Task Migration Algorithm Research Articles

Load-aware task migration algorithm toward adaptive load balancing in Edge Computing

The rapid advancement of the Internet of Things (IoT) is leading to more and more devices joining the network to interact with information, which requires improving the performance of IoT applications to accommodate more data, faster response times, and more complex tasks. Edge computing, as a new computing paradigm, brings resource contention and load imbalance while reducing communication overhead and task latency. This paper addresses the workload distribution challenge in edge networks, aiming to optimize resource utilization and thereby enhance IoT application performance. To achieve this goal, we present the Load-aware Task Migration (LATM) algorithm. Firstly, we present a load state detection model that captures edge nodes’ workloads and dynamically classifies them according to their resource requirements. We then propose an innovative optimization problem, transforming task migration into a weighted tripartite graph matching problem. This problem leverages the Kuhn–Munkres(KM) task migration algorithm to attain the optimal matching between tasks and nodes. Finally, we assess the algorithm’s performance through experimental simulation. The experimental results underscore the algorithm’s substantial potential in reducing task response times, task execution times, load balance, and enhancing resource utilization.

Optimizing risk-aware task migration algorithm among multiplex UAV groups through hybrid attention multi-agent reinforcement learning

Optimizing risk-aware task migration algorithm among multiplex UAV groups through hybrid attention multi-agent reinforcement learning

Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platforms

Heterogeneous distributed computing systems are becoming more and more common to process practical applications in many embedded systems, where energy efficiency is very important, especially for battery-powered systems. This paper studies the problem of how to adaptively schedule dynamic applications on the fly on a heterogeneous embedded system such that the system’s energy consumption can be minimized, while meeting applications’ response time and reliability requirements simultaneously. We first study the problem of scheduling a set of static applications. This problem is formulated as an integer nonlinear programming problem by deriving the response time equation of directed acyclic graph (DAG) applications. A reliability-aware scheduling strategy is designed to solve this problem. On top of this strategy, we develop an algorithm that tries to use as few processors as possible to finish the computation because, in this way, the system’s static energy can be saved. We then extend this solution to handle the problem of scheduling dynamic applications. By developing a task migration algorithm and a schedule adjustment algorithm, the system can adaptively handle suspended and newly released applications on the fly. Extensive simulation experiments are conducted, and the results demonstrate the high efficiency of our proposed approaches against some classic heuristics and a state-of-the-art approach.

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-Assisted Vehicular Networks

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.

Internet of Things Task Migration Algorithm under Edge Computing in the Design of English Translation Theory and Teaching Practice Courses

This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.

Energy‐Efficient Task Migration and Path Planning in UAV‐Enabled Mobile Edge Computing System

With the rapid development of unmanned aerial vehicles (UAVs) technology and the advent of the 5G era, the role of UAV‐enabled mobile edge computing (MEC) system has attracted much attention, especially in the event of some emergencies. However, considering the limited battery life and computing capabilities of UAVs, it is challenging to provide energy‐efficient services for mobile devices. To solve this challenge, we propose an energy‐efficient dynamic task migration algorithm (EDTM) that minimizes the total energy consumption of the system while ensuring UAVs system load balance. Based on the improved ant colony algorithm and path elimination strategy, the proposed algorithm comprehensively considers task migration distance between UAVs, the load situation of UAVs, and environmental factors (e.g., wind speed and air density) and finally plans a reasonable task migration path. The simulation results show that the performance of the proposed EDTM is superior to the benchmark schemes.

Distributed Task Migration Optimization in MEC by Extending Multi-Agent Deep Reinforcement Learning Approach

Closer to mobile users geographically, mobile edge computing (MEC) can provide some cloud-like capabilities to users more efficiently. This enables it possible for resource-limited mobile users to offload their computation-intensive and latency-sensitive tasks to MEC nodes. For its great benefits, MEC has drawn wide attention and extensive works have been done. However, few of them address task migration problem caused by distributed user mobility, which can't be ignored with quality of service (QoS) consideration. In this article, we study task migration problem and try to minimize the average completion time of tasks under migration energy budget. There are multiple independent users and the movement of each mobile user is memoryless with a sequential decision-making process, thus reinforcement learning algorithm based on Markov chain model is applied with low computation complexity. To further facilitate cooperation among users, we devise a distributed task migration algorithm based on counterfactual multi-agent (COMA) reinforcement learning approach to solve this problem. Extensive experiments are carried out to assess the performance of this distributed task migration algorithm. Compared with no migrating (NM) and single-agent actor-critic (AC) algorithms, the proposed distributed task migration algorithm can achieve up 30-50 percent reduction about average completion time.

An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

With large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for “hot” tasks to be migrated to run on a “cool” core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this article, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50 percent reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.

A Distributed Game Methodology for Crowdsensing in Uncertain Wireless Scenario

With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology (SPG) for crowdsensing.

