Execution Time Constraints Research Articles

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

Demand Response as a Service: Clearing Multiple Distribution-Level Markets

The uncertain and non-dispatchable nature of renewable energy sources renders Demand Response (DR) a critical component of modern electricity distribution systems. Demand Response (DR) service provision takes place via aggregators and special distribution-level markets (e.g., flexibility markets), where small, distributed DR resources, such as building energy management systems, electric vehicle charging stations, micro-generation and storage, connected to the low-voltage distribution grid, offer DR services. In such systems, energy balancing (and thus, also DR decisions) have to be made close to real-time. Thus, market clearing algorithms for DR service provision must fulfill several requirements related to the efficiency of their operation. More specifically, a DR market clearing algorithm needs to be optimal in terms of cost-efficiency, scalable in terms of number of assets and locations, and able to satisfy real-time constraints. In order to cope with these challenges, this article presents a distributed DR market clearing algorithm based on Lagrangian decomposition, combined with an optimal cloud resource allocation algorithm for assigning the required computation power. A heuristic algorithm is also presented, able to achieve a near-optimal solution, within negligible computational time. Simulations, performed on a testbed, demonstrate the computational burden introduced by various DR models, as well as the heuristic algorithm's near-optimal performance. The resource allocation algorithm is able to service multiple DR requests (e.g., in multiple distribution networks), and minimize the cost of computational resources while respecting the execution time constraints of each request. This enables third parties to offer cost-efficient and competitive DR operation as a service.

5G communication resource allocation strategy based on edge computing

Aiming at the problem that most of the existing strategies cannot deal with the explosive growth of user data after the popularization of 5G communication technology, this paper proposes a 5G communication resource allocation strategy based on edge computing. First, the 5G communication network is virtualized to build mobile edge computing system with multiple mobile user equipment and multiple servers. Then, the communication model and calculation model of system are established. Besides, the optimization objectives are proposed based on this, which can minimize total user cost and maximize the success rate of task offloading and successful execution rate. Finally, the resource allocation problem is formalized and modelled as Markov model. The objective is to minimize the ratio of overall system energy consumption to task execution time and delay constraints, the optimal communication resource allocation scheme is obtained by solving it based on Q-learning algorithm. The experimental demonstration of proposed strategy is carried out based on the python simulation platform. Experimental results show that the total system cost and average delay of proposed strategy do not exceed 500 W and 1000 ms under the experimental conditions; its overall performance is better than other comparison strategies, can meet the interest requirements of users and service providers.

Preemptive Mobile Computation Offloading With Hard Deadlines and Concurrent Local Execution

This paper considers Preemptive Mobile Computation Offloading when concurrent local execution (CLE) is used to guarantee task execution time constraints. By allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of unforeseen wireless channel conditions. In the preemptive offloading case, at the start of each time slot, a decision is made to either continue or temporarily interrupt the offload. This mechanism allows the system to adapt when channel conditions change. The paper considers the case for homogeneous Markovian wireless channels. Using Markovian decision process stopping theory, an online energy-optimal computation offloading algorithm is formulated for preemptive offloading, referred to as Optimal Preemptive Offloading (OPO). Since the computational complexity of OPO can be prohibitive, the paper introduces three computationally efficient techniques motivated by OPO: 1) water-filling; 2) water-filling with scheduling; and 3) generalized water-filling. For each method, two variations are considered. The first (Equ) uses the equilibrium channel state probabilities in offloading decision calculations, and the second (Exp) uses Markovian transition matrix exponentiation. This results in six algorithms with a wide variety of energy performance and computational complexity. Performance of the algorithms is compared that shows the tradeoffs between complexity and mobile energy saving performance.

Optimal multi-part mobile computation offloading with hard deadline constraints

This paper considers mobile computation offloading when concurrent local execution (CLE) is used to enforce task execution time constraints. This mechanism can ensure that hard task deadlines are satisfied regardless of any randomness induced by the wireless channel, network or cloud servers. In this type of system, mobile device energy may be reduced by segmenting a task upload into multiple parts, rather than doing a conventional contiguous task upload. The paper uses this mechanism to adapt to changes in channel conditions during the offload. Unlike the contiguous task offload case however, the upload initiation times of each part must be determined dynamically. This is done while ensuring that hard task deadlines are always satisfied. In this paper, the multi-part computation offloading case is considered. In multi-part offloading, the task to be offloaded is partitioned into K upload parts before any offload initiation decisions are made. In this case, current channel state information is incorporated into the offload decisions, and the system must always satisfy a hard task execution time constraint using concurrent local execution. The paper considers the case for Markovian wireless channels. A provably energy-optimal online computation offloading algorithm (MuliOpt) is introduced for multi-part offloading. MultiOpt is shown to be optimal using Markovian decision process stopping theory. Since the computational complexity of MultiOpt can be significant, simpler and more computationally efficient heuristics, which also respect the hard task execution deadline, may be used. The paper introduces two such heuristics, the Immediate Offloading, and Multi Threshold algorithms. The mobile energy use of MultiOpt is compared to these heuristics, as well as to local execution without offloading and an offline energy bound. Simulation results show that MultiOpt performs significantly better when compared to the proposed heuristics, as well as when K increases.

Adaptive Modular Multilevel Converter Model for Electromagnetic Transient Simulations

This paper proposes an adaptive model of modular multilevel converter (MMC) for electromagnetic transient (EMT) simulations. The model is applicable to MMCs with arbitrary numbers of half-bridge and full-bridge submodules. The proposed design includes average value model, arm equivalent model, and detailed equivalent model. It allows smoothly transitioning from one model to another during time-domain simulations depending on the desired accuracy and execution time constraints. Modifications required in conventional MMC models to achieve smooth transitions are presented in the paper. Time-domain initialization methods are developed for each constituting model, including initialization of the appropriate control system blocks. Validity and effectiveness of the proposed adaptive model is demonstrated using EMT simulations of 401-level MMC-HVDC system.

Fault tolerance for a scientific workflow system in a Cloud computing environment

High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed resources. However, earlier clustering methods ignore the effect of failures on the system, despite their significant impact on large-scale distributed resources, such as Clouds. In this paper, we present a new fault-tolerant task clustering method called FT-HCC that is designed by including the workflow execution time (makespan) and execution cost constraints, which are used to increase workflow performance. The proposed method is implemented and evaluated in a simulation-based approach, using a real-time workflow execution to analyze performance improvement. The results consolidate that the proposed strategies and techniques work efficiently in terms of fault tolerance and improve both workflow makespan and execution cost when compared to existing approaches.

An energy saving based on task migration for mobile edge computing

Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of “how to realize low-energy migration of complex dependent applications,” a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users’ expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

Progressive Wasserstein Barycenters of Persistence Diagrams.

This paper presents an efficient algorithm for the progressive approximation of Wasserstein barycenters of persistence diagrams, with applications to the visual analysis of ensemble data. Given a set of scalar fields, our approach enables the computation of a persistence diagram which is representative of the set, and which visually conveys the number, data ranges and saliences of the main features of interest found in the set. Such representative diagrams are obtained by computing explicitly the discrete Wasserstein barycenter of the set of persistence diagrams, a notoriously computationally intensive task. In particular, we revisit efficient algorithms for Wasserstein distance approximation [12,51] to extend previous work on barycenter estimation [94]. We present a new fast algorithm, which progressively approximates the barycenter by iteratively increasing the computation accuracy as well as the number of persistent features in the output diagram. Such a progressivity drastically improves convergence in practice and allows to design an interruptible algorithm, capable of respecting computation time constraints. This enables the approximation of Wasserstein barycenters within interactive times. We present an application to ensemble clustering where we revisit the k-means algorithm to exploit our barycenters and compute, within execution time constraints, meaningful clusters of ensemble data along with their barycenter diagram. Extensive experiments on synthetic and real-life data sets report that our algorithm converges to barycenters that are qualitatively meaningful with regard to the applications, and quantitatively comparable to previous techniques, while offering an order of magnitude speedup when run until convergence (without time constraint). Our algorithm can be trivially parallelized to provide additional speedups in practice on standard workstations. We provide a lightweight C++ implementation of our approach that can be used to reproduce our results.

Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems

In this paper, we study a class of minimum-energy scheduling problems in non-orthogonal multiple access (NOMA) systems. NOMA is adopted to enable efficient channel utilization and interference mitigation, such that base stations can consume minimal energy to empty their queued data in presence of transmission deadlines, and each user can obtain all the requested data timely. Due to the high computational complexity in resource scheduling and the stringent execution-time constraints in practical systems, providing a time-efficient and high-quality solution to 5G real-time systems is challenging. The conventional iterative optimization approaches may exhibit their limitations in supporting online optimization. We herein explore a viable alternative and develop a learning-assisted optimization framework to improve the computational efficiency while retaining competitive energy-saving performance. The idea is to use deep-learning-based predictions to accelerate the optimization process in conventional optimization methods for tackling the NOMA resource scheduling problems. In numerical studies, the proposed optimization framework demonstrates high computational efficiency. Its computational time is insensitive to the input size. The framework is able to provide optimal solutions as long as the learning-based predictions satisfy a derived optimality condition. For the general cases with imperfect predictions, the algorithmic solution is error-tolerable and performance scaleable, leading the energy-saving performance close to the global optimum.

CuRnet: an R package for graph traversing on GPU

BackgroundR has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms.ResultsThis work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis.ConclusionscuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

BULLET: Particle Swarm Optimization Based Scheduling Technique for Provisioned Cloud Resources

Cloud resource scheduling requires mapping of cloud resources to cloud workloads. Scheduling results can be optimized by considering Quality of Service (QoS) parameters as inherent requirements of scheduling. In existing literature, only a few resource scheduling algorithms have considered cost and execution time constraints but efficient scheduling requires better optimization of QoS parameters. The main aim of this research paper is to present an efficient strategy for execution of workloads on cloud resources. A particle swarm optimization based resource scheduling technique has been designed named as BULLET which is used to execute workloads effectively on available resources. Performance of the proposed technique has been evaluated in cloud environment. The experimental results show that the proposed technique efficiently reduces execution cost, time and energy consumption along with other QoS parameters.

Computational resource management for data‐driven applications with deadline constraints

SummaryRecent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user‐defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud‐based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints.

Optimization Approach for Resource Allocation on Cloud Computing for IoT

Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS (Quality of Service) constraints are satisfied and provider's profit is maximized. In order to increase the profit, the penalty cost for SLA (Service Level Agreement) violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job's urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider's profit and success rate of job completion with conventional mechanism using real workload data.

Determining map partitioning to minimize wind field uncertainty in forest fire propagation prediction

Wind speed and direction are the parameters that most significantly affect forest fire propagation. The accurate estimation of such parameters is critical in providing precise predictions of forest fire propagation. Wind speed and direction can be measured in meteorological stations or estimated from meteorological models, but, in both cases, they are obtained at a very low resolution. Moreover, the meteorological wind is modified by the terrain topography and a complete wind field is generated with different wind speed and direction at any point of the terrain. So, wind field models providing wind speed and direction at a very high resolution (30m) are crucial in forest fire propagation prediction. WindNinja is one of the most widely used wind field simulators in this area. However, when the terrain map under consideration is very large (30km×30km or more), the execution time becomes unaffordable and the simulator cannot be used in real operation. A map partitioning strategy was developed to parallelize the wind field calculation and reduce the execution time to make it affordable. Map partitioning introduces a certain error in wind field that is propagated to forest fire propagation prediction. So, a complete methodology has been developed to determine the map partitioning that accomplishes real operation execution time constraints and keeps the forest fire propagation error below feasible limits. The experimental results show the execution time reduction accomplished and the accuracy of the wind field generated and forest fire propagation prediction.

