Compute-intensive Applications Research Articles

An optimized heterogeneous multi-access edge computing framework based on transfer learning and artificial internet of things

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous Multi-access Edge Computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users’ quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing

With the development of Internet of Things (IoT) networks and Mobile Edge Computing (MEC), many computing-intensive applications have been developed in large quantities. Due to the heterogeneity of tasks, different application services are required to perform each task. Caching application services and related data in edge servers is challenging. Hence, we study the service cache placement and task offloading problem in IoT networks. Since IoT devices and edge servers with limited storage resources can only cache a few services at the same time, we formulate the service cache placement and task offloading of IoT devices problem to minimize task service delay with long-term energy constraint of IoT devices, which is a mixed integer nonlinear programming problem. To solve this problem, an online Deep Reinforcement Learning guided by the Lyapunov optimization framework algorithm (LYADRL) is proposed. We first build a virtual queue model to decouple the problem by Lyapunov optimization technique to transform the problem into a single time slot optimization problem. Then, we use Deep Reinforcement Learning techniques to find the optimal edge service caching and task offloading policies for each time slot. Simulation results show that our algorithm can reduce the service delay compared with other benchmark algorithms.

Lyapunov-guided deep reinforcement learning for delay-aware online task offloading in MEC systems

With the arrival of 5G technology and the popularization of the Internet of Things (IoT), mobile edge computing (MEC) has great potential in handling delay-sensitive and compute-intensive (DSCI) applications. Meanwhile, the need for reduced latency and improved energy efficiency in terminal devices is becoming urgent increasingly. However, the users are affected by channel conditions and bursty computational demands in dynamic MEC environments, which can lead to longer task correspondence times. Therefore, finding an efficient task offloading method in stochastic systems is crucial for optimizing system energy consumption. Additionally, the delay due to frequent user–MEC interactions cannot be overlooked. In this article, we initially frame the task offloading issue as a dynamic optimization issue. The goal is to minimize the system’s long-term energy consumption while ensuring the task queue’s stability over the long term. Using the Lyapunov optimization technique, the task processing deadline problem is converted into a stability control problem for the virtual queue. Then, a novel Lyapunov-guided deep reinforcement learning (DRL) for delay-aware offloading algorithm (LyD2OA) is designed. LyD2OA can figure out the task offloading scheme online, and adaptively offload the task with better network quality. Meanwhile, it ensures that deadlines are not violated when offloading tasks in poor communication environments. In addition, we perform a rigorous mathematical analysis of the performance of Ly2DOA and prove the existence of upper bounds on the virtual queue. It is theoretically proven that LyD2OA enables the system to realize the trade-off between energy consumption and delay. Finally, extensive simulation experiments verify that LyD2OA has good performance in minimizing energy consumption and keeping latency low.

ParallEdge: Exploiting Computing-Mobility Parallelism for Efficient 5G/6G Edge Computing

With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt$</tex-math></inline-formula> 10000 times larger than the deployment cost of the 4G infrastructure. To address this fundamental limit, we propose ParallEdge, a deployment scheme that employs mobile edge servers for cost-effective service coverage. ParallEdge is designed based on the observation that the processing delay and server moving delay become comparable in many computing-intensive applications in the 5G/6G edge computing. Unlike the traditional “move-then-process” frameworks, ParallEdge follows a “move-while-processing” paradigm, which exploits the parallelism between task processing and server movement to enable resource sharing among more users. Moreover, with the joint optimization of path planning and task scheduling for multiple mobile servers, the deployment cost for service coverage can be further reduced. We analyze the approximation gap of the proposed algorithm, and conduct extensive simulation experiments based on the real-world application data, and the results show that ParallEdge can significantly reduce deployment cost and improve resource utilization compared to the state-of-the-art schemes.

