Abstract: Embodiments of the present invention relate to systems, methods, and computer-storage media for maintaining privacy while delivering advertisements based on encrypted user profile identifiers. In embodiments, a Public key Encryption with Keyword Search (PEKS) is used to generate a public key and a private key. In embodiments, a public key and a private key are used to encrypt user profile identifiers and generate trapdoors associated with defined profile identifiers, respectively. A portion of the encrypted user profile identifiers are compared to a portion of the trapdoors. If a match is present between at least one encrypted user profile identifier and an associated trapdoor, a delivery engine is provided with an identification of content to be delivered to the user. The provided description is then used to determine an advertisement to present to a user. The advertisement is then presented to the user.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that maintain privacy during user profiling are provided. A profiling service receives, from a first device, rules for profiling a user. The rules were encrypted using a private key. The profiling service also receives, from a second device, user data. The user data was encrypted using a public key communicated to the second device by the first device. The profiling service then matches the encrypted rules with the encrypted user data, and based on the matching, generates a profile for the user. In embodiments, such a user profile can be utilized to deliver personalized digital content to a user.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A job scheduler may schedule concurrent distributed jobs in a computer cluster by assigning tasks from the running jobs to compute nodes while balancing fairness with efficiency. Determining which tasks to assign to the compute nodes may be performed using a network flow graph. The weights on at least some of the edges of the graph encode data locality, and the capacities provide constraints that ensure fairness. A min-cost flow technique may be used to perform an assignment of the tasks represented by the network flow graph. Thus, online task scheduling with locality may be mapped onto a network flow graph, which in turn may be used to determine a scheduling assignment using min-cost flow techniques. The costs may encode data locality, fairness, and starvation-freedom.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that maintain privacy during user profiling are provided. A profiling service receives, from a first device, rules for profiling a user. The rules were encrypted using a private key. The profiling service also receives, from a second device, user data. The user data was encrypted using a public key communicated to the second device by the first device. The profiling service then matches the encrypted rules with the encrypted user data, and based on the matching, generates a profile for the user. In embodiments, such a user profile can be utilized to deliver personalized digital content to a user.

Abstract: Embodiments of the present invention relate to systems, methods, and computer-storage media for maintaining privacy while delivering advertisements based on encrypted user profile identifiers. In embodiments, a Public key Encryption with Keyword Search (PEKS) is used to generate a public key and a private key. In embodiments, a public key and a private key are used to encrypt user profile identifiers and generate trapdoors associated with defined profile identifiers, respectively. A portion of the encrypted user profile identifiers are compared to a portion of the trapdoors. If a match is present between at least one encrypted user profile identifier and an associated trapdoor, a delivery engine is provided with an identification of content to be delivered to the user. The provided description is then used to determine an advertisement to present to a user. The advertisement is then presented to the user.

Abstract: A job scheduler may schedule concurrent distributed jobs in a computer cluster by assigning tasks from the running jobs to compute nodes while balancing fairness with efficiency. Determining which tasks to assign to the compute nodes may be performed using a network flow graph. The weights on at least some of the edges of the graph encode data locality, and the capacities provide constraints that ensure fairness. A min-cost flow technique may be used to perform an assignment of the tasks represented by the network flow graph. Thus, online task scheduling with locality may be mapped onto a network flow graph, which in turn may be used to determine a scheduling assignment using min-cost flow techniques. The costs may encode data locality, fairness, and starvation-freedom.

Abstract: A virtual machine distribution system is described herein that uses a multiphase approach that provides a fast layout of virtual machines on physical computers followed by at least one verification phase that verifies that the layout is correct. During the fast layout phase, the system uses a dimension-aware vector bin-packing algorithm to determine an initial fit of virtual machines to physical hardware based on rescaled resource utilizations calculated against hardware models. During the verification phase, the system uses a virtualization model to check the recommended fit of virtual machine guests to physical hosts created during the fast layout phase to ensure that the distribution will not over-utilize any host given the overhead associated with virtualization. The system modifies the layout to eliminate any identified overutilization. Thus, the virtual machine distribution system provides the advantages of a fast, automated layout planning process with the robustness of slower, exhaustive processes.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A distributed data store employs replica placement techniques in which a number k hash functions are used to compute k potential locations for a data item. A number r of the k locations are chosen for storing replicas. These replica placement techniques provide a system designer with the freedom to choose r from k, are structured in that they are determined by a straightforward functional form, and are diffuse such that the replicas of the items on one server are scattered over many other servers. The resulting storage system exhibits excellent storage balance and request load balance in the presence of incremental system expansions, server failures, and load changes. Data items may be created, read, and updated or otherwise modified.