Minimum Execution Time Research Articles

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

Minimizing execution time, energy consumption, and network load through scheduling algorithms is challenging for multi-processor-on-chip (MPSoC) based network-on-chip (NoC) systems. MPSoC based systems are prevalent in high performance computing systems. With the increase in computing capabilities of computing hardware, application requirements have increased many folds, particularly for real world scientific applications. Scheduling large scientific workflows consisting hundreds and thousands of tasks consume significant amount of time and resources. In this article, energy aware parallel scheduling techniques are presented primarily aimed at reducing the algorithm execution time while considering network load. Experimental results reveal that the proposed parallel scheduling algorithms achieve significant reduction in execution time.

Dynamic solution for multiple question optimization

The problem of multiple query optimization is considered as a complete optimization problem, the input parameters for optimization are determined: queries, tasks, plans. Proposed and substantiated architecture of the system in which the DBMS with the query optimizer is used - a computer system in which one or more computers (information processing system, one or more client computers, control computer, one or more the number of database servers) are interconnected by the client network and the server network. The data processing system receives the primary request made by one of the client computers, creates one or more secondary requests and transmits them to the database servers, in the case, if necessary, performs links or data updates, as defined in the primary request, and then returns the resulting data to the computer of the client from which the initial request was made. The I / O processing module, query analyzer, query optimizer, query processing module, optimization controller, external memory device are the elements that make up the information processing module. The I / O processing module receives the request from the client computers and the control request from the control computer and responds to these requests. The query optimizer optimizes the query using the query tree generated by the query analyzer, and develops a procedure for a series of operations (query execution plan) to obtain the results of query processing. The query processing module executes the query execution plan developed by the query optimizer. The order of execution of elementary requests which provide the minimum time for one, two and three processors is developed. A comparison of multi-request execution time with joint and incompatible processing depending on the number of processors is presented. The minimum execution time of the query can be achieved by executing elementary queries in the appropriate order, determined by the condition of order.

Cryptanalysis of Internet of Health Things Encryption Scheme Based on Chaotic Maps

Many encryption algorithms are designed to decrease the probability of cyberattacks by assuring data security as well as system and participant authentication. However, in the development of computer resources cryptanalytical techniques have been developed and performed competitively in information security with good results. In this paper, we reported security flaws in the recently offered encryption technique based on a chaotic map for Internet of Health Things (IoHT) security. The scheme was based on a new chaotic map, modified Mandelbrot set, and conditional shift algorithm asserting the encryption algorithm is secure. We have performed some cryptographic attacks to retrieve the key from the understudy cryptosystem. The key was retrieved in little computation by using a chosen-plaintext attack and one known plaintext ciphertext pair. The minimum execution time of performed attacks indicates the vulnerability of the diffusion-based encryption algorithm. To enhance the security of the understudy cryptographic algorithm, we have also suggested certain improvements.

RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment

Fog Computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The ModuleScheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by CompareModule. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm.

Packet Optimization of Software Defined Network Using Lion Optimization

There has been an explosion of cloud services as organizations take advantage of their continuity, predictability, as well as quality of service and it raises the concern about latency, energy-efficiency, and security. This increase in demand requires new configurations of networks, products, and service operators. For this purpose, the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization. This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests. Performance is evaluated in terms of reducing bandwidth, task execution times and latencies, and increasing throughput. A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment. The proposed work is shown to improve the throughput and latency rate.

A Proposed Data Partitioning Approach on Heterogeneous HPC Platforms: Data Locality Perspective

We propose a new data partitioning approach to improve the performance of heterogeneous parallel applications in modern high-performance computing (HPC) systems. Existing approaches do not consider an important aspect that has a critical impact on the performance of parallel applications: the method of assigning partitions to each processor so as to minimize the communication cost and hence minimize data movement, which dominates energy and performance cost. Such an aspect for managing data locality is important for a large range of applications. Therefore, to achieve efficient data partitioning, we propose a method for distribution considering this aspect. Our algorithm seeks to minimize execution time by using two models. The first is a fine-grained computational model of heterogeneous processors, which is sufficiently adequate and accurate to guarantee efficient partitioning results that maximize utilization. The second is a communication model of heterogeneous processors to minimize data motion and hide communication overheads. The correctness of our algorithm was analyzed and validated. The complexity of our algorithm is approximately of order <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> + <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> ), where s is problem size/steps (where steps is the step size between data points in the computational model of each processor), and p is the number of heterogeneous processors. The experiments were performed on AZIZ supercomputer using two types of applications: an application with no dependency between its partitions, i.e., matrix multiplication, and another one with high dependency between its partitions, i.e., the Jacobi method. The results show the efficiency of our algorithm in improving performance.

