Reliability models for dataflow computer systems

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers

A Survey of Pipelined Workflow Scheduling: Models and Algorithms

International audienceA large class of applications need to execute the same workflow on different data sets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task-, data-, pipelined-, and/or replicated-parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors or optimization goals. This paper surveys the field by summing up and structuring known results and approaches

Analysis of Various Decentralized Load Balancing Techniques with Node Duplication

Experience in parallel computing is an increasingly necessary skill for today’s upcoming computer scientists as processors are hitting a serial execution performance barrier and turning to parallel execution for continued gains. The uniprocessor system has now reached its maximum speed limit and, there is very less scope to improve the speed of such type of system. To solve this problem multiprocessor system is used, which have more than one processor. Multiprocessor system improves the speed of the system but it again faces some problems like data dependency, control dependency, resource dependency and improper load balancing. So this paper presents a detailed analysis of various decentralized load balancing techniques with node duplication to reduce the proper execution time

Industry applications of neutral-atom quantum computing solving independent set problems

Architectures for quantum computing based on neutral atoms have risen to prominence as candidates for both near and long-term applications. These devices are particularly well suited to solve independent set problems, as the combinatorial constraints can be naturally encoded in the low-energy Hilbert space due to the Rydberg blockade mechanism. Here, we approach this connection with a focus on a particular device architecture and explore the ubiquity and utility of independent set problems by providing examples of real-world applications. After a pedagogical introduction of basic graph theory concepts of relevance, we briefly discuss how to encode independent set problems in Rydberg Hamiltonians. We then outline the major classes of independent set problems and include associated example applications with industry and social relevance. We determine a wide range of sectors that could benefit from efficient solutions of independent set problems -- from telecommunications and logistics to finance and strategic planning -- and display some general strategies for efficient problem encoding and implementation on neutral-atom platforms.Comment: 28 pages, 9 example application

Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

Energy Awareness and Scheduling in Mobile Devices and High End Computing

In the context of the big picture as energy demands rise due to growing economies and growing populations, there will be greater emphasis on sustainable supply, conservation, and efficient usage of this vital resource. Even at a smaller level, the need for minimizing energy consumption continues to be compelling in embedded, mobile, and server systems such as handheld devices, robots, spaceships, laptops, cluster servers, sensors, etc. This is due to the direct impact of constrained energy sources such as battery size and weight, as well as cooling expenses in cluster-based systems to reduce heat dissipation. Energy management therefore plays a paramount role in not only hardware design but also in user-application, middleware and operating system design. At a higher level Datacenters are sprouting everywhere due to the exponential growth of Big Data in every aspect of human life, the buzz word these days is Cloud computing. This dissertation, focuses on techniques, specifically algorithmic ones to scale down energy needs whenever the system performance can be relaxed. We examine the significance and relevance of this research and develop a methodology to study this phenomenon. Specifically, the research will study energy-aware resource reservations algorithms to satisfy both performance needs and energy constraints. Many energy management schemes focus on a single resource that is dedicated to real-time or nonreal-time processing. Unfortunately, in many practical systems the combination of hard and soft real-time periodic tasks, a-periodic real-time tasks, interactive tasks and batch tasks must be supported. Each task may also require access to multiple resources. Therefore, this research will tackle the NP-hard problem of providing timely and simultaneous access to multiple resources by the use of practical abstractions and near optimal heuristics aided by cooperative scheduling. We provide an elegant EAS model which works across the spectrum which uses a run-profile based approach to scheduling. We apply this model to significant applications such as BLAT and Assembly of gene sequences in the Bioinformatics domain. We also provide a simulation for extending this model to cloud computing to answers “what if” scenario questions for consumers and operators of cloud resources to help answers questions of deadlines, single v/s distributed cluster use and impact analysis of energy-index and availability against revenue and ROI

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function