Task Migration Research Articles

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

In this article, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems&#x2019; power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared resource contention in the selected multicore architecture. The variables are used at runtime to produce a performance trace that encompasses sufficient information for the ANN model to predict the impact of a frequency change on the performance of tasks. A migration heuristic encompassing a weighted activity vector is combined with the ANN model to dynamically adjust frequencies and also to trigger task migrations among cores, enabling further optimization by solving resource contentions and balancing the load among cores. The proposed solution achieved energy-savings of 24.97 percent on average when compared to the run-to-end approach, and it did it without compromising the criticality of any single task. The overhead incurred in terms of execution time was 0.1791 percent on average. Each prediction added 15.3585<inline-formula><tex-math notation="LaTeX">$\mu s$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>&#x03BC;</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="conradihoffmann-ieq1-3056070.gif"/></alternatives></inline-formula> on average and each retraining cycle triggered at frequency adjustments was never larger than 100<inline-formula><tex-math notation="LaTeX">$\mu s$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>&#x03BC;</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="conradihoffmann-ieq2-3056070.gif"/></alternatives></inline-formula>.

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.

The Power Control Method of Data Center Based on Cloud- Edge Collaboration

Data centers have the characteristics of huge energy consumption and strong power fluctuations. To solve the problem of unstable supply and demand such as frequent power fluctuations in the micro-grid of the data center using renewable energies, this paper establishes a controllable load model of server clusters based on the task migration mechanism by analyzing the various tasks and characteristics of large cloud data centers, as well as edge computing nodes. Through the collaborative optimization control of edge and cloud computing tasks, and dynamically adjusting and migrating the server cluster load, a new data center tie-line power control method is proposed. The results of calculation examples show that the method can achieve power smoothing of large-scale cloud data center tie lines through cloud-edge collaborative control.

A new task offloading algorithm in edge computing

In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

Future many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip’s thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previously proposed to improve many-core system performance by moving tasks between active and dark cores. As task migration imposes system performance overhead due to the large wake-up latency of the dark cores, this paper proposes a prediction-based early wake-up (PEW) to reduce the dark cores’ wake-up latency during task migration. A window-based online ridge regression (RR) is used as the prediction model. The prediction model uses the past window’s thermal, power, and core status (i.e., active or dark) to predict the future core temperatures at run-time. If task migration is predicted in the next control period, the proposed PEW puts the dark cores in a power state with low wake-up latency. Thus, the proposed PEW reduces the time for the dark cores to start executing the tasks. The comparison results show that our proposed PEW reduces the completion time by up to 7.9% and 4.1% compared to non-early wake-up (NoEW) and a fixed threshold wake-up (FEW), respectively. It also shows that the proposed PEW increases the MIPS/Watt by up to 5.5% and 2.3% over NoEW and FEW, respectively. These results show that the proposed PEW improves the many-core system’s overall performance in terms of reducing dark cores’ wake-up latency and increasing the number of executed instructions per Watt.

EDF‐Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real‐Time

Most of the multiprocessor real‐time scheduling algorithms follow the partitioned approach, the global approach, or the semipartitioned approach which is a hybrid of the first two by allowing a small subset of tasks to migrate. EDF‐fm (Earliest Deadline First‐based Fixed and Migrating) and EDF‐os (Earliest Deadline First‐based Optimal Semipartitioned) are semipartitioned approaches and were proposed for soft real‐time sporadic task systems. Despite their desirable property that migrations are boundary‐limited such as they can only occur at job boundaries, EDF‐fm and EDF‐os are not always optimal and have higher tardiness and cost of overheads due to task migration. To address these issues, in this paper, we classify the systems into different types according to the utilization of their tasks and propose a new semipartitioned scheduling algorithm, earliest deadline first‐adaptive, dubbed as EDF‐adaptive. Our experiments show that EDF‐adaptive can achieve better performance than EDF‐fm and EDF‐os, in terms of system utilization and tardiness overhead. It is also proved that EDF‐adaptive is able to lessen the task migration overhead, by reducing the number of migrating jobs and the number of processors to which a task is migrated.

Task Migration Based on Reinforcement Learning in Vehicular Edge Computing

Multiaccess edge computing (MEC) has emerged as a promising technology for time‐sensitive and computation‐intensive tasks. With the high mobility of users, especially in a vehicular environment, computational task migration between vehicular edge computing servers (VECSs) has become one of the most critical challenges in guaranteeing quality of service (QoS) requirements. If the vehicle’s tasks unequally migrate to specific VECSs, the performance can degrade in terms of latency and quality of service. Therefore, in this study, we define a computational task migration problem for balancing the loads of VECSs and minimizing migration costs. To solve this problem, we adopt a reinforcement learning algorithm in a cooperative VECS group environment that can collaborate with VECSs in the group. The objective of this study is to optimize load balancing and migration cost while satisfying the delay constraints of the computation task of vehicles. Simulations are performed to evaluate the performance of the proposed algorithm. The results show that compared to other algorithms, the proposed algorithm achieves approximately 20–40% better load balancing and approximately 13–28% higher task completion rate within the delay constraints.

