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Hadoop is a popular open source framework that supports the processing and storage of large data sets using the MapReduce programming model. To run intensive-data that changes over time, Hadoop decoupling resource management and processing framework is used. Hadoop workloads can run on both Homogeneous and Heterogeneous infrastructures, but running workload in Heterogeneous cluster leads to several...
Cloud computing is one of the emerging technology which is rapidly developing nowadays in the current environment. The Large Scale Organizations are shifting their databases to cloud due to the various services provided by the Cloud Platforms. IAAS is the one of the services which is intended to provide the Client Centric Service to the client as per their need. Large amount of Virtual Machines (VM)...
Antenna selection methods have been proposed to increase the capacity of massive multiple-antenna systems. In this paper, first, a comparison is made between several common antenna selection methods, and the best antenna selection method in terms of increasing transmission channel capacity and reducing the computational complexity is determined. Then, to improve downlink channel capacity in a cell...
Optimizing security dynamic scheduling method in cloud storage is significant for improving resource date throughput and storage space of a cloud storage system. The paper presents a self-adaptive layered sleep vision-based method for security dynamic scheduling in cloud storage. A decision-making tree model is used for feature classification of cloud storage resources scheduling, top-down analytical...
The cloud computing system (CCS) is with large scale and complex structure. Benefited from its enabling technology, virtualization, the CCS has the characteristics of elastic, providing on-demand service according to resource scheduling strategy. Different resource scheduling strategies lead to different operation hours, which have different effects on the reliability of cloud computing infrastructure...
A large variety of modern technologies fade the borders between the cyber and the physical worlds. Nonetheless, the two-dimensional architecture of cyber-physical systems also enabled the proliferation of innovative attacks where traditional computer systems malware caused significant damages to physical infrastructures, such as the power grid. In this work, we propose a methodology that provides...
The advancement in the fields of science, engineering, social networking and e-commerce along with the tremendous growth in the pervasive technologies has generated a tsunami of data in digital form. To store and process this type of data is a big challenge for the researcher. Different distributed computing and processing systems have been developed to overcome these real world data computational...
Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing interest, underlying the trustworthiness of the experiments and the conclusions derived from experiments. Dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and...
Parallel applications are highly irregular and high performance computing (HPC) infrastructures are very complex. The HPC applications of interest herein are timestepping scientific applications (TSSA). Often, TSSA involve the repeated execution of multiple parallel loops with thousands of iterations and irregular behavior. Dynamic loop scheduling (DLS) techniques were developed over time and have...
Although cloud computing greatly utilises virtualised environments for applications to be executed efficiently in low-cost hosting, it has turned energy wasting and overconsumption issues into major concerns. Cloud infrastructure is built on a great amount of server equipment, including high performance computing (HPC), and the servers are naturally prone to failures.In this paper, we report on an...
We address the topology-aware job scheduling and placement problems on 3D torus-based high performance computing systems, with the objective of reducing system fragmentation. In our previous work, we proposed a job placement algorithm based on a local migration process, which aims at reducing the internal fragmentation due to using a convex prism shape for job allocation. However, HPC systems are...
We consider a system that a single batch processing machine can serve compatible job families (jobs from different families can be processed together in the same batch). The system is characterized by random jobs arrival, random processing time and unlimited buffer capacities. Optimization scheduling method needs to be used with the objective to minimize average cycle time in the long run. First,...
As cloud computing is growing rapidly, efficient task scheduling algorithm plays a vital role to improve the resource utilization and enhance overall performance of the cloud computing environment. However, task scheduling is the severe challenge needed to solve urgently in cloud computing. Therefore, the simulated annealing multi-population genetic algorithm (SAMPGA) is proposed for task scheduling...
Effective big data analysis is one of the most notable research challenge of the latest few years. Hadoop, the most popular implementation of the MapReduce framework, has today become widespread used for processing large data sets using cloud resources. However, in many scenarios, data are geographically distributed over data centers and moving them to a single site for processing may result extremely...
Coflow scheduling without prior knowledge has been proposed recently. However, the previous solution, Aalo, has two problems: it does not explicitly control the flow rate; it assumes the network is ideally non-blocking (with perfect traffic balancing and no oversubscription). In this paper, we show the performance loss caused by the above two problems. We propose EAalo to cope with the problems. It...
This paper investigates networked control systems using random sampling method which can be tuned by a continue time Markov chain. The sampling instants are modeled by using jumps between states of a continue time Markov chain. Whenever there is a jump from a state in the Markov chain to a state that represent a subsystem in the networked system, we sample that particular subsystem and transmit its...
As big data analytics frameworks are developing towards larger degrees of parallelism and shorter task durations to provide lower latency, millions of scheduling decisions per second pose a great challenge to centralized schedulers. Therefore, increasing efforts are devoted to the study of distributed scheduling approaches to avoid the throughput limitation of centralized designs. Among these approaches,...
Big data analytics frameworks are developing towards larger degrees of parallelism and shorter task durations to achieve lower latency. Consequently, millions of scheduling decisions need to be made per second, which has posed a big challenge to today's centralized schedulers. Therefore, many researchers and enterprises turn to distributed scheduling approaches to avoid the throughput limitation of...
In this work an Auto Guided Vehicles (AGV) scheduling and routing problem is considered with maximal and minimal time lags along with heterogeneous characteristics. A framework with dynamic flow control has been proposed for the execution of events dedicated to a fleet of AGVs. The proposed framework allows the setting up for missions which are defined by a set of routes and stations regardless of...
The optimal scheduling algorithms in real-time multiprocessor systems are considered impractical. This is mainly because of the overhead generated due to the frequent scheduling points, migrations and preemptions. The solution to this problem is either to propose new algorithms with less overhead or to improve the existing ones. In this article, some simple heuristics to control the overhead are proposed...
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