Parallel process placement

This thesis investigates methods of automatic allocation of processes to available processors in a given network configuration. The research described covers the investigation of various algorithms for optimal process allocation. Among those researched were an algorithm which used a branch and bound technique, an algorithm based on graph theory, and an heuristic algorithm involving cluster analysis. These have been implemented and tested in conjunction with the gathering of performance statistics during program execution, for use in improving subsequent allocations. The system has been implemented on a network of loosely-coupled microcomputers using multi-port serial communication links to simulate a transputer network. The concurrent programming language occam has been implemented, replacing the explicit process allocation constructs with an automatic placement algorithm. This enables the source code to be completely separated from hardware consideration

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

Not availabl

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Self-management Framework for Mobile Autonomous Systems

The advent of mobile and ubiquitous systems has enabled the development of autonomous systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application domains comes a new challenge – enabling self-management in mobile autonomous systems. The primary challenge in using autonomous systems for real-life missions is shifting the burden of management from humans to these systems themselves without loss of the ability to adapt to failures, changes in context, and changing user requirements. Autonomous systems have to be able to manage themselves individually as well as to form self-managing teams that are able to recover or adapt to failures, protect themselves from attacks and optimise performance. This thesis proposes a novel distributed policy-based framework that enables autonomous systems to perform self management individually and as a team. The framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach for optimal role assignment, caters for communication link maintenance among team members and recovery from failure. Adaptive management is achieved by employing an architecture that uses policy-based techniques to allow dynamic modification of the management strategy relating to resources, role behaviour, team and communications management, without reloading the basic software within the system. Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems participating in the mission. It is also shown to be optimal with respect to role assignments, and robust to intermittent communication link disconnections and permanent team-member failures. The prototype implementation was tested on mobile robots as a proof-ofconcept demonstration

Distributed Online Rollout for Multivehicle Routing in Unmapped Environments

In this work we consider a generalization of the well-known multivehicle routing problem: given a network, a set of agents occupying a subset of its nodes, and a set of tasks, we seek a minimum cost sequence of movements subject to the constraint that each task is visited by some agent at least once. The classical version of this problem assumes a central computational server that observes the entire state of the system perfectly and directs individual agents according to a centralized control scheme. In contrast, we assume that there is no centralized server and that each agent is an individual processor with no a priori knowledge of the underlying network (including task and agent locations). Moreover, our agents possess strictly local communication and sensing capabilities (restricted to a fixed radius around their respective locations), aligning more closely with several real-world multiagent applications. These restrictions introduce many challenges that are overcome through local information sharing and direct coordination between agents. We present a fully distributed, online, and scalable reinforcement learning algorithm for this problem whereby agents self-organize into local clusters and independently apply a multiagent rollout scheme locally to each cluster. We demonstrate empirically via extensive simulations that there exists a critical sensing radius beyond which the distributed rollout algorithm begins to improve over a greedy base policy. This critical sensing radius grows proportionally to the $\log^*$ function of the size of the network, and is, therefore, a small constant for any relevant network. Our decentralized reinforcement learning algorithm achieves approximately a factor of two cost improvement over the base policy for a range of radii bounded from below and above by two and three times the critical sensing radius, respectively

Distributed and Multiprocessor Scheduling

This chapter discusses CPU scheduling in parallel and distributed systems. CPU scheduling is part of a broader class of resource allocation problems, and is probably the most carefully studied such problem. The main motivation for multiprocessor scheduling is the desire for increased speed in the execution of a workload. Parts of the workload, called tasks, can be spread across several processors and thus be executed more quickly than on a single processor. In this chapter, we will examine techniques for providing this facility. The scheduling problem for multiprocessor systems can be generally stated as \How can we execute a set of tasks T on a set of processors P subject to some set of optimizing criteria C? The most common goal of scheduling is to minimize the expected runtime of a task set. Examples of other scheduling criteria include minimizing the cost, minimizing communication delay, giving priority to certain users\u27 processes, or needs for specialized hardware devices. The scheduling policy for a multiprocessor system usually embodies a mixture of several of these criteria. Section 2 outlines general issues in multiprocessor scheduling and gives background material, including issues specific to either parallel or distributed scheduling. Section 3 describes the best practices from prior work in the area, including a broad survey of existing scheduling algorithms and mechanisms. Section 4 outlines research issues and gives a summary. Section 5 lists the terms defined in this chapter, while sections 6 and 7 give references to important research publications in the area

Mapping of portable parallel programs

An efficient parallel program designed for a parallel architecture includes a detailed outline of accurate assignments of concurrent computations onto processors, and data transfers onto communication links, such that the overall execution time is minimized. This process may be complex depending on the application task and the target multiprocessor architecture. Furthermore, this process is to be repeated for every different architecture even though the application task may be the same. Consequently, this has a major impact on the ever increasing cost of software development for multiprocessor systems. A remedy for this problem would be to design portable parallel programs which can be mapped efficiently onto any computer system. In this dissertation, we present a portable programming tool called Cluster-M. The three components of Cluster-M are the Specification Module, the Representation Module, and the Mapping Module. In the Specification Module, for a given problem, a machine-independent program is generated and represented in the form of a clustered task graph called Spec graph. Similarly, in the Representation Module, for a given architecture or heterogeneous suite of computers, a clustered system graph called Rep graph is generated. The Mapping Module is responsible for efficient mapping of Spec graphs onto Rep graphs. As part of this module, we present the first algorithm which produces a near-optimal mapping of an arbitrary non-uniform machine-independent task graph with M modules, onto an arbitrary non-uniform task-independent system graph having N processors, in 0(M P) time, where P = max(M, N). Our experimental results indicate that Cluster-M produces better or similar mapping results compared to other leading techniques which work only for restricted task or system graphs

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization