Abstract: Methods and systems for implementing ranged operations in a distributed computing system are provided. A write operation is received from a client device, wherein the write operation is configured to associate a range key and range key value with a range of key-value (KV) pairs included in a sorted key space, wherein the range corresponds to a key span of the sorted key space defined by a start boundary key and an end boundary key. A range key and range key value is written for the range based on the write operation, wherein an iterator is configured to read a respective value of a KV pair of the range and/or the range key value based on a position of the iterator within the key span.

Abstract: Methods and systems for managing requests in a distributed computing system are provided. A plurality of requests are received by one or more of the plurality of computing nodes in the distributed computing system. A respective timestamp is assigned to each of the plurality of requests. A subset of the plurality of requests are assigned to an epoch, wherein the epoch corresponds to an interval of time comprising the respective timestamp of each request of the subset of the plurality of requests. A present time is compared to a closing time of the epoch. Based on the comparison, the subset of the plurality of requests are queued for execution in a waiting data structure by last-in, first-out (LIFO) ordering based on the respective timestamp of each request of the subset.

Abstract: Systems and methods for encoding block properties for data structures and filtering data structures using block properties are provided. A group of a plurality of key-value entries can be added to a data block of a data file. A block property indicative of attributes of the group of the plurality of key-value entries can be determined. The block property can be encoded in a block handle and added to an index block of the data file. A query directed to a plurality of data files can be received from a client device. A target block property included in the query can be compared to a block property corresponding to a data block of a data file of the plurality of data files. Based on the target block property intersecting with the block property of the data block, values corresponding to the data block can be sent to the client device.

Abstract: Systems and methods for controlling admission of transactional operations for execution are provided. An admission queue configured to control admission to an LSM tree can receive a plurality of work items corresponding to a plurality of tuples. The plurality of work items can be queued in the admission queue for execution based on the plurality of tuples. Indications of a capacity of the LSM tree and a size of each work item of the plurality of work items can be determined. A plurality of tokens configured to enable admission of the plurality of work items to the LSM tree can be generated based on the indications of the capacity of the LSM tree and the size of each work item. Based on the plurality of tokens, a work item of the plurality of work items can be admitted from the admission queue to the LSM tree.

Abstract: A system and method which integrates a greedy allocation for consumer admission control with a Lagrangian allocation to compute flow rates and which links the results of the two approaches to allow a tradeoff between consumer admission control and flow rates. The Lagrangian Rates, Greedy Populations (hereinafter, “LRGP”) solution is a scalable and efficient distributed solution for maximizing the total utility in an event-driven distributed infrastructure. The greedy population, consumer portion generates prices used in the LaGrangian rate flow approach. The method is iterative including a regular exchange of information for ongoing optimization, dynamically adjusting producer rates in response to changes to consumer service and dynamically adjusting the service to consumer populations in response to changes in the producer rates.

Abstract: A system and method which integrates a greedy allocation for consumer admission control with a Lagrangian allocation to compute flow rates and which links the results of the two approaches to allow a tradeoff between consumer admission control and flow rates. The Lagrangian Rates, Greedy Populations (hereinafter, “LRGP”) solution is a scalable and efficient distributed solution for maximizing the total utility in an event-driven distributed infrastructure. The greedy population, consumer portion generates prices used in the LaGrangian rate flow approach. The method is iterative including a regular exchange of information for ongoing optimization, dynamically adjusting producer rates in response to changes to consumer service and dynamically adjusting the service to consumer populations in response to changes in the producer rates.

Abstract: A system, method and computer program storage device providing event and/or state sharing support e.g., for building object-oriented interactive groupware in wide-area distributed environments (such as the Internet). For collaborative applications programmed using events, mechanisms are provided for sharing application-specific events. For example: an event based programming model allows applications to post an event and triggers corresponding ERUs (Event Reaction Unit) in reaction to a received event; preconditions for control activation of ERUs; and event consistency policy objects implement application specified event consistency model. Some policy examples are: a policy in which event order is not guaranteed, but all events are guaranteed to be sent to the ERUs eventually; and a policy that first triggers local ERUs and then posts the event to the server.

Abstract: A system, method and computer program storage device providing event and/or state sharing support e.g., for building object-oriented interactive groupware in wide-area distributed environments (such as the Internet). For collaborative applications programmed using events, mechanisms are provided for sharing application-specific events. For example: an event based programming model allows applications to post an event and triggers corresponding ERUs (Event Reaction Unit) in reaction to a received event; preconditions for control activation of ERUs; and event consistency policy objects implement application specified event consistency model. Some policy examples are: a policy in which event order is not guaranteed, but all events are guaranteed to be sent to the ERUs eventually; and a policy that first triggers local ERUs and then posts the event to the server.