Intelligent task prediction and computation offloading based on mobile-edge cloud computing

Edge computing overcomes the high communication delay shortcoming of traditional cloud computing and provides computing services with high reliability and high bandwidth for mobile devices. At present, edge computing has become the forefront and hotspot of mobile-edge cloud computing (MEC) research. However, with the increasing requirements and services of mobile users, offloading strategy of simple edge computing is no longer applicable to MEC architecture. This paper puts forward a new intelligent computation offloading based MEC architecture in combination with artificial intelligence (AI) technology. According to the data size of computation task from mobile users and the performance features of edge computing nodes, a computation offloading and task migration algorithm based on task prediction is proposed. The computation task prediction based on LSTM algorithm, computation offloading strategy for mobile device based on task prediction, and task migration for edge cloud scheduling scheme are used to assist in optimizing the edge computing offloading model. Experiments show that our proposed architecture and algorithm can effectively reduce the total task delay with the increasing data and subtasks.

Task migration for mobile edge computing using deep reinforcement learning

Mobile edge computing (MEC) is a new network architecture that puts computing capabilities and storage resource at the edges of the network in a distributed manner, instead of a kind of centralized cloud computing architecture. The computation tasks of the users can be offloaded to the nearby MEC servers to achieve high quality of computation experience. As many applications’ users have high mobility, such as applications of autonomous driving, the original MEC server with the offloaded tasks may become far from the users. Therefore, the key challenge of the MEC is to make decisions on where and when the tasks had better be migrated according to users’ mobility. Existing works formulated this problem as a sequential decision making model and using Markov decision process (MDP) to solve, with assumption that mobility pattern of the users is known ahead. However, it is difficult to get users’ mobility pattern in advance. In this paper, we propose a deep Q-network (DQN) based technique for task migration in MEC system. It can learn the optimal task migration policy from previous experiences without necessarily acquiring the information about users’ mobility pattern in advance. Our proposed task migration algorithm is validated by conducting extensive simulations in the MEC system.

Performance/energy aware task migration algorithm for many‐core chips

This study proposes an efficient task migration algorithm for mesh-based multi- and many-core chips. The proposed algorithm collects tasks running on a rectangular-based set of cores, that is, source sub-mesh and moves the tasks to another rectangular-based set to remove chip temperature hotspots and to provide balanced load on the chip. The proposed migration algorithm uses the concept of gathering/scattering to minimise the traffic induced by the migration. In this regard, the proposed algorithm uses a selected node in each row of the source sub-mesh to gather tasks of all cores in the same row. Selection of the gathering node is done based on its location in the row and traffic rate of other cores in the row. When gathering nodes are migrated, in the destination sub-mesh, then, they scatter their tasks according to the same pattern among the cores in their rows. Simulations of the proposed migration algorithm are done by Access Noxim simulator in a various range of network conditions with application graphs of D263DECMP3DEC, DMPEG4, and DVOPD. Results obtained from simulations show that the proposed algorithm offers 36% better performance, 28% lower energy consumption, and 7% lower temperature in comparison with the previously proposed migration algorithms.

Thermal-Aware On-Line Scheduler for 3-D Many-Core Processor Throughput Optimization

3-D many-core processor (3-D MCP) has become an emerging technology to tackle the power wall problem due to rapidly increasing number of transistors. However, when maximizing the throughput of 3-D MCP, which is expressed as a weighted sum of the speeds, due to the inherent heat removal limitation, thermal issues must be taken into consideration. Since the temperature of a core strongly depends on its location in the 3-D IC, a proper task allocation can alleviate the thermal problem and improve the throughput. Nevertheless, conventional techniques require computationally intensive thermal simulation, which prohibits its usage from the online application. In this paper, we propose an efficient online task allocation and task migration algorithm attempting to maximize the throughput of 3-D MCP simultaneously, considering unfinished tasks left from the last scheduling interval and new incoming tasks of this scheduling interval. The results of our experiments show that our proposed method achieves a 20.82X runtime speedup. These results are comparable to the exhaustive solutions obtained from optimization-modeling software LINGO. In addition, on average, our throughput results, with and without consideration of unfinished tasks, are only 4.39% and 0.69% worse, respectively, than that of the exhaustive method. In 128 task-to-core allocations, our method takes only 0.951 ms, which is 59.39 times faster than that of the previous work.

Non- Preemptive Real Time Scheduling using Checkpointing Algorithm for Cloud Computing

This paper focuses on providing a solution for online real time services using non-preemptive scheduling algorithm in order to minimize the execution time of the migrated tasks. Earlier, a non-preemptive scheduling with task migration algorithm is used to minimize the penalty. Whenever a task misses its deadline, it will migrate the task to another virtual machine and starts its execution from the beginning. Therefore it increases the execution time of the migrated task. In order to overcome this problem, a non-preemptive real time scheduling using checkpointing algorithm is proposed to minimize the execution time of the migrated tasks and minimizes the penalty even better by earlier completion of migrated tasks. This improves the overall system performance. Our simulation results outperform the older approaches based on the similar model.

