Synergy : a comprehensive software distributed shared memory system

Considerable research and development has been invested in software Distributed Shared Memory (DSM). The primary focus of this work has traditionally been on high performance and consistency protocols. Unfortunately, clusters present a number of challenges for any DSM systems not solvable through consistency protocols alone. These challenges relate to the ability of DSM systems to adjust to load fluctuations, computers being added/removed from the cluster, to deal with faults, and the ability to use DSM objects larger than the available physical memory. This paper introduces the Synergy DSM System and its integration with the virtual memory, group communication and process migration services of the Genesis Cluster Operating System.

Toward a comprehensive software based DSM system

Software based Distributed Shared Memory (DSM) systems have been the focus of considerable research effort, primarily in improving performance and consistency protocols. Unfortunately, computer clusters present a number of challenges for any DSM systems that are not solvable through consistency protocols alone. These challenges relate to the ability of DSM systems to adjust to load fluctuations, computers being added/removed from the cluster, to deal with faults, and the ability to use DSM objects larger than the available physical memory. We present here a proposal for the Synergy Distributed Shared Memory System and its integration with the virtual memory, group communication and process migration services of the Genesis Cluster Operating System.

Spatial memory in a food storing corvid I. Near tall landmarks are primarily used

This work suggests how food storing corvids use spatial memory to relocate caches, and how they can do this after some landmarks surrounding caches have become hidden due to leaf fall, snow fall or plant growth. Experiments involved training European jays (Garrulus glandarius) to find buried food, the location of which was specified by an array of 12 landmarks. Tests were then performed with the array rotated, or with certain landmarks removed from the array. The.main findings were: (1) birds primarily remembered the position of the goal using the near tall landmarks (15-30 cm from the goal and 20 cm high); (2) birds obtained a sense of direction both from the landmark array and something external to the array; (3) birds did not use smell or marks in the surface of the ground to find the goal. Memory of near tall landmarks is likely to be functional for these birds since (a) nearer landmarks provide a more accurate fix, and (b) taller landmarks are less likely to be completely obscured by snow fall, leaf fall or intervening vegetation. The work also demonstrates the use of G.I.S. software for the analysis and representation of animal search patterns.

Joint optimization of rule placement and traffic engineering for QoS provisioning in software defined network

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

A state machine for detecting c/c++ memory faults

Memory faults are major forms of software bugs that severely threaten system availability and security in c/c++ program. Many tools and techniques are available to check memory faults, but few provide systematic full-scale research and quantitative analysis. Furthermore, most of them produce high noise ratio of warning messages that require many human hours to review and eliminate false-positive alarms. And thus, they cannot locate the root causes of memory faults precisely. This paper provides an innovative state machine to check memory faults, which has three main contributions. Firstly, five concise formulas describing memory faults are given to make the mechanism of the state machine simple and flexible. Secondly, the state machine has the ability to locate the cause roots of the memory faults. Finally, a case study applying to an embedded software, which is written in 50 thousand lines of c codes, shows it can provide useful data to evaluate the reliability and quality of software.

DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.