Optimum time-energy-jerk trajectory planning for serial robotic manipulators by reparameterized quintic NURBS curves

This paper deals with the trajectory planning for serial robotic manipulators passing through key points by minimizing execution time, energy consumption and joint jerks. Quintic NURBS curves are adopted to fit the trajectory, of which the trajectory is reparameterized with respect to time for generation of geometric path and motion laws, aiming at continuity of the robot velocity, acceleration and jerk. A trajectory planning approach for optimum robot performance is proposed by solving a multi-objective optimization problem to attain optimal curve parameters and distributed execution time along curve segments simultaneously. The proposed technique of trajectory planning is numerically illustrated with a robotic arm and evaluated by experimental measurements. The comparison of total execution time and joint dynamics with/without variables optimization shows the effectiveness of the proposed approach.

Enhanced multi-verse optimizer for task scheduling in cloud computing environments

Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization.

A novel dynamic source routing (DSR) protocol based on minimum execution time scheduling and moth flame optimization (MET-MFO)

In general, the conventional routing approaches in mobile ad hoc networks (MANETs) provide a route between the source and the target with a minimum hop count. Dynamic source routing (DSR) as a typical prototype of routing protocols relies on the minimum hop count parameter to provide the path without considering any other factors such as energy consumption and node energy level, which significantly affect the routing algorithm performance. To enhance the performance of the DSR, a novel and efficient routing mechanism based on a hybrid approach using the minimum execution time (MET) scheduling and moth flame optimization (MFO) scheme is proposed. This hybridization version of DSR is called the (MET-MFODSR) algorithm, and it aims to improve the routing mechanism through establishing an optimal route with minimum energy consumption, which increases the network lifetime and reduces route failure issues. The proposed MET-MFODSR protocol is implemented using the MATLAB platform, analyzed and evaluated in different simulation environments. The simulation results demonstrate that the suggested routing algorithm is applicative and practicable, and its performance exceeds the performance of the existing Bee DSR (BEEDSR) and Bee-inspired protocol (BeeIP) algorithms.

Fast Scheduling in Distributed Transactional Memory

We investigate scheduling algorithms for distributed transactional memory systems where transactions residing at nodes of a communication graph operate on shared, mobile objects. A transaction requests the objects it needs, executes once those objects have been assembled, and then possibly forwards those objects to other waiting transactions. Minimizing execution time in this model is known to be NP-hard for arbitrary communication graphs, and also hard to approximate within any factor smaller than the size of the graph. Nevertheless, networks on chips, multi-core systems, and clusters are not arbitrary. Here, we explore efficient execution schedules in specialized graphs likely to arise in practice: Clique, Line, Grid, Cluster, Hypercube, Butterfly, and Star. In most cases, when individual transactions request k objects, we obtain solutions close to a factor O(k) from optimal, yielding near-optimal solutions for constant k. These execution times approximate the TSP tour lengths of the objects in the graph. We show that for general networks, even for two objects (k = 2), it is impossible to obtain execution time close to the objects’ optimal TSP tour lengths, which is why it is useful to consider more realistic network models. To our knowledge, this is the first attempt to obtain provably fast schedules for distributed transactional memory.

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

Minimum Time Extrema Estimation for Large-Scale Radio-Frequency Identification Systems

We consider the extrema estimation problem in large-scale radio-frequency identification (RFID) systems, where there are thousands of tags and each tag contains a finite value. The objective is to design an extrema estimation protocol with the minimum execution time. Because the standard binary search protocol wastes much time due to interframe overhead, we propose a parameterized protocol and treat the number of slots in a frame as an unknown parameter. We formulate the problem and show how to find the best parameter to minimize the worst-case execution time. Finally, we propose two rules to further reduce the execution time. The first is to find and remove redundant frames. The second is to concatenate a frame from minimum value estimation with a frame from maximum value estimation to reduce the total number of frames. Simulations show that, in a typical scenario, the proposed protocol reduces execution time by 79% compared with the standard binary search protocol.

Intelligent Adaptation of Hardware Knobs for Improving Performance and Power Consumption

Current microprocessors include several knobs to modify the hardware behavior in order to improve performance, power, and energy under different workload demands. An impractical and time consuming offline profiling is needed to evaluate the design space to find the optimal knob configuration. Different knobs are typically configured in a decoupled manner to avoid the time-consuming offline profiling process. This can often lead to underperforming configurations and conflicting decisions that jeopardize system power-performance efficiency. Thus, a dynamic management of the different hardware knobs is necessary to find the knob configuration that maximizes system power-performance efficiency without the burden of offline profiling. In this article, we propose libPRISM, an infrastructure that enables the transparent management of multiple hardware knobs in order to adapt the system to the evolving demands of hardware resources in different workloads. libPRISM can minimize execution time, energy-delay product or power consumption by dynamically managing the SMT level, the data prefetcher, and the DVFS hardware knobs. Overall, the proposed solutions increase performance up to 130 percent (16.9 percent on average), reduce energy-delay product up to 80 percent, and reduce power consumption up to 33 percent depending on the target metric compared to the default knob configuration of the system.

Efficient task scheduling on virtual machine in cloud computing environment

PurposeCloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.Design/methodology/approachCloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.FindingsTo evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.Originality/valueThe simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite

&lt;table width="0" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="593"&gt;&lt;p&gt;In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;

Multi Cloud Based Service Recommendation System Using DBSCAN Algorithm

The historical collaborative filtering based recommendation system has become the most essential one in cloud computing environment. The recommender system which has been depends on collaborative filtering easily identify the user references and allow to learn relationship items—past users from the user group who exhibit similar preferences. Hence, the recommendation system has been considered as the most powerful tool for cloud providers and users. This paper proposed the clustering recommendation system executed in cloud environment. The accuracy of the system reduced when irrelevant features presents in data. So that in this proposed scheme, an effective feature selection approach named as modified LDA has been utilized for acquiring the relevant information only. The LDA technique defined as Linear Discriminant Analysis which decreased features numbers to expected value before classification process. DBSCAN is utilized as a clustering approach which provides better quality in terms of segregating the number of movies. Based on the genre, the similar movies are clustered together with the user ratings. DBSCAN elaborated as Density Based Spatial Clustering of Applications with Noise termed as famous method of learning or clustering which detached the high density clusters from low density clusters. The LDA technique has been utilized to accomplish the appropriate user reviews with categorized ratings. In this paper, the main challenge described as to analyze the reviews of the user with ratings using best clustering approach. The evaluation results proved the highest accuracy of 92.56% when compared with various methods of accuracy. This proposed method also has minimum execution time.

AFCL: An Abstract Function Choreography Language for serverless workflow specification

Serverless workflow applications or function choreographies (FCs), which connect serverless functions by data- and control-flow, have gained considerable momentum recently to create more sophisticated applications as part of Function-as-a-Service (FaaS) platforms. Initial experimental analysis of the current support for FCs uncovered important weaknesses, including provider lock-in, and limited support for important data-flow and control-flow constructs. To overcome some of these weaknesses, we introduce the Abstract Function Choreography Language (AFCL) for describing FCs at a high-level of abstraction, which abstracts the function implementations from the developer. AFCL is a YAML-based language that supports a rich set of constructs to express advanced control-flow (e.g. parallelFor loops, parallel sections, dynamic loop iterations counts) and data-flow (e.g multiple input and output parameters of functions, DAG-based data-flow). We introduce data collections which can be distributed to loop iterations and parallel sections that may substantially reduce the delays for function invocations due to reduced data transfers between functions. We also support asynchronous functions to avoid delays due to blocking functions. AFCL supports properties (e.g. expected size of function input data) and constraints (e.g. minimize execution time) for the user to optionally provide hints about the behavior of functions and FCs and to control the optimization by the underlying execution environment. We implemented a prototype AFCL environment that supports AFCL as input language with multiple backends (AWS Lambda and IBM Cloud Functions) thus avoiding provider lock-in which is a common problem in serverless computing. We created two realistic FCs from two different domains and encoded them with AWS Step Functions, IBM Composer and AFCL. Experimental results demonstrate that our current implementation of the AFCL environment substantially outperforms AWS Step Functions and IBM Composer in terms of development effort, economic costs, and makespan.

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention.&#x0D; Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

Rectilinear Tasks Optimization of a Modular Serial Metamorphic Manipulator

Abstract This paper proposes a method for optimizing rectilinear tasks on a 3 degrees-of-freedom (DoF) modular serial metamorphic manipulator. The overall experimental process was designed in order for theoretical assumptions and previous experimental results, regarding the characteristics of a class of reconfigurable manipulators to be verified. The optimization procedure undergoes two stages. In each stage, the tasks are initially simulated and the optimal solutions obtained are afterward evaluated in the manipulator. Optimal task placement in the configuration space of the reference anatomy is concerned in the first optimization stage. Two different kinematic manipulability measures are utilized to form the objective function of the genetic algorithm (GA) used. Determination of the optimal anatomy for each task execution is concerned in the second stage. All feasible anatomies are exhaustively evaluated, and the anatomy with minimum execution time achieved in simulation is considered as optimal. The simulated tasks are executed for the reference and the optimal anatomy extracted. Overall task execution time reduction is measured. For tasks executed, Tool Center Point (TCP) position and velocity are obtained from navigation equations using measurements from an inertial measurement unit (IMU) sensor. In order to obtain more accurate solutions from position and velocity equations, a Kalman filter (KF) algorithm is implemented. Finally, conclusions are made based on the results of each task execution. Overall the metamorphic manipulator can achieve higher kinematic performance and minimize task execution time for the optimal anatomy calculated. Optimal task placement for the reference anatomy also reduces the task execution time.

Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.