Using task migration to improve non-contiguous processor allocation in NoC-based CMPs

In this paper, a processor allocation mechanism for NoC-based chip multiprocessors is presented. Processor allocation is a well-known problem in parallel computer systems and aims to allocate the processing nodes of a multiprocessor to different tasks of an input application at run time. The proposed mechanism targets optimizing the on-chip communication power/latency and relies on two procedures: processor allocation and task migration. Allocation is done by a fast heuristic algorithm to allocate the free processors to the tasks of an incoming application when a new application begins execution. The task-migration algorithm is activated when some application completes execution and frees up the allocated resources. Task migration uses the recently deallocated processors and tries to rearrange the current tasks in order to find a better mapping for them. The proposed method can also capture the dynamic traffic pattern of the network and perform task migration based on the current communication demands of the tasks. Consequently, task migration adapts the task mapping to the current network status. We adopt a non-contiguous processor allocation strategy in which the tasks of the input application are allowed to be mapped onto disjoint regions (groups of processors) of the network. We then use virtual point-to-point circuits, a state-of-the-art fast on-chip connection designed for network-on-chips, to virtually connect the disjoint regions and make the communication latency/power closer to the values offered by contiguous allocation schemes. The experimental results show considerable improvement over existing allocation mechanisms.

Task migration in three-dimensional meshes

As a result of the emerging use of mesh-based multicomputers (and recently mesh-based multiprocessor systems-on-chip), issues related to processor management have attracted much attention. In a mesh-based multiprocessor, after repeated submesh allocations and de-allocations, the system network may be fragmented, i.e. there might be unallocated nodes in the network. As a result, in a system with contiguous processor allocation, no new tasks can start running due to the lack of enough free adjacent processors to form a suitable submesh. Although there might be enough free processors available, they remain idle until the allocator can find a set of adjacent free nodes forming a submesh to be used for the new task. This can lead to low system performance. Task migration was introduced as a solution to this problem through migration of tasks running on some submeshes to other free areas in order to reduce fragmentation by chaining the newly freed areas and disengaging nodes to form larger submeshes. In this paper, we propose a novel structured and formulated way to code task migration, which is helpful for congestion detection in different steps of task migration algorithms. Moreover, considering the fact that the 3D mesh-based multicomputers are now very popular, a new task migration algorithm in 3D meshes is proposed. We also address the special case of the 2D migration in a 3D mesh multicomputer.

Partitionability of k-Extra-Stage Omega Networks and an Optimal Task Migration Algorithm

In a large-scale parallel computing system, partitionability is an important feature that allows a large system to be partitioned into smaller subsystems so that multiple tasks can be run independently on different subsystems. In such a system, a task run on a subsystem may need to be migrated to another subsystem if the subsystem is faulty, if load balance is needed, or if subsystem restructuring is desired. This paper studies the partitionability of k-extra-stage Omega networks and presents an optimal task migration algorithm for k-extra-stage Omega networks.

A task migration algorithm for heterogeneous distributed computing systems

Most load balancing schemes are based on the assumption that all processors in a distributed computing system (DCS) have similar processing power and workload. However, as technology continues to advance, networks have become more likely to contain processors of different capabilities and configurations. The proposed load balancing method is based on the coterie, a tool first used in mutual exclusion and vote assignment. This paper describes how the unique properties of coteries allow the underlying protocol to be tailored to the capabilities of the individual nodes in a connected network. In general, each processor communicates with only a subset of the other processors in the network, keeping message traffic relatively low. The coterie allows these subsets to be constructed according to a more thoughtful method – one that is designed to better meet the needs of networks of varying characteristics. It is also shown that the proposed method will always perform task migration whenever possible i.e., if at least one lightly loaded processor and at least one heavily loaded processor exist in the network, task migration will occur. Further, an extension of our algorithm to provide fault tolerance is given: when a processor fails, the network can continue to function with a modified system configuration.

Efficient task migration algorithm for distributed systems

The objective of the study was to achieve balanced load among processors, reduce the communication overhead of the load balancing algorithm, and improve respource utilization, which results in better average resonse time. A communication protocol and a fully distributed algorithm for dynamic load balancing through task migration in a connected N-processor network are presented. Each processor communicates its load directly with only a subset (of the size square root N) of processors, reducing communication traffic and average response time. It is proved that the given algorithm will perform task migration even if there is only one light load processor and one heavy load processor in the system. Simulation results show that the proposed scheme can save up to 60% of the protocol messages used by the broadcast algorithms and can reduce the average response time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>