HARP2: An X-Scale Reconfigurable Accelerator-Rich Platform for Massively-Parallel Signal Processing Algorithms

This paper presents design, development and evaluation of an eXtra-large Scale, Homogeneous and a Heterogeneous Accelerator-Rich Platform (HARP2) for massively parallel signal processing algorithms. HARP is an integrated platform of multiple Coarse-Grained Reconfigurable Arrays (CGRAs) over a Network-on-Chip (NoC) where each CGRA is scaled and tailored for a specific application. The architecture of the NoC consists of nine nodes in a topology of 3-rows ? 3-columns and acts as backbone of communication between different CGRAs. In this experimental work, the HARP template is used to instantiate a homogeneous (HARP-hom) and a heterogeneous (HARP-het) platform. The HARP-het is generated for a proof-of-concept test to verify the design and functionality of HARP. It also provides insight to many features of the design and evaluation in terms of different performance metrics. The other version (HARP-hom) is instantiated for a relatively realistic design problem, i.e., satisfying the execution-time constraints imposed on Fast Fourier Transform processing in IEEE-802.11n demodulators. Both of the versions of HARP are treated for comparative analysis using different performance metrics against some of the existing state-of-the-art platforms. The HARP versions are designed to illustrate large-scale homogeneous/heterogeneous multicore architectures while presenting the advantages of maximizing the number of reconfigurable processing resources on a single chip.

Hardware resource utilization optimization in FPGA-based Heterogeneous MPSoC architectures

Next generation FPGA circuits will allow the integration of dozens of hard and soft cores as well as dedicated accelerators in the same chip. These Heterogeneous Multiprocessor System-on-Chip (Ht-MPSoC) architectures will allow the design of very complex System-on-Chips (SoC) on a single FPGA chip and will fulfill modern application requirements, in terms of performance/energy consumption ratio. In this paper, we extend existing FPGA-based Ht-MPSoC architectures by considering sharing hardware accelerators among the cores. In these architectures, cores on the FPGA may have different resources that can be shared in different manners. To explore the large space of possible configurations of Ht-MPSoC on FPGA, designer needs a fast and accurate exploration tool. For this reason, a Mixed Integer Programming (MIP) model is also proposed to determine the Ht-MPSoC configuration that consumes the least HW resources while respecting the application execution time constraints. Using our MIP model, the design space of several hundreds of private and shared HW accelerators can be explored in a reasonable time with high accuracy.

Scheduling for energy minimization on restricted parallel processors

Scheduling for energy conservation has become a major concern in the field of information technology because of the need to reduce energy use and carbon dioxide emissions. Previous work has focused on the assumption that a task can be assigned to any processor. In contrast, we initially study the problem of task scheduling on restricted parallel processors. The restriction takes account of affinities between tasks and processors; that is, a task has its own eligible set of processors. We adopt the Speed Scaling (SS) method to save energy under an execution time constraint (on the makespan Cmax), and the processors can run at arbitrary speeds in [smin,smax]. Our objective is to minimize the overall energy consumption. The energy-efficient scheduling problem, involving task assignment and speed scaling, is inherently complex as it is proved to be NP-complete for general tasks. We formulate the problem as an Integer Programming (IP) problem. Specifically, we devise a polynomial-time optimal scheduling algorithm for the case in which tasks have a uniform size. Our algorithm runs in O(mn3logn) time, where m is the number of processors and n is the number of tasks. We then present a polynomial-time algorithm that achieves a bounded approximation factor when the tasks have arbitrary-size work. Numerical results demonstrate that our algorithm could provide an energy-efficient solution to the problem of task scheduling on restricted parallel processors.

Adapting Map Resolution to Accomplish Execution Time Constraints in Wind Field Calculation

Abstract Forest fire propagation prediction is a key point to fight against such hazards. Several models and simulators have been developed to predict forest fire propagation. These models require input parameters such as digital elevation map, vegetation map, and other parameters describing the vegetation and meteorological conditions. Coupling wind field model and forest fire propagation model improves accuracy prediction, but increases significantly prediction time. This fact is critical since propagation prediction must be provided in advance to allow the control centers to manage firefighters in the best possible way. This work analyses WindNinja execution time, describes a WindNinja parallelisation based on map partitioning, determines the limitations of such methodology for large maps and presents an improvement based on adapting map resolution to accomplish execution time limitations.