Decentralized computation offloading via multi-agent deep reinforcement learning for NOMA-assisted mobile edge computing with energy harvesting devices

Supporting latency-sensitive and computation-intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server’s limited computing resources, available communication channel quality, and time-limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non-orthogonal multiple access (NOMA)-assisted MEC with multiple EH-enabled MDs. To learn decentralized offloading policies, we propose a multi-agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi-agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional-based prioritized experience replay (pPER) and propose an algorithm called multi-agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

Partial and cost-minimized computation offloading in hybrid edge and cloud systems

Nowadays, numerous mobile devices (MDs) provide nearly anytime and anywhere services, running on top of various computation-intensive applications. However, bearing limited battery, bandwidth, computing, and storage resources, MDs cannot completely execute all tasks of such applications in real-time. Cloud data centers (CDCs) possess enormous resources and energy, which can help execute tasks offloaded from MDs. Nonetheless, CDCs reside in remote sites, thereby leading to long transmission latency. In recent years, small base stations (SBSs) have emerged to offer close proximity, high bandwidth, and low latency services to their nearby and limited MDs. However, it becomes a new challenge to minimize the total system cost in a complex and heterogeneous architecture. To address it, this work proposes an energy-minimized partial computation offloading technique. First, a limited optimization problem of cost minimization for the system is formulated. Afterward, an improved hybrid meta-heuristic algorithm is developed, which synergistically combines a Metropolis acceptance criterion of simulated annealing and genetic operations. One uniqueness of the proposed algorithm is that it simultaneously determines task allocation among MDs, an SBS, and a CDC, the transmission power of MDs, and bandwidth allocation of wireless channels between MDs and SBS. Experiments with real-life tasks from Google data centers have shown that the proposed Genetic Simulated-annealing-based Particle swarm optimization (GSP) significantly achieves lower system cost and faster convergence speed than benchmark peers.

DAFA: Dynamic approximate full adders for high area and energy efficiency

As the number of transistors on a chip surface increases, power consumption becomes more and more a serious concern. A promising solution to bridge the gap between resource-constrained gadgets and computation-intensive applications could be the approximate computing paradigm. This paper presents four efficient approximate full adder cells based on dynamic logic and carbon nanotube field-effect transistors (CNFETs). To the best of our knowledge, dynamic logic has never been deployed in the design of approximate full adders before. Comprehensive simulations and analyses are conducted to study the efficacy of the new circuits. Simulation results indicate remarkable improvements compared to state-of-the-art circuits. For instance, at 0.9 V power supply, our final proposed design improves the power-delay-area product (PDAP) metric by at least 63% compared to its peers. Moreover, the applicability of the proposed adders in the image sharpening application is examined by measuring peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) using the MATLAB tool. The proposed designs have also a reasonable performance in this regard.

Automatic Discovery of Collective Communication Patterns in Parallelized Task Graphs

Collective communication APIs equip MPI vendors with the necessary context to optimize cluster-wide operations on the basis of theoretical complexity models and characteristics of the involved interconnects. Modern HPC runtime systems with a programmability focus can perform dependency analysis to eliminate the need for manual communication entirely. Profiting from optimized collective routines in this context often requires global analysis of the implicit point-to-point communication pattern or tight constrains on the data access patterns allowed inside kernels. The Celerity API provides a high degree of freedom for both runtime implementors and application developers by tieing transparent work assignment to data access patterns through user-defined range-mapper functions. Canonically, data dependencies are resolved through an intra-node coherence model and inter-node point-to-point communication. This paper presents Collective Pattern Discovery (CPD), a fully distributed, coordination-free method for detecting collective communication patterns on parallelized task graphs. Through extensive scheduling and communication microbenchmarks as well as a strong scaling experiment on a compute-intensive application, we demonstrate that CPD can achieve substantial performance gains in the Celerity model.

