A Tag-Frame System of Resource Management for Proof Search in Linear-Logic Programming

To achieve end to end (E2E) quality management capability for different services (e.g. voice, video and data), an SOA-based E2E resource orchestration architecture based on hierarchical distributed model and virtualization mechanisms for individual networks is proposed. Possible implementations and operation mechanisms considering possible service scenarios are discussed. Prototype of resource management (RM) system is demonstrated for the first time to manage and control distributed GE-PON-based access network resources, RPR-based metro network resources and GMPLS-based core network resources. Proposed RM system consists of network resource managers (NRMs) virtualizing individual network resources and network resource broker (NRB) leading orchestration of NRMs with a single point of contact to a service control layer. Exposure of E2E network service to a service control layer as well as the exposure of virtualized network services to NRB are represented by a universal Web services interface (WSI). Using the RM system, the heterogeneous resources are orchestrated to provide appropriate bandwidth-managed E2E services following service demand of a network service client, and appropriate resources are applied to assumed voice, video and data flows.

Information-as-a-Service (InfaaS) is a key concept for collaborative disaster management applications. For developing a collaborative disaster management application based on InfaaS concept, the control in infrastructure-layer is required. We have been developing InfaaS-adaptive Disaster Management Application Platform (InfaaS-AP) to support the construction and operation of collaborative disaster management applications that satisfy InfaaS concept. InfaaS-AP leverages multiple existing Software-Defined technologies, each of which has a dedicated controller. All controllers are centrally managed by Resource Management System (RMS) in InfaaS-AP. Though availability is critical for InfaaS-AP, RMS in current InfaaS-AP is a single point of failure. It is due to the connectivity between RMS and the controllers. RMS with redundant configuration requires maintaining connectivity after fail-over. In addition, since each controller has its own redundancy mechanism, the behavior of the redundancy mechanism makes it difficult to maintain the connectivity. From such a situation, we propose the connectivity-aware redundancy control module. The proposed module not only handles the fail-over of RMS but also monitors and interrupts the behavior of the redundancy mechanism of each controller. In the evaluation, we conducted experiments in which one of the controllers or RMS was stopped and checked the behavior of the proposed module.

Probably the most significant result concerning cut-free sequent calculus proofs in linear logic is the completeness of focused proofs. This completeness theorem has a number of proof theoretic applications — e.g. in game semantics, Ludics, and proof search — and more computer science applications — e.g. logic programming, call-by-name/value evaluation. Andreoli proved this theorem for first-order linear logic 15 years ago. In the present paper, we give a new proof of the completeness of focused proofs in terms of proof transformation. The proof of this theorem is simple and modular: it is first proved for MALL and then is extended to full linear logic. Given its modular structure, we show how the proof can be extended to larger systems, such as logics with induction. Our analysis of focused proofs will employ a proof transformation method that leads us to study how focusing and cut elimination interact. A key component of our proof is the construction of a focalization graph which provides an abstraction over how focusing can be organized within a given cut-free proof. Using this graph abstraction allows us to provide a detailed study of atomic bias assignment in a way more refined that is given in Andreoli’s original proof. Permitting more flexible assignment of bias will allow this completeness theorem to help establish the completeness of a number of other automated deduction procedures. Focalization graphs can be used to justify the introduction of an inference rule for multifocus derivation: a rule that should help us better understand the relations between sequentiality and concurrency in linear logic.

Experimental applications of data from multispectral and other advanced sensors have demonstrated that remote sensing can make a valuable contribution to the monitoring and management of Canada's land resources. More frequent coverage and additional spectral bands on satellites planned for the mid-1980s and beyond will increase the opportunities for regular use of remotely sensed data. To effectively utilize these data in resource management, the remote sensing input must be matched with the resource management systems existing at that time. Thus, it is essential to anticipate the needs of resource management systems of the late 1980s and 1990s, to determine the appropriate role for remotely sensed data and to develop and implement a plan which will yield the remote sensing systems and methodologies necessary to meet the operational resource management requirements Previous studies of resource information requirements indicate that there will be a need for geocoded remotely sensed data, improved image analysis techniques and better information integration concepts for future resource management systems. To develop a plan for meeting the anticipated requirements, the flow from the recording of the remotely sensed data to the end use of the derived information is considered first. The timeliness and accuracy requirements of different users, the diverse data types and forms for individual applications, the analysis methods/decision models needed and the implications of these factors for the configuration(s) of remote sensing input into the future resource management systems are examined. From this analysis, areas requiring further work (research, development, demonstration, transfer) are identified, and a plan of action is suggested.

