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Network Functions Virtualization (NFV) is a new network paradigm that has been strongly promoted from both scientific community and telecom industry, where network functions (NFs) such as firewalls, load balancers, gateways among others, are virtualized, isolated from middleboxes and housed on one or more industry standard computing nodes. One of the main challenges for service providers when they...
One technique of solving energy consumption and performance issues in virtualized data centers consists in migrating the virtual machines between the physical hosts, in order to achieve either resource consolidation or load balancing. These migrations, however, may degrade the performance of the virtualized applications and of the servers and network involved in the migration process. A possible solution...
With the rapid development of WEB applications, the demand for dynamically adjusting computing resources based on the load variation is increasing. However, most of the traditional WEB systems have limited ability to respond to load changes. In order to solve the problem, software self-adaptation technology has been applied to the resource management of WEB systems. Many researchers have tried to...
Finding an appropriate resource to host the next application to be deployed in a Cloud environment can be a non-trivial task. To deliver the appropriate level of service, the functional requirements of the application must be met. Ideally, this process involves filtering the best resource from a number of possible candidates, whilst simultaneously satisfying multiple objectives. If timely responses...
Service chaining composed of several different virtual network functions (VNFs) constitutes a use case of Network Functions Virtualization (NFV). In a conventional method, the optimal allocation of VNFs and logical paths was resolved by using integer linear programming. However, because the approach did not consider that the required resources of each service differ, the residual resources of the...
Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloudcomputing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become...
As clouds move to the network edge to facilitate mobile applications, edge cloud providers are facing new challenges on resource allocation. As users may move and resource prices may vary arbitrarily, %and service delays are heterogeneous, resources in edge clouds must be allocated and adapted continuously in order to accommodate such dynamics. In this paper, we first formulate this problem with a...
Mobile edge computing (MEC) has risen as a promising technology to augment computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle...
Cloud service providers are trying to reduce their operating costs while offering their services with a higher quality via resorting to the concept of elasticity. However, the vast majority of related work focuses solely on guaranteeing the quality of service (QoS) of interactive applications such as Web services. Nevertheless, a broad range of applications have different QoS constraints that do not...
In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used as one of the most common techniques to achieve both energy-and resource-utilization efficiency. Live migrations of VMs are used for dynamic consolidation but due to dynamic resource demand variation of VMs may lead to frequent and non-optimal migrations. Assuming deterministic workload of the VMs may ensure...
Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the...
Cloud computing is the most popular computing model in the current information industry, and cloud is a complex system with a large number of servers and users, it has to schedule tasks frequently among the servers and manage its computing resource flexibly to meet the demand of the users. With the growing service demand and higher QoS requirement of the users, the performance of the system is facing...
Server workloads benefit from execution on many-core processors due to their massive request-level parallelism. A key characteristic of server workloads is the large instruction footprints. While a shared last-level cache (LLC) captures the footprints, it necessitates a low-latency network-on-chip (NOC) to minimize the core stall time on accesses serviced by the LLC. As strict quality-of-service requirements...
We propose a quality of service (QoS) aware resource allocation algorithm for virtual machines (VMs) in cloud servers. The proposed scheme consists of two parts: a service profiler that describes the relationship between QoS and resource utilization, and a resource controller that determines an appropriate VM resource configuration. The performance of the proposed VM resource allocation scheme is...
The NEPHELE datacenter network is divided into pods/clusters of racks and relies on hybrid electro-optical top-of rack switches that access an all-optical network consisting of WDM rings. To enable dynamic and efficient sharing of the optical resources and a collision-free network operation, the NEPHELE network is designed to operate in a slotted manner with a software-defined-network (SDN) based...
This paper presents an Elastic Cloud Resource Allocation scheme that allocates minimal cloud VM resources that are needed to satisfy a given Service Level Objective (SLO) response time for cloud based elastic applications. More importantly, the algorithm attempts to mitigate any response time violation that could arise during the provisioning of cloud VM instances. Our proposed scheme utilizes queueing...
RFID technology suffers from a recurring issue: the reader-to-reader collision. Numerous protocols have been proposed to attempt to reduce them, but, remaining reading errors still heavily impact the performances and fairness of dense RFID deployments. This paper introduces a new Distributed Efficient & Fair Anticollision for RFID (DEFAR) protocol. It reduces both monochannel and multichannel...
In this paper, we propose a Virtual Machine (VM) allocator for Cloud Computing Data Center (DC). We allocate a set of VMs on servers that are interconnected through a three-tier fat-tree network topology. VMs require four different resources: CPU, memory, disk, and bi-directional network bandwidth forcommunications directed to and coming from the external gateway. Our goal is not to overload computing...
Idle servers only utilize 10% to 20% of their potential. As such, configuring computer resource management has been considered to optimize servers' capabilities. In this research, a series of experiments were conducted using virtual machines (VMs) with different CPU and memory allocation techniques implemented in XenServer. The created virtual machines ran benchmarks and stress test applications with...
The Internet of Things (IoT) is an emerging technology that is considered by industry and academy as a global infrastructure for the information society in today's digital world. IoT is implemented now in several ecosystems to improve economic indicators and productivity of enterprises, and also to improve the quality of consumer's lives. Thus a development of new architectures and approaches for...
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