Mobility-aware task offloading in MEC with task migration and result caching

Numerous computation-intensive applications have emerged, demanding lower delay and energy consumption. Multi-access Edge Computing (MEC) provides task offloading services for mobile users (MUs) through proximate MUs, effectively minimizing service delay and energy consumption. However, the challenges of supporting MUs’ mobility persist, and ensuring uninterrupted task offloading services from MEC servers still needs to be solved. Additionally, applications like autonomous driving generate repetitive tasks, introducing unnecessary computational overhead. This paper adopts a task migration strategy to tackle these issues, specifically addressing task offloading failure due to MU mobility. Simultaneously, a computation-result cache-assisted technique is leveraged to alleviate the burden of repetitive computation. The objective is to minimize the system cost, which consists of the processing delay and energy consumption of tasks for all MUs. The problem is modeled as an integer linear programming problem, and the optimal solution is meticulously derived. In response to the high computational complexity inherent in obtaining the optimal solution, a low-complexity heuristic algorithm (CMHA) is introduced. Simulation results demonstrate that optimal and heuristic schemes significantly reduce the overall system delay and energy consumption.

Multi-UAV Cooperative Task Offloading and Resource Allocation in 5G Advanced and Beyond

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Service Satisfaction-Oriented Task Offloading and UAV Scheduling in UAV-Enabled MEC Networks

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.

A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks

In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks

With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.

SD-AETO: Service-Deployment-Enabled Adaptive Edge Task Offloading Scheme in MEC

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

WattScope: Non-intrusive application-level power disaggregation in datacenters

Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters’ increasing energy consumption and carbon footprint. The most basic prerequisite for optimizing a datacenter’s energy- and carbon-efficiency is accurately monitoring and attributing energy consumption to specific users and applications. Since datacenter servers tend to be multi-tenant, i.e., they host many applications, server- and rack-level power monitoring alone does not provide insight into the energy usage and carbon emissions of their resident applications. At the same time, current application-level energy monitoring and attribution techniques are intrusive: they require privileged access to servers and necessitate coordinated support in hardware and software, neither of which is always possible in cloud environments. To address the problem, we design WattScope, a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server’s aggregate power usage and without requiring direct access to the server’s operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server’s total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power meter measurements that are already available in data centers. We evaluate WattScope’s accuracy on a production workload and show that it yields high accuracy, e.g., often <∼10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage.

Exploring Instruction Set Architectural Variations: x86, ARM, and RISC-V in Compute-Intensive Applications

As computational demands continue to evolve in the modern era, the choice of hardware architecture plays a pivotal role in optimizing the performance of compute-intensive applications. This research paper delves into the exploration and comparison of three prominent hardware architectures: x86, ARM, and RISC-V, within the context of compute-intensive applications. The study begins with a comprehensive overview of these architectures, highlighting their distinctive features, and strengths. Subsequently, we investigate their suitability and adaptability in diverse compute-intensive workloads. Our analysis encompasses a wide spectrum of parameters, including computational throughput, power efficiency, scalability, and architectural flexibility. We scrutinize the architectural intricacies that impact the execution of compute-intensive tasks, shedding light on both the advantages and limitations of each architecture. We used the gem5 simulator to compare these Instruction Set Architectures (ISA). We run different benchmarks on gem5 with different ISA and different configurations and compare the result. Based on these results we predict which architecture is better in which scenario. Gem5 is not a cycle accurate simulator but it’s a model accurate. In conclusion, "Exploring Architectural Variations: x86, ARM, and RISC-V in Compute-Intensive Applications" offers a comprehensive insight into the nuances of hardware selection for compute-intensive workloads. Our findings aid system architects, researchers, and technology enthusiasts in making informed decisions about the most suitable architectural choice for their specific computeintensive applications, ultimately contributing to advancements in computational performance and efficiency

UDCO-SAGiMEC: Joint UAV Deployment and Computation Offloading for Space–Air–Ground Integrated Mobile Edge Computing

Computation-intensive applications offloading is challenging, especially in the designated regions where communication infrastructure is absent or compromised. In this paper, we present a Space–Air–Ground integrated Mobile Edge Computing (SAGiMEC) system for these regions to provide quality computational services, where the unmanned aerial vehicles (UAVs) act as in-fight edge servers to provide low-latency edge computing and the satellite provides resident cloud computing. A joint optimization problem is formulated considering UAV deployment, ground device (GD) access, and computation offloading to minimize the system average response latency. To cope with the problem’s complexity, we propose a Particle Swarm Optimization (PSO) and Greedy Strategy (GS)-based algorithm (PSO&amp;GS) to obtain an approximate optimal solution. Extensive simulations validate the convergence of the proposed algorithm. Numerical results show that the proposed approach has excellent convergence, and the system average response latency is about 0.65x–0.85x that of the benchmark algorithm.