A Hybrid Task Crash Recovery Solution for Edge Computing in IoT-Based Manufacturing

Smart factory is the deep integration of technology, business, products, and industry. The R&D of products, the construction of factory information, customer orders, and supply chain data are combined to act on the production. This paper proposes a Hybrid Internet of Things (H-IoT) platform framework, which consists of five layers: intelligent device layer, communication protocol layer, edge computing layer, IoT control layer, and application layer. Object Linking and Embedding (OLE) for Process Control (OPC) transmits the received device data to the database of the edge server through the TCP/IP protocol. After the edge server performs preprocessing such as data cleaning, the device data are uniformly collected and distributed through the IoT layer. The various subsystems of the smart factory obtain the required resource data through IoT. To run tasks uninterruptedly and efficiently, we propose a Container-Managed Task Crash Recovery Mechanism for the Edge Computing (EC) layer. We design a reasonable EC hierarchy, then propose a task migration scheduling strategy. Our method ensures the reliability and stability of manufacturing with mass data transmission even in the case of a random crash of partial edge servers.

Self-Adaptive Fault Recovery Mechanism Based on Task Migration Negotiation

Self-Adaptive Fault Recovery Mechanism Based on Task Migration Negotiation

More on Pipelined Dynamic Scheduling of Big Data Streams

An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs.

CL-ADMM: A Cooperative-Learning-Based Optimization Framework for Resource Management in MEC

We consider the problem of the intelligent and efficient resource management framework in mobile-edge computing (MEC), which can reduce delay and energy consumption, and features distributed optimization and efficient congestion avoidance. In this article, we present a cooperative learning framework for resource management in MEC from an alternating direction method of multipliers (ADMMs) perspective, named the CL-ADMM framework. First, computing a task requires both the user personal data and corresponding program that processes it, to efficiently cache program in a group, a novel program popularity estimation scheme is proposed, which is based on a semi-Markov process model. Then, a greedy program cooperative caching mechanism is established, which can effectively reduce delay and energy consumption. Second, to address group congestion, a dynamic task migration scheme based on improved cooperative Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Third, to minimize delay and energy consumption for resource allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM-based scheme to solve this problem, which can reduce the complexity of the problem with a new set of auxiliary variables, these subproblems are all convex problems that can be solved by using a primal-dual approach, which guarantees its convergence. Finally, we prove its convergence by using the Lyapunov theory. The numerical results demonstrate the effectiveness of the CL-ADMM framework in reducing delay and energy consumption in MEC.

Efficient hardware task migration for heterogeneous FPGA computing using HDL-based checkpointing

Task migration plays an important role in load balancing and energy savings in data centers. It also challenges service providers to minimize service interruptions during task migration. FPGA computing requires checkpointing as an essential function for hardware task migration. However, the current methods of implementing such a function for FPGAs have a high cost in hardware resources and significant degradation in performance. To overcome these problems, in this paper we propose a system using checkpointing at the hardware description language (HDL) level for hardware task migration. First, we propose a hardware task migration scheme in which checkpointing procedures and context transfer can overlap to reduce the service downtime. Second, we present a new checkpointing architecture for FPGAs that flattens the structure of nested modules at the HDL level. Third, we propose a static analysis of the original HDL source code to reduce the cost of hardware. Fourth, we introduce a Python-based tool to generate the checkpointing architecture at the HDL level. We evaluated our checkpointing architecture and the migration scheme using four application benchmarks running on a heterogeneous FPGA cluster. Our evaluations showed that the migration downtime was minimized at only 1.251 ms in the S-Search benchmark. When compared with a tree-based checkpointing architecture, the proposed architecture with the static analysis can reduce the LUT overhead by up to 50%, on the average. The checkpointing hardware caused small degradation in the maximum clock frequency (1.66% on the average), and consumed small memory footprints. Other comparisons with the previous hardware task migration scheme highlight the advantages of our migration scheme.

Efficiënt load balancing by optimized flexi max-min algorithm

Load Balancing is required to distribute tasks across Virtual Machines on one hand and on the other hand in a cloud Environment. Here, a new task scheduling algorithm: ‘Optimized Flexi Max-Min Scheduling Algorithm’ is proposed. The algorithm maintains a data structure which is modeled after a Binary Search Tree (BST) for estimations, enhanced searching, task allocation, and migration of tasks. Cloud-Sim is used to model and simulate the cloud computing environment in order to obtain simulated data. The result of this experiment shows that the proposed algorithm outperforms the traditional Max-Min task scheduling algorithms.

