Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of ...

In the digital age of exponential data proliferation and growing computing demands, efficient resource management within data centers is crucial. A key challenge is the provisioning of Virtual Machines (VMs), which significantly impacts ...

Modern scientific discoveries rely on an insatiable demand for computational resources. To meet this ever-growing computing demand, the datacenters have been established, which are complex controlled environments that host thousands of computing ...

• The thermal hazard (anomaly) prediction framework is essential for the efficient utilization of datacenters.
• Developing a statistical tool for automatically labeling thermal anomalies by studying reported thermal hazards.
• An AI-...

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential ...

With the evolution of networking technology, RDMA (Remote Direct Memory Access) has become an increasingly critical component in data centers. Its zero-copy and kernel-bypass capabilities significantly boost network transmission speeds and reduce ...

The increasing large-scale workload trace collected by datacenter monitors has provided great potential for workload prediction. However, resource usage pattern changes of workloads often exist, which are usually caused by user demand changes, ...

• We pioneer using shapelets for measuring long-term workload resource usage patterns.
• We propose a novel multi-view contrast graph-evolution learning algorithm.
• We design a GNN-based TCN encoder to realize the long-term workload ...

Datacenters are the primary driving force behind the energy shift to renewable sources. This shift limits the environmental impact of non-renewable sources and decarbonizes datacenters. However, renewable energy generation fluctuates with time and ...

Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters’ increasing energy consumption and ...

With the rapid development of cloud applications, the workload pattern of datacenters presents the characteristics of mixed long and short flows and frequent micro-burst traffic, which puts forward new requirements for network transmission ...

Reactive congestion control (RCC) protocols have undergone decades of evolution, where senders first send data packets and then back off when congestion occurs. Recently, there has been a surge of interest in proactive congestion control (PCC) that ...

The overwhelming growth of cloud computing has introduced new challenges for the cloud infrastructure. Many cloud service providers (CSPs) have been adopting renewable energy (RE) sources to increase profitability and reduce carbon emissions. ...

Traditional datacenter analysis is based on high-level, coarse-grained metrics. This obscures our vision of datacenter behavior, as we do not observe the full picture nor subtleties that might make up these high-level, coarse metrics. ...

• Proposes a method for the analysis of datacenter operations using rich datasets.

In recent years, data centers (DCs) have evolved a lot, and this change is related to the advent of cloud computing, e-commerce, services aimed at social networks, and big data. Such architectures demand high availability, reliability, and ...

Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and ...

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle ...

In Priority Flow Control (PFC) enabled datacenter networks (DCNs), PFC is inevitably triggered due to bursty traffic even with end-to-end congestion control. Load balancing as a complementary mechanism to transport protocols can make rerouting ...

Data-parallel computing applications (DPCAs) (e.g., MapReduce, web search, etc.) are driving the need of scalable, low-latency, high-speed, and energy-efficient datacenters, because a DPCA consists of a series of heavy-computation stages within a ...

• We propose a novel graph convolutional neural network for learning on directed graphs.

The growing interest in graph deep learning has led to a surge of research focusing on learning various characteristics of graph-structured data. Directed graphs have generally been treated as incidental to definitions on the more ...

The rapid growth in virtualization solutions has driven the widespread adoption of cloud computing paradigms among various industries and applications. This has led to a growing need for XaaS solutions and equipment to enable teleworking. To meet ...

Live migration of virtual machines proves to be inexorable in providing load balancing among physical devices and allowing scalability and flexibility in resource allocation. The existing approaches exhibit different policies, distinct performance ...$\mathrm{}$$\mathrm{}$