VADF: V ersatile A pproximate D ata F ormats for Energy-Efficient Computing

Approximate computing (AC) techniques provide overall performance gains in terms of power and energy savings at the cost of minor loss in application accuracy. For this reason, AC has emerged as a viable method for efficiently supporting several compute-intensive applications, e.g., machine learning, deep learning, and image processing, that can tolerate bounded errors in computations. However, most prior techniques do not consider the possibility of soft errors or malicious bit-flips in AC systems. These errors may interact with approximation-introduced errors in unforeseen ways, leading to disastrous consequences, such as the failure of computing systems. A recent research effort, FTApprox (DATE’21) proposes an error-resilient approximate data format. FTApprox stores two blocks, starting from the one containing the most significant valid (MSV) bit. It also stores location of the MSV block and protects them using error-correcting bits (ECBs). However, FTApprox has crucial limitations such as lack of flexibility, redundantly storing zeros in the MSV, etc. In this paper, we propose a novel storage format named Versatile Approximate Data Format (VADF) for storing approximate integer numbers while providing resilience to soft errors. VADF prescribes rules for storing, for example, a 32-bit number in either 8-bit, 12-bit or 16-bit numbers. VADF identifies the MSV bit and stores a certain number of bits following the MSV bit. It also stores the location of the MSV bit and protects it by ECBs. VADF does not explicitly store the MSB bit itself and this prevents VADF from accruing significant errors. VADF incurs lower error than both truncation methodologies and FTApprox. We further evaluate five image-processing and machine-learning applications and confirm that VADF provides higher application quality than FTApprox in the presence and absence of soft errors. Finally, VADF allows the use of narrow arithmetic units. For example, instead of using a 32-bit multiplier/adder, one can first use VADF (or FTApprox) to compress the data and then use a 8-bit multiplier/adder. Through this approach, VADF facilitates 95.97% and 79.3% energy savings in multiplication and addition, respectively. However, the subsequent re-conversion of the 8-bit output data to 32-bit data using Inv-VADF(16,3,32) diminishes the energy savings by 9.6% for addition and 0.56% for multiplication operation, respectively. The code is available at https://github.com/CandleLabAI/VADF-ApproximateDataFormat-TECS .

Cost-efficient security-aware scheduling for dependent tasks with endpoint contention in edge computing

Edge computing empowers latency-sensitive and computation-intensive applications composed of multiple dependent tasks of mobile devices to execute on nearby edge servers. Despite the advantages, intermediate data across edge servers faces external security threats. Moreover, most existing works on task scheduling are designed on the idealized system model where communications are performed in parallel, they neglect the endpoint contention problem during data transmission, and thereby can hardly meet the user-specified deadline. To tackle these issues, this paper studies the endpoint contention-aware scheduling problem for dependent tasks to minimize the execution cost under the security and deadline constraints in the edge computing environment. We propose a heuristic algorithm named CSSDE that first distributes the deadline to each task and sorts tasks according to probabilistic endpoint contention-aware upward rank, and then allocates bandwidth resources and computation resources to intermediate data and tasks, respectively, finally performs rescheduling to improve the initial scheduling scheme. In addition, based on CSSDE and genetic algorithm, we present a metaheuristic algorithm called C-GA that considers the impact of task ordering and incorporates a multi-population co-evolution mechanism to enhance population diversity and avoid falling into local optimum for a better scheduling scheme. Simulation experiments are conducted on synthetic and real-world applications. The results demonstrate that our proposed algorithms achieve a higher success rate, and C-GA reduces the execution cost by 43.92, 40.52, and 8.56 percent compared with SCAS-E, ELSH, and CSSDE, respectively.