相关论文: Application-Agnostic Offloading of Packet Processi…

With the advent of the big data, graph are processed in an iterative manner, which incrementally described in the form of graph in big data applications. Most currently, graph processing methods treat the underlying map data as black boxes.…

Partitioning large machine learning models across distributed accelerator systems is a complex process, requiring a series of interdependent decisions that are further complicated by internal sharding ambiguities. Consequently, existing…

Application partitioning and code offloading are being researched extensively during the past few years. Several frameworks for code offloading have been proposed. However, fewer works attempted to address issues occurred with its…

In the context of fog computing, we consider a simple case when data centers are installed at the edge of the network and assume that if a request arrives at an overloaded data center, then it is forwarded to a neighboring data center with…

We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server.…

An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce…

Autoregressive decoding becomes bandwidth-limited at long contexts, as generating each token requires reading all $n_k$ key and value vectors from KV cache. We present Stochastic Additive No-mulT Attention (SANTA), a method that sparsifies…

In recent years, data-intensive applications have been increasingly deployed on cloud systems. Such applications utilize significant compute, memory, and I/O resources to process large volumes of data. Optimizing the performance and…

Serverless computing has transformed cloud application deployment by introducing a fine-grained, event-driven execution model that abstracts away infrastructure management. Its on-demand nature makes it especially appealing for…

Modern commodity computing systems are composed by a number of different heterogeneous processing units, each of which has its own unique performance and energy characteristics. However, the majority of current network packet processing…

Given the rapid rise in energy demand by data centers and computing systems in general, it is fundamental to incorporate energy considerations when designing (scheduling) algorithms. Machine learning can be a useful approach in practice by…

With the mass deployment of computing-intensive applications and delay-sensitive applications on end devices, only adequate computing resources can meet differentiated services' delay requirements. By offloading tasks to cloud servers or…

The multi-user linearly-separable distributed computing problem is considered here, in which $N$ servers help to compute the real-valued functions requested by $K$ users, where each function can be written as a linear combination of up to…

The Opportunistic Spectrum Access (OSA) model has been developed for the secondary users (SUs) to exploit the stochastic dynamics of licensed channels for file transfer in an opportunistic manner. Common approaches to design channel sensing…

The increasing volume of traffic (especially from IoT devices) is posing a challenge to the current anomaly detection systems. Existing systems are forced to take the support of the control plane for a more thorough and accurate detection…

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multi-access edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile…

We consider a task of serving requests that arrive in an online fashion in Software-Defined Networks (SDNs) with network function virtualization (NFV). Each request specifies an abstract routing and processing "plan" for a flow. Each…

Memory bandwidth is known to be a performance bottleneck for FPGA accelerators, especially when they deal with large multi-dimensional data-sets. A large body of work focuses on reducing of off-chip transfers, but few authors try to improve…

The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic…

We are living in the era of Big Data and witnessing the explosion of data. Given that the limitation of CPU and I/O in a single computer, the mainstream approach to scalability is to distribute computations among a large number of…