arXiv subjects

Shahram Ghandeharizadeh

Publications and source records attributed to Shahram Ghandeharizadeh.

21 records · Page 2Linked to original sources

arXiv2021

Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks

Unmanned Aerial Vehicles (UAVs) have moved beyond a platform for hobbyists to enable environmental monitoring, journalism, film industry, search and rescue, package delivery, and entertainment. This paper describes 3D displays using swarms of flying light specks, FLSs. An FLS is a small (hundreds of micrometers in size) UAV with one or more light sources to generate different colors and textures with adjustable brightness. A synchronized swarm of FLSs renders an illumination in a pre-specified 3D volume, an FLS display. An FLS display provides true depth, enabling a user to perceive a scene more completely by analyzing its illumination from different angles. An FLS display may either be non-immersive or immersive. Both will support 3D acoustics. Non-immersive FLS displays may be the size of a 1980's computer monitor, enabling a surgical team to observe and control micro robots performing heart surgery inside a patient's body. Immersive FLS displays may be the size of a room, enabling users to interact with objects, e.g., a rock, a teapot. An object with behavior will be constructed using FLS-matters. FLS-matter will enable a user to touch and manipulate an object, e.g., a user may pick up a teapot or throw a rock. An immersive and interactive FLS display will approximate Star Trek's Holodeck. A successful realization of the research ideas presented in this paper will provide fundamental insights into implementing a Holodeck using swarms of FLSs. A Holodeck will transform the future of human communication and perception, and how we interact with information and data. It will revolutionize the future of how we work, learn, play and entertain, receive medical care, and socialize.

cs.HC↗

arXiv2021

Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store

The cloud infrastructure motivates disaggregation of monolithic data stores into components that are assembled together based on an application's workload. This study investigates disaggregation of an LSM-tree key-value store into components that communicate using RDMA. These components separate storage from processing, enabling processing components to share storage bandwidth and space. The processing components scatter blocks of a file (SSTable) across an arbitrary number of storage components and balance load across them using power-of-d. They construct ranges dynamically at runtime to parallelize compaction and enhance performance. Each component has configuration knobs that control its scalability. The resulting component-based system, Nova-LSM, is elastic. It outperforms its monolithic counterparts, both LevelDB and RocksDB, by several orders of magnitude with workloads that exhibit a skewed pattern of access to data.

cs.DB↗

arXiv2016

The Subset Assignment Problem for Data Placement in Caches

We introduce the subset assignment problem in which items of varying sizes are placed in a set of bins with limited capacity. Items can be replicated and placed in any subset of the bins. Each (item, subset) pair has an associated cost. Not assigning an item to any of the bins is not free in general and can potentially be the most expensive option. The goal is to minimize the total cost of assigning items to subsets without exceeding the bin capacities. This problem is motivated by the design of caching systems composed of banks of memory with varying cost/performance specifications. The ability to replicate a data item in more than one memory bank can benefit the overall performance of the system with a faster recovery time in the event of a memory failure. For this setting, the number $n$ of data objects (items) is very large and the number $d$ of memory banks (bins) is a small constant (on the order of $3$ or $4$). Therefore, the goal is to determine an optimal assignment in time that minimizes dependence on $n$. The integral version of this problem is NP-hard since it is a generalization of the knapsack problem. We focus on an efficient solution to the LP relaxation as the number of fractionally assigned items will be at most $d$. If the data objects are small with respect to the size of the memory banks, the effect of excluding the fractionally assigned data items from the cache will be small. We give an algorithm that solves the LP relaxation and runs in time $O({3^d \choose d+1} \text{poly}(d) n \log(n) \log(nC) \log(Z))$, where $Z$ is the maximum item size and $C$ the maximum storage cost.

cs.DS↗