Recently, large-language models (LLMs) have demonstrated strong potential to solve database problems. However, LLMs still face two challenges in solving the index selection problem: (1) representing the workload in an LLM-friendly form and (2) finding the optimal index set. To solve these challenges, we propose LLM4IA, an LLM-based index selection method that can recommend indexes for any analytical workload directly on any database instance. LLM4IA proposes a concise description of natural language by extracting and sorting predicates and completely avoiding numerical input. LLM4IA adopts an iterative index selection process by repeatedly improving previous index candidates and summarizing effective candidates. Experiments on TPC-H and TPC-DS show that LLM4IA surpasses the near-optimal index advisor Extend by 5%-10%. Our demonstration highlights how LLM4IA recommends high-quality indexes for a new database instance without expensive retraining or fine-tuning.

In the current communications environment, there is rapid proliferation of different types of broadband wired and wireless access networks. It is conceivable for users to roam between these heterogeneous networks and access content and services in a seamless manner and at different times. From the viewpoint of a service provider, the availability of different networks creates the possibility to deliver content using the most cost-effective route. This is particularly important for most multimedia content, especially of the entertainment genre where there is flexibility of time of delivery. Because of the added flexibility on the consumption time, a route selection among the different available networks for content delivery becomes feasible. In this paper, we present a solution that uses a remote site downloading function to deliver requested content through a customer selected route. Key problems of remote site downloading are addressed, including location management, remote service invocations, access account management and security etc. An overall system architecture design is presented.

We study the problem of real time VBR video traffic smoothing. One fundamental difficulty in this problem is that at any time during transmission, the information of most future video frames is not available and thus it is unlikely that global optimization can be achieved in the smoothing process. In order to measure the effectiveness of real time video smoothing methods, we first propose a benchmark algorithm which achieves optimality on some of the smoothness parameters in the smoothing results. Based on this algorithm, we found that significant discrepancy exists between the results produced by some of the existing smoothing methods and the smoothness upper bounds. With this observation, we then focus on devising an algorithm which improves the smoothing results. Experimental results show that our algorithm makes noticeable improvements in some of the smoothness parameters compared to existing smoothing methods.

We consider the use of large buffers and feedback as a mechanism to maintain loosely coupled synchronization between a multimedia server and a client. The multimedia stream is modeled as a fluid flow through rate controlled valves and buffers with multiple thresholds. These thresholds are used to control the rates upstream. The quality of service for the multimedia connection is characterized in terms of the jitter in the received media stream due to buffer underflow and overflow. This quality of service is used to exercise rate and admission control in the presence of congestion. The feedback mechanism is, implemented in GRAMS (gopher-style real time ATM multimedia system), an adaptive multimedia client-server system. Experimental statistics are gathered for the purpose of traffic engineering. We employ a fluid flow and first passage time analysis to understand the traffic process through the pipelines and the buffers and to estimate the amount of signaling required by the feedback mechanism.

Gopher-style real-time ATM multimedia services (GRAMS) is a multimedia system designed for an ATM local area network with the server providing multimedia services to multiple users in real-time. The major goal of this system is to serve heterogeneous media requests so that every client served has satisfactory service while the network bandwidth and the host capacity are fully exploited. Therefore, a server that can determine the service rates and successfully multiplex media transmissions according to the quality-of-service (QoS) requirement of each request becomes the key component of this system. However, given the heterogeneity of QoS requirements from different types of media, it is usually very difficult to clearly specify and accurately measure the QoS for these media. Also, it is difficult to control the QoS in a distributed-multimedia environment. We propose in this paper a simple yet effective way to measure QoS in the GRAMS system. Based on the GRAMS system architecture, we use different kinds of starvation counters to measure system resource utilization and individual service quality. These counters are employed as major QoS parameters for the admission and rate-control algorithms in the GRAMS server that are under intensive evaluation and refinement. Experimental results are also presented, showing the feasibility of simultaneously providing tens of video and images transfer sessions using low-cost workstation technology. Bottlenecks for the video server are identified.>