Abstract: A distributed computing system manages execution of jobs and their associated tasks. A broker manages assignment of computing tasks from clients to available computing resources. Clients and available computing resources contact the broker by polling. To prevent “ringing,” the broker specifies wait times for the polling entities, and randomizes the wait times in a range around a desired target latency. That is, a pseudo-random number generator is used to select values within a range of the target value, to avoid the situation in which deterministic patterns in the polling and response times result in highly synchronized message traffic, which might otherwise overwhelm the broker and/or the communication network.

Abstract: An adaptive system for dynamically provisioning a shared computing infrastructure among a plurality of software applications and a plurality of types of software application servers providing run-time environments for the software applications. The system includes computing engines assigned to execute instances of the software applications, clients accessing the computing engines to request and receive services from the software applications, and a broker device that dynamically allocates engines domains for executing the software applications. The broker device includes an optimization module for allocating the computing engines to the domains, and a configuration manager for configuring the engines. The configuration manager reconfigures a computing engine by halting a current instance of a first software application, and by loading and starting an instance of a second software application.

Abstract: A shared computing infrastructure includes a plurality of computing engines, applications servers, and computing domains. A broker component executes a method for dynamically allocating the computing engines among the computing domains. The allocation method begins with the step of determining an expected number of computing engines to be allocated to each of the computing domains as a function of a predetermined service policy and a predicted demand for the domain While fewer than the expected number of computing engines has been allocated to each domain, the computing domains are sequentially selecting as a function of predetermined domain priorities. Unallocated computing engines are identified, and the unallocated computing engines are allocated to each selected computing domain according to predetermined selection rules for the domain. During an allocation improvement step, allocations among the computing domains are further adjusted to maximize a fitness statistic computed for the allocations.

Abstract: A distributed computing system manages execution of jobs and their associated tasks. A broker manages assignment of computing tasks from clients to available computing resources. Clients and available computing resources contact the broker by polling. To prevent “ringing,” the broker specifies wait times for the polling entities, and randomizes the wait times in a range around a desired target latency. That is, a pseudo-random number generator is used to select values within a range of the target value, to avoid the situation in which deterministic patterns in the polling and response times result in highly synchronized message traffic, which might otherwise overwhelm the broker and/or the communication network.

Abstract: A shared computing infrastructure includes a plurality of computing engines, applications servers, and computing domains. A broker component executes a method for dynamically allocating the computing engines among the computing domains. The allocation method begins with the step of determining an expected number of computing engines to be allocated to each of the computing domains as a function of a predetermined service policy and a predicted demand for the domain While fewer than the expected number of computing engines has been allocated to each domain, the computing domains are sequentially selecting as a function of predetermined domain priorities. Unallocated computing engines are identified, and the unallocated computing engines are allocated to each selected computing domain according to predetermined selection rules for the domain. During an allocation improvement step, allocations among the computing domains are further adjusted to maximize a fitness statistic computed for the allocations.

Abstract: An adaptive system for dynamically provisioning a shared computing infrastructure among a plurality of software applications and a plurality of types of applications servers providing run-time environments for the software applications. The system includes computing engines assigned to execute instances of the software applications, clients accessing the computing engines to request and receive services from the software applications, and a broker device that dynamically allocates engines domains for executing the software applications. The broker device includes an optimization module for allocating the computing engines to the domains, and a configuration manager for configuring the engines. The configuration manager reconfigures a computing engine by halting a current instance of a first software application, and by loading and starting an instance of a second software application.

Abstract: A service-oriented framework allows client applications to services hosted on a distributed computing access computational grid. Services facilitate remote, parallel execution of code in a way that is scalable, fault-tolerant, dynamic and language-independent. Services can be written in a variety of languages, and do not need to be compiled or linked with vendor-supplied code. A client written in one language can invoke a Service written in another. A benefit of the invention over traditional approaches is that it virtualizes the Service. Rather than send a request directly to the remote machine hosting the Service, a client request is sent to a manager, which enqueues until an Engine is available. The first Engine to dequeue the request hosts the Service. This mechanism, in which a single virtual Service instance (the client-side object) is implemented by one or more physical instances (Engine processes), provides for fault tolerance and essentially unlimited scalability.

Abstract: A distributed computing system manages execution of jobs and their associated tasks. Multiple scheduling strategies respect job priority preferences. A graphical user interface allows viewing of job status information and on-the-fly modification of job priorities.

Abstract: The invention provides an off-the-shelf product solution to target the specific needs of commercial users with naturally parallel applications. A top-level, public API provides a simple “compute server” or “task farm” model that dramatically accelerates integration and deployment. A number of described and claimed adaptive scheduling and caching techniques provide for efficient resource and/or network utilization of intermittently-available and interruptible computing resource in distributed computing systems.

Abstract: A service-oriented framework allows client applications to access computational services hosted on a distributed computing grid. Services facilitate remote, parallel execution of code in a way that is scalable, fault-tolerant, dynamic and language-independent. Services can be written in a variety of languages, and do not need to be compiled or linked with vendor-supplied code. A client written in one language can invoke a Service written in another. A benefit of the invention over traditional approaches is that it virtualizes the Service. Rather than send a request directly to the remote machine hosting the Service, a client request is sent to a manager, which enqueues it until an Engine is available. The first Engine to dequeue the request hosts the Service. This mechanism, in which a single virtual Service instance (the client-side object) is implemented by one or more physical instances (Engine processes), provides for fault tolerance and essentially unlimited scalability.

Abstract: An Internet-based, secure, distributed task-brokering and parallel-processing method/system/article-of-manufacture advantageously leverages under-utilized network-based computing resources for bandwidth-intensive and/or computationally-intensive problems, and provides significant cost advantages over traditional coarse-grained parallel computing techniques.

Abstract: The invention provides an off-the-shelf product solution to target the specific needs of commercial users with naturally parallel applications. A top-level, public API provides a simple “compute server” or “task farm” model that dramatically accelerates integration and deployment. A number of described and claimed adaptive scheduling and caching techniques provide for efficient resource and/or network utilization of intermittently-available and interruptible computing resource in distributed computing systems.

Abstract: The invention provides an off-the-shelf product solution to target the specific needs of commercial users with naturally parallel applications. A top-level, public API provides a simple “compute server” or “task farm” model that dramatically accelerates integration and deployment. A Propagator API allows parallel applications that require inter-node communication to be seamlessly deployed in heterogeneous environments, including networks of interruptible PCs. Implementation of parallel applications using the Propagator API does not require that the environment provide a separate node (or processor) for each block of concurrently-executable code. Nor does the Propagator API require that the assignment between particular blocks of code and processing resources remain static during execution of the parallel application.

Abstract: Improved methods, apparatus and articles-of-manufacture for providing distributed computing in a peer-to-peer network of processing elements utilize redundancy to improve quality-of-service and/or mitigate processing delays attributable to failures or slowdowns of processing elements or network connections.