The resource management system is designed to support location-transparent access to resources and to improve performance in a distributed internetwork environment. In this environment, access to one of several machines that have a given resource is determined by the effective bandwidth and reliability of internetwork communications, the ability to interact with the machines offering the resources, and the load on those machines. Using these criteria, the resources management system enables applications to determine and obtain access to the “best” copy of a resource. Should the resource become unavailable from one machine, the resource management system can dynamically direct the applications to another machine known to offer the same resource. This paper discusses some of the problems that motivated the design, an implementation for a UNIX workstation environment, and future directions for the resource management system .

Recent development of logic programming languages based on linear logic suggests a successful direction to extend logic programming to be more expressive and more efficient. The treatment of formulasas-resources gives us not only powerful expressiveness, but also efficient access to a large set of data. However, in linear logic, whole resources are kept in one context, and there is no straight way to represent complex data structures as resources. For example, in order to represent an ordered list and time-dependent data, we need to put additional indices for each resource formula. This paper describes a logic programming language, called TLLP, based on intuitionistic temporal linear logic. This logic, an extension of linear logic with some features from temporal logics, allows the use of the modal operators '?'(next-time) and '?'(always) in addition to the operators used in intuitionistic linear logic. The intuitive meaning of modal operators is as follows: ? B means that B can be used exactly once at the next moment in time; ? B means that B can be used exactly once any time; !B means that B can be used arbitrarily many times (including 0 times) at any time. We first give a proof theoretic formulation of the logic of the TLLP language. We then present a series of resource management systems designed to implement not only interpreters but also compilers based on an extension of the standard WAM model.

The Complexity of Local Proof Search in Linear Logic: (Extended Abstract)

Efficient resource management for linear logic proof search

Artificial Intelligence (AI) approaches, such as the big data, mobile computing, and Internet of Things (IoT) have become more common in the computer science area in recent years. It is critical to properly manage resources in order to maintain service quality, service level agreements, and the system's overall availability. AI may be used to address the needs of cloud resource management, and this study examines and evaluates several approaches. Fog computing systems, intelligent cloud computing systems and edge-cloud systems are all types of AI-based cloud resource management systems that have been reviewed in this contribution. In this research, an intelligent resource management method that controls mobile resources by monitoring device states and projecting their future stability is proposed. We look at the possibility of using our suggested resource management system to a variety of cloud-based platforms.

This paper presents the design and implementation of an energy saving daemon for clusters called cherub. The design of the cherub daemon is modular and extensible. In the field of High Performance Computing (HPC) well known Resource Management Systems (RMSs) like Portable Batch System (PBS) [8]or its open source derivative, the TORQUE resource manager [11]are used to manage clusters and work queues. Thus, cherub is able to interact with different RMSs to make them energy aware. cherub is also suited for load-balancing clusters managed by dispatchers like Linux Virtual Server (LVS) [17] since the only requirement is a central approach for resource management.

Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management Systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.

In this thesis, we present our works on resource management in large scale systems, especially for enterprise cluster and storage systems. Large-scale cluster systems become quite popular among a community of users by offering a variety of resources. Such systems require complex resource management schemes for multi-objective optimizations and should be specific to different system requirements. In addition, burstiness has often been found in enterprise workloads, being a key factor in performance degradation. Therefore, it is an extremely challenging problem of managing heterogeneous resources (e.g., computing, networking and storage) for such a large scale system under bursty conditions while providing performance guarantee and cost efficiency.

Recently, linear Logic has been used to specify sequent calculus proof systems in such a way that the proof search in linear logic can yield proof search in the specified logic. Furthermore, the meta-theory of linear logic can be used to draw conclusions about the specified sequent calculus. For example, derivability of one proof system from another can be decided by a simple procedure that is implemented via bounded logic programming-style search. Also, simple and decidable conditions on the linear logic presentation of inference rules, called homogeneous and coherence, can be used to infer that the initial rules can be restricted to atoms and that cuts can be eliminated. In the present paper we introduce Llinda, a logical framework based on linear logic augmented with inference rules for definition (fixed points) and induction. In this way, the above properties can be proved entirely inside the framework. To further illustrate the power of Llinda, we extend the definition of coherence and provide a new, semi-automated proof of cut-elimination for Girard’s Logic of Unicity (LU).

Encryption as an abstract data-type: An extended abstract

Adaptive parallel computations-computations that can adapt to changes in resource availability and requirement-can effectively use networked machines because they dynamically expand as machines become available and dynamically acquire machines as needed. While most parallel programming systems provide the means to develop adaptive programs, they do not provide any functional interface to external resource management systems. Thus no existing resource management system has the capability to manage resources on commodity system software, arbitrating the demands of multiple adaptive computations written using diverse programming environments. This paper presents a set of novel mechanisms that facilitate dynamic allocation of resources to adaptive parallel computations. The mechanisms are built on low-level features common to many programming systems, and unique in their ability to transparently manage multiple adaptive parallel programs that were not developed to have their resources managed by external systems. We also describe the design and the implementation of the initial prototype of ResourceBroker, a resource management system built to validate these mechanisms.