Regulated enterprises often seek to extend their workloads into the cloud, but are impeded by integration concerns relating to security, governance and compliance. Further, enterprises running mission-critical applications, face throughput and latency challenges due to cloud integration overheads. We present Hybrid Cloud Connector to accelerate on-prem to cloud integration by handling non-functional aspects in lieu of the application, reducing complexity, and centralizing administration via a policy-driven control point.

This work tackles an inherent conflict between two important trends. The first is the integration of data compression capabilities into many storage systems supporting random I/O on the compressed data. The second is encrypting data at the host, before data is written to the storage, in order to address regulatory and enterprise requirements. This provides end-to-end protection for the data, but since the data arrives encrypted, it prevents the storage from compressing the data. Can compression savings be achieved together with host side encryption without changing the storage protocols or storage backend? In this paper we show that they can.

Extracting value from data stored in object stores,such as OpenStack Swift and Amazon S3, can be problematicin common scenarios where analytics frameworks and objectstores run in physically disaggregated clusters. One of the mainproblems is that analytics frameworks must ingest large amountsof data from the object store prior to the actual computation;this incurs a significant resources and performance overhead. Toovercome this problem, we present Scoop. Scoop enables analyticsframeworks to benefit from the computational resources of objectstores to optimize the execution of analytics jobs. Scoop achievesthis by enabling the addition of ETL-type actions to the dataupload path and by offloading querying functions to the objectstore through a rich and extensible active object storage layer. Asa proof-of-concept, Scoop enables Apache Spark SQL selectionsand projections to be executed close to the data in OpenStackSwift for accelerating analytics workloads of a smart energy gridcompany (GridPocket). Our experiments in a 63-machine clusterwith real IoT data and SQL queries from GridPocket show thatScoop exhibits query execution times up to 30x faster than thetraditional “ingest-then-compute” approach.

We present QoS experiments done in an advanced object store that supports the local execution of code on objects. We show how by giving different levels of resources to different objects, we are able to offer considerable improvement in the performance of the more-important objects, at the expense of the less-important objects. We created two tiers: Gold for premium objects, and Silver for standard objects. We present a workload on which we achieve a running time improvement of 33.5 percent for Gold objects. This is done at the expense of the performance given to Silver objects. The resource parameters we manipulate are the level of redundancy and CPU resources.

Reduction of disk drive power consumption is a challenging task, particularly since the most prevalent way of achieving it, powering down idle disks, has many undesirable side-effects. Some hard disk drives support acoustic modes, meaning they can be configured to reduce the acceleration and velocity of the disk head. This reduces instantaneous power consumption but sacrifices performance. As a result, input/output (I/O) operations run longer at reduced power. This is useful for power capping since it causes significant reduction in peak power consumption of the disks. We conducted experiments on several disk drives that support acoustic management. Most of these disk drives support only two modes - quiet and normal. We ran different I/O workloads, including SPC-1 to simulate a real-world online transaction processing workload. We found that the reduction in peak power can reach up to 23% when using quiet mode. We show that for some workloads this translates into a reduction of 12.5% in overall energy consumption. In other workloads we encountered the opposite phenomenon-an increase of more than 6% in the overall energy consumption.

This article presents an algebraic analysis of a mesh-compression technique called high-pass quantization [Sorkine et al. 2003]. In high-pass quantization, a rectangular matrix based on the mesh topological Laplacian is applied to the vectors of the Cartesian coordinates of a polygonal mesh. The resulting vectors, called δ-coordinates, are then quantized. The applied matrix is a function of the topology of the mesh and the indices of a small set of mesh vertices (anchors) but not of the location of the vertices. An approximation of the geometry can be reconstructed from the quantized δ-coordinates and the spatial locations of the anchors. In this article, we show how to algebraically bound the reconstruction error that this method generates. We show that the small singular value of the transformation matrix can be used to bound both the quantization error and the rounding error which is due to the use of floating-point arithmetic. Furthermore, we prove a bound on this singular value. The bound is a function of the topology of the mesh and of the selected anchors. We also propose a new anchor-selection algorithm, inspired by this bound. We show experimentally that the method is effective and that the computed upper bound on the error is not too pessimistic.