Related papers: A Poly-Log Approximation for Transaction Schedulin…
We consider a parallel system of $m$ identical machines prone to unpredictable crashes and restarts, trying to cope with the continuous arrival of tasks to be executed. Tasks have different computational requirements (i.e., processing time…
Cloud-based computing infrastructure provides an efficient means to support real-time processing workloads, e.g., virtualized base station processing, and collaborative video conferencing. This paper addresses resource allocation for a…
Every robotic network cloud system can be seen as a graph with nodes as hardware with independent computational processing powers and edges as data transmissions between nodes. When assigning a task to a node we may change several values…
Scheduling applications on wide-area distributed systems is useful for obtaining quick and reliable results in an efficient manner. Optimized scheduling algorithms are fundamentally important in order to achieve optimized resources…
Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications requires methods that handle the resource diversity and network structure of Fog devices while maximizing the service…
In blockchain sharding, $n$ processing nodes are divided into $s$ shards, and each shard processes transactions in parallel. A key challenge in such a system is to ensure system stability for any ``tractable'' pattern of generated…
In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed,…
Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute…
Recently, the concept of fog computing which aims at providing time-sensitive data services has become popular. In this model, computation is performed at the edge of the network instead of sending vast amounts of data to the cloud. Thus,…
Applications in data-parallel computing typically consist of multiple stages. In each stage, a set of intermediate parallel data flows (Coflow) is produced and transferred between servers to enable starting of next stage. While there has…
As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as…
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…
Datacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of…
Co-flows model a modern scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. In co-flow scheduling, there are $m$ input ports and $m$ output ports. Each co-flow $j \in J$ can be…
The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks…
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many…
Fog computing is seen as a promising approach to perform distributed, low-latency computation for supporting Internet of Things applications. However, due to the unpredictable arrival of available neighboring fog nodes, the dynamic…
Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can…
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this…
The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed…