methods were adopted to achieve excellent performances. However, DRL-based SFC E2E and E2C joint offloading is still a research gap. In this paper, we propose a DRL-based SFC E2E and E2C joint offloading optimization algorithm for the edge-cloud network to maximize the utilization efficiency of edge resources. Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is applied to the optimization problem. The simulation results indicate that the proposed algorithm has excellent convergence performance and improves the utilization efficiency of edge resources in edge-cloud network scenarios of diverse scales.

Cost-Effective Scheduling for Dependent Tasks With Tight Deadline Constraints in Mobile Edge Computing

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task’s future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.

Trine: Cloud-Edge-Device Cooperated Real-Time Video Analysis for Household Applications

Real-time mobile video analysis like object detection and tracking is key to various household applications such as AR, cognitive assistance and smart home. Such applications rely on heavy DNN models, which are not suitable for mobile devices due to resource limitation. The long latency of cloud offloading is unacceptable for the real-time requirements, and the direct edge offloading relies on powerful edge servers, which is impractical for household scenarios. To solve this challenge, we take advantage of the computing devices that are low-cost or already exist in our lives, and propose Trine, a cloud-edge-device cooperated framework, in which complicated computation tasks are offloaded from the device to the cloud with the edge as the key bond to coordinate. In addition, due to the heterogeneity of edge devices, which leads to no one-fits-all algorithm that is optimal in all situations, we propose a profile-based algorithm to customize trackers for various edge devices. We implemented Trine on an android phone and three edge devices. The experiments demonstrate that Trine achieves 8-36% higher real-time accuracy and 25-89% higher robustness than state-of-the-art.

Cost optimization of omnidirectional offloading in two-tier cloud–edge federated systems

The use of the federation can exploit the advantages of cloud and edge computing technologies as the federation provides the facilities by which both can complement each other. However, certain services are needed to offload from clouds to edges, termed reverse offloading, and between edges, termed horizontal offloading. By considering the scenarios discussed above, in this paper, we propose a generic omnidirectional (OMNI) architecture of cloud–edge computing systems intending to provide vertical (edge to cloud offloading, and vice versa), and horizontal (between edges) offloading. To investigate the effectiveness of the proposed architecture in different operational scenarios, we formulate the cost optimization problem with different latency (loose, low, ultra-low) constraints. We develop an offloading algorithm using simulated annealing named the two-tier simulated annealing (TTSA) algorithm. We set two criteria for offloading: (1) offloading based on the job’s size and (2) offloading based on the job’s priority, irrespective of its size. The experimental results show that our proposed OMNI architecture can reduce the total cost by 7%–10%, compared to other existing architectures, and our proposed TTSA algorithm can reduce the cost by 45%–55%, compared to other existing algorithms. The average latency in OMNI architecture is relatively very less compared to other architectures.

Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks

AbstractIn this article, we evaluate the first experience of computation offloading from drones to real fifth‐generation (5G) operator systems, including commercial and private carrier‐grade 5G networks. A follow‐me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is processed at the edge, and image tracking displacements are translated into positioning commands that are sent back to the drone, so that the drone keeps the camera focused on the person at all times. The application is characterised to identify the processing and communication contributions to service delay. Then, we evaluate the latency of the application in a real non standalone 5G operator network, a standalone carrier‐grade 5G private network, and, to compare these results with previous research, a Wi‐Fi wireless local area network. We considered both multi‐access edge computing (MEC) and cloud offloading scenarios. Onboard computing was also evaluated to assess the trade‐offs with task offloading. The results determine the network configurations that are feasible for the follow‐me application use case depending on the mobility of the end user, and to what extent MEC is advantageous over a state‐of‐the‐art cloud service.

DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device

Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks (CNNs). To alleviate their excessive computational demands, developers have traditionally resorted to cloud offloading, inducing high infrastructure costs and a strong dependence on networking conditions. On the other end, the emergence of powerful SoCs is gradually enabling on-device execution. Nonetheless, low- and mid-tier platforms still struggle to run state-of-the-art CNNs sufficiently. In this article, we present DynO, a distributed inference framework that combines the best of both worlds to address several challenges, such as device heterogeneity, varying bandwidth, and multi-objective requirements. Key components that enable this are its novel CNN-specific data packing method, which exploits the variability of precision needs in different parts of the CNN when onloading computation, and its novel scheduler, which jointly tunes the partition point and transferred data precision at runtime to adapt inference to its execution environment. Quantitative evaluation shows that DynO outperforms the current state of the art, improving throughput by over an order of magnitude over device-only execution and up to 7.9× over competing CNN offloading systems, with up to 60× less data transferred.

SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning

Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a “device-edge-cloud” collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the problem of offloading power line inspection tasks is classified as an optimization problem to obtain the minimum delay under the constraints of edge server computing and communication resources. Finally, the problem is constructed as a Markov decision, and a deep Q-network (DQN) is used to obtain the minimum delay of the system. In addition, an experience replay mechanism and a greedy algorithm are introduced in the learning process to improve the offloading accuracy. The experimental results show that the proposed offloading strategy in this paper saves 54%, 37% and 26% of the task completion time, respectively, compared with local offloading, cloud offloading and random offloading. It effectively reduces the UAV inspection delay and improves the transmission line inspection efficiency.

Lyapunov Optimization-Based Trade-Off Policy for Mobile Cloud Offloading in Heterogeneous Wireless Networks

In order to improve mobile users’ service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.

VentQsys: Low-cost open IoT system for CO_2 monitoring in classrooms

In educational context, a source of nuisance for students is carbon dioxide (CO_2) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide (CO_2), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

Edge Computing - The Way Forward

As of late, the Edge Computing worldview has acquired significant notoriety in scholastic and mechanical circles. It fills in as a key empowering influence for some, future advances like 5G, Internet of Things (IoT), augmented reality by interfacing distributed computing offices and administrations to the end clients. The Edge registering worldview gives low idleness, versatility, and area mindfulness backing to delay-delicate applications. Edge figuring can possibly address the worries of reaction time necessity, transmission capacity cost saving, just as information wellbeing and protection. In this paper, we present the meaning of edge Computing, trailed by a few contextual investigations, going from cloud offloading to smart home and city.

Blockchain-Based Secure Computation Offloading in Vehicular Networks

Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.

Efficient Latency Control in Fog Deployments via Hardware-Accelerated Popularity Estimation

Introduced as an extension of the Cloud at the network edge for computing and storage purposes, the Fog is increasingly considered a key enabler for Internet-of-Things applications whose latency requirements are not compatible with a Cloud-only approach. Unlike Cloud platforms, which can elastically accommodate large numbers of requests, Fog deployments are usually dimensioned for an average traffic load and, thus, unable to handle sudden bursts of requests without violating latency guarantees. In this article, we address the problem of efficiently controlling Fog admission to guarantee application response time. We propose request-aware admission control (AC) strategies maximizing the number of Fog-handled requests by means of dynamic popularity estimation. In particular, the LRU-AC , an AC strategy based on online learning of the request popularity distribution via a Least Recently Used (LRU) filter, is introduced. We contribute an analytical model for assessing LRU-AC performance and quantifying the incurred reduction of Cloud offload cost, w.r.t. both an ideal oracle-based and a request-oblivious AC strategy. Further, we propose a feasible implementation design of LRU-AC on FPGA hardware using Aging Bloom Filters (ABF) to mimic the function of the LRU-AC, while providing a compact memory representation. The use of ABFs for LRU-AC is theoretically validated and verified through simulation. The current implementation shows a throughput of 16.7 Mpps and a processing latency of less than 3μ s while multiplying the Fog acceptance-rate by 10 in the evaluated scenario.

Smartphone processor architecture, operations, and functions: current state-of-the-art and future outlook: energy performance trade-off

Balancing energy–performance trade-offs for smartphone processor operations is undergoing intense research considering the challenges with the evolving technology of mobile computing. However, to guarantee energy-efficient processor operation, layout, and architecture, it is necessary to identify and integrate optimization techniques and parameters influencing energy–performance trade-off in various processor activity domains. Existing literature on energy optimization in smartphones focuses primarily on individual sub-domains such as OS, GPU, and cloud offloading methods. It reflects multiple smartphone processor activities domains as being the most discussed but less integrated. Through this study, we intend to provide the current state-of-the-art energy optimization techniques for smartphone processor operations. It also classifies multiple energy-draining processor operations along with their thorough discussion of methodologies and popular optimization techniques. The study models smartphone processor sub-components highlighting conventional techniques and performance parameters among its varied domains affecting the device’s energy performance along with significant energy drain minimization without any serious performance degradation. The study analyzes these approaches in the context of applicability, performance, and expected future demands along with revealing limitations of those approaches and open research issues prevailing in the available literature. Finally, we conclude our study by summarizing the current state of the art for smartphone processor activities power consumption.

Addressing the Bandwidth Demand of Immersive Applications Through NFV in a 5G Network

Immersive technology is expected to become a key player in the next wave of consumer electronics with potential applications spanning a wide variety of sectors. Yet, there are a number of technological hurdles which need to be addressed. Without speed and computation capability, interactivity cannot be ensured. More than often the constrained nature of the mobile device demands is complemented by cloud ones through the use of cloud offloading techniques. With cloud offloading, battery consumption and processing performance problems are translated into sensor data selection and network latency problems. In this context, we focus our study on some functional building blocks that can be used in conjunction and deployed in a 5G virtualized architecture to alleviate bandwidth and latency requirements of immersive application scenarios. To this end, we have created an experimental scenario exploiting real data to test our combined solution based on the field of view cut and optimized transport protocols, discussing the tradeoffs that emerge.

Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive Cloudlets

Cloudlet-based mobile cloud offloading is an emerging technology designed to augment mobile elements by migrating resource-hungry components to adjacent local resource pooling. However, the Cloudlet resources are usually limited in terms of the computing utility, storage and network bandwidth. In this scenario, the remote Cloud infrastructure can provide additional computing and storage utility during run-time; the heterogeneous offloading methods for different mobile applications and diverse offloading resources complicate the Cloudlet-based offloading and resource allocation process. As a result, a considerable amount of delay is caused by setting up the execution environment, communication overhead and waiting in the queue, which significantly downgrade the QoS and the usability of such systems. In this paper, we propose a novel hybrid offloading model to solve the heterogeneous resource-constraint offloading issues in the Cloudlet, concerning the offloading energy and execution efficiency. A queue-based offloading framework is developed to formulate and analyze the mixed migration-based and partition-based offloading behaviors in the Cloudlet. The execution and energy-aware heterogeneous offloading resource allocation problem is formalized, and a Particle Swarm Optimization heuristic solution is presented. A SARIMA-based load prediction model is designed in the Cloudlet to achieve fine-grain proactive resource allocation. Experimental results reveal that the proposed framework can effectively reduce the offloading energy cost and execution time, compared to currently existing solutions.