DTaPO: Dynamic Thermal-Aware Performance Optimization for Dark Silicon Many-Core Systems

Future many-core systems need to handle high power density and chip temperature effectively. Some cores in many-core systems need to be turned off or ‘dark’ to manage chip power and thermal density. This phenomenon is also known as the dark silicon problem. This problem prevents many-core systems from utilizing and gaining improved performance from a large number of processing cores. This paper presents a dynamic thermal-aware performance optimization of dark silicon many-core systems (DTaPO) technique for optimizing dark silicon a many-core system performance under temperature constraint. The proposed technique utilizes both task migration and dynamic voltage frequency scaling (DVFS) for optimizing the performance of a many-core system while keeping system temperature in a safe operating limit. Task migration puts hot cores in low-power states and moves tasks to cooler dark cores to aggressively reduce chip temperature while maintaining high overall system performance. To reduce task migration overhead due to cold start, the source core (i.e., active core) keeps its L2 cache content during the initial migration phase. The destination core (i.e., dark core) can access it to reduce the impact of cold start misses. Moreover, the proposed technique limits tasks migration among cores that share the last level cache (LLC). In the case of major thermal violation and no cooler cores being available, DVFS is used to reduce the hot cores temperature gradually by reducing their frequency. Experimental results for different threshold temperatures show that DTaPO can keep the average system temperature below the thermal limit. Affirmatively, the execution time penalty is reduced by up to 18% compared with using only DVFS for all thermal thresholds. Moreover, the average peak temperature is reduced by up to 10.8°C. In addition, the experimental results show that DTaPO improves the system’s performance by up to 80% compared to optimal sprinting patterns (OSP) and reduces the temperature by up to 13.6°C.

Energy‐aware decision‐making for dynamic task migration in MEC‐based unmanned aerial vehicle delivery system

AbstractNowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV‐based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high‐speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC‐based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC‐based UAV delivery system. Specifically, we propose an energy‐aware decision‐making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real‐time system status and QoS constraints, and through a dynamic two‐tier decision‐making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real‐world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.

A tree based mechanism for the load balancing of virtual machines in cloud environments

Cloud computing is a paradigm for providing resources as a utility over the Internet. With the cloud computing, new administrations in data innovation has risen up in terms of business and innovation as a viewpoint which facilitates client’s access to IT assets whenever and anyplace utilizing pay-per-utilize model. Along these, it provides prominent working environment to the client and at the same time should be invaluable for the cloud specialist organization. Load balancing is a major aspect of cloud computing which balances overloaded and under-loaded nodes or less burden node. Load balancing improves the quality of service metrics of multi-objectives like response-time, cost, vitality, and throughput. This paper presents, paired tree algorithm to improve the performance of makespan and migration. The paired tree algorithm on 25 cloudlets provides task migration of 7 and 91.32% of makespan.

An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud

The limitations of mobile devices have attracted researchers to work out an energy-efficient mechanism to enhance user experience. The emerging mobile cloud computing (MCC) provides a new approach to solve this problem. Some parts of mobile applications, i.e., heavy computational tasks, are migrated to remote servers with powerful computational resources, which can improve the performance of mobile devices. This paper focuses on a popular MCC architecture, CloneCloud, and constructs a scheduling problem of task migration as a constrained stochastic shortest path problem in a directed acyclic graph. And then it designs a scheduling algorithm based on genetic algorithm to obtain the optimal task migrations. A user flexibly makes migration decisions through its own mobile device and migrates some tasks to the clone in CloneCloud without any change of application codes. Furthermore, this scheme facilitates mobile devices to collaboratively process computational applications. Real testbed experiments in Android smartphone demonstrate that the smartphone is able to save at most 59.42% energy within a time constraint by using the proposed task migration scheme.

Efficient load balancing techniques for multi-datacenter cloud milieu

Load balancing is a challenge in the cloud network for proper resource utilization and improvement of makespan. To overcome the problem of load balancing various static and dynamic scheduling methods come into existence. In this paper, we have proposed two multi-datacenter two-phase load adjustment techniques for load balancing and apposite task scheduling. Our proposed methods allocate user jobs to different virtual machines present in different datacenters based on better makespan. Here we have considered communication delay and bandwidth requirement for inter-datacenter task migration. The simulation result specifies that proposed techniques give a significant improvement in makespan and resource utilization when compared with existing algorithms.

A hierarchical task scheduling strategy in mobile edge computing

Mobile Edge Computing (MEC) has attracted much attention from the global researchers because it supports the task migration from the cloud server to the mobile edge devices, which greatly improves the computation performance. Under MEC, this letter proposes a hierarchical task scheduling strategy. To be specific, the system framework includes the edge computing layer and the frog computing layer, in which the former is responsible for processing the simple tasks (outputting the sketchy result) and the latter is used to addressing the complex tasks (outputting the precise result). In particular, the tasks after going through the edge computing layer, they are allocated with the priorities; on this basis, the frog computing layer embeds a priority‐based task scheduling strategy. The experimental results demonstrate that the proposed hierarchical scheduling strategy not only is feasible but also has better delay performance and load balance performance than the benchmarks.