Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEdge service resource allocation strategy based on intelligent prediction
241
0
0.0
(
0
)
Added by
Xin Du
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Artificial intelligence is one of the important technologies for industrial applications, but it requires a lot of computing resources and sensor data to support it. With the development of edge computing and the Internet of Things, artificial intelligence are playing an increasingly important role in the field of edge services. Therefore, how to make intelligent algorithms provide better services and the development of the Internet of Things has become an increasingly important topic. This paper focuses on the application of edge service distribution strategy, and proposes an edge service distribution strategy based on intelligent prediction, which reduces the bandwidth consumption of edge service providers and minimizes the cost of edge service providers. In addition, this article uses the real data provided by the Wangsu Technology Company and an improved long and short term memory prediction method to dynamically change the bandwidth, and achieves better optimization of resources allocation comparing with actual industrial applications.The simulation results show that our intelligent prediction can achieve good results, and the mechanism can achieve higher resource utilization.
rate research
Read More
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.
P2P clusters like the Grid and PlanetLab enable in principle the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. The key unsolved problem is resource allocation. Existing solutions are not economically efficient and require high latency to acquire resources. We designed and implemented Tycoon, a market based distributed resource allocation system based on an Auction Share scheduling algorithm. Preliminary results show that Tycoon achieves low latency and high fairness while providing incentives for truth-telling on the part of strategic users.
We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications. This study demonstrates that synchronization groups, where each node selects a different channel, are non-uniformly distributed in phase space such that every distance between groups is larger than the area of influence. New type of self-organized spatiotemporal patterns can be formed for resource allocation according to channel rewards.
Distributed clusters like the Grid and PlanetLab enable the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. One major challenge is allocating resources in an economically efficient and low-latency way. A common solution is proportional share, where users each get resources in proportion to their pre-defined weight. However, this does not allow users to differentiate the value of their jobs. This leads to economic inefficiency. In contrast, systems that require reservations impose a high latency (typically minutes to hours) to acquire resources. We present Tycoon, a market based distributed resource allocation system based on proportional share. The key advantages of Tycoon are that it allows users to differentiate the value of their jobs, its resource acquisition latency is limited only by communication delays, and it imposes no manual bidding overhead on users. We present experimental results using a prototype implementation of our design.
In Wolke et al. [1] we compare the efficiency of different resource allocation strategies experimentally. We focused on dynamic environments where virtual machines need to be allocated and deallocated to servers over time. In this companion paper, we describe the simulation framework and how to run simulations to replicate experiments or run new experiments within the framework.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
