Dynamic Resource Management Scheme Research Articles

RESTRAIN: A dynamic and cost-efficient resource management scheme for addressing performance interference in NFV-based systems

Network Functions Virtualization (NFV) replaces the conventional middleboxes by their software counterparts known as Virtual Network Functions (VNFs) which run on general-purpose hardware platforms and promise several benefits like reduced cost, ease of deployment, flexibility, etc. However, NFV faces some critical challenges as VNFs running on the same physical hardware still have to compete for shared resources such as Last Level Cache (LLC) and different levels of Memory Bandwidth (MB) (between L2 cache & LLC and LLC & main memory), which might result in unpredictable and variable performance interference to the co-located VNFs deployed. Some recent works have explored mechanisms for allocating LLC dynamically using Cache Allocation Technology (CAT) but they did not look into MB contentions among the co-located VNFs. Dynamic allocation of both LLC and MB to the co-located VNFs remains unexplored. To address this, in this work, by leveraging Intel’s CAT and Memory Bandwidth Allocation (MBA) technologies, we profile different VNFs to determine their minimum LLC and MB resource requirements to achieve performance isolation for different input traffic rates. We then propose a dynamic, joint resource allocation scheme, RESTRAIN, that takes each VNF’s input traffic rate as an input and dynamically adjusts LLC ways and MB resources allocated among them to avoid performance interference and thereby improves the overall resource utilization of the underlying hardware system and the number of VNFs meeting their QoS guarantees. Experimental results on a prototype system show that the proposed RESTRAIN scheme guarantees performance isolation. Further, it improves performance by 30% over a static allocation mechanism and 17% over ResQ, a state-of-the-art scheme.

Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.

Hybrid gradient descent spider monkey optimization (HGDSMO) algorithm for efficient resource scheduling for big data processing in heterogenous environment

Big Data constructed based on the advancement of distributed computing and virtualization is considered as the current emerging trends in Data Analytics. It is used for supporting potential utilization of computing resources focusing on, on-demand services and resource scalability. In particular, resource scheduling is considered as the process of resource distribution through an effective decision making process with the objective of facilitating required tasks over time. The incorporation of heterogeneous computing resources by the Big Data consumers also permits the option of reducing energy usage and enhanced resource efficiency. Further, optimal scheduling of resources is considered as an NP hard problem due to the dynamic characteristics of the resources and fluctuating users’ demand. In this paper, a Hybrid Gradient Descent Spider Monkey Optimization (HGDSMO) algorithm is proposed to efficient resource scheduling by handling the issues and challenges in the Hadoop heterogenous environment. The proposed HGDSMO algorithm uses the Gradient Descentand foraging and social behavior of the spider monkey optimization algorithm involved in the objective of effective resource allocation. It is designed as the efficient task scheduling approach that balances the load of the cloud by allocating them to appropriate VMs depending on their requirements. It is also proposed as a dynamic resource management scheme for efficiently allocating the cloud resources for effective execution of clients’ tasks. The simulation results of the proposed HGDSMO algorithm confirmed to be potent in throughput, load balancing and makespan compared to the baseline hybrid meta-heuristic resource allocation algorithms used for investigation.

WITHDRAWN: A Dynamic Energy-efficient resource management scheme and performance optimization in cloud data centers

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Robust resource allocation for rates maximization using fuzzy estimation of dynamic channel states in OFDMA femtocell networks

One of the challenging issues in dynamic resource management scheme of wireless communications is to achieve the instantaneous channel gain for transmission pairs. When designing the resource allocation algorithms, the previous works usually rely on the statistical information of the channel distribution and the harsh assumption of uncertain channel, and they are with much conservatism for the dynamic channel gain. To overcome the limitations of the existing methods, the Fuzzy Logic System (FLS) is adopted in this paper to estimate the instantaneous channel gain for the dynamic communication environments. Based on the estimated channel gains, the power allocation scheme is proposed for the orthogonal frequency division multiple access (OFDMA) femtocell network. The proposed scheme attempts to maximize the total rate of the communication system and to guarantee the quality of service of each user. The original optimization problem is a nonconvex optimization problem which is difficult to solve. The successive convex approximation (SCA) is adopted to transform the nonconvex original problem into a tractable one. The distributed iterative algorithm is developed to integrate with the proposed scheme in real-time dynamic communication environments. The effectiveness of the proposed scheme is demonstrated through the simulations, and the results show that the sum rate and energy efficiency of users can be improved by the proposed scheme, compared to the existing schemes.

Dynamic Resource Management for LTE-Based Hybrid Access Femtocell Systems

Hybrid access femtocells for long term evolution (LTE)-based cellular networks provide a tradeoff between closed and open access femtocells whereby all subscribers are granted access albeit with priority given to closed access subscribers. Due to the need to accommodate both closed and open access subscribers, quality of service (QoS) provisioning for LTE-based hybrid access femtocells has become more challenging. This paper addresses this issue and proposes a new dynamic resource management scheme for such hybrid architectures. In particular, the proposed scheme first classifies and performs lexicographic admission control on the incoming traffic data flows using an optimal greedy algorithm. A suboptimal delay-bounded packet scheduling algorithm and a dual decomposition-based power allocation algorithm are developed to solve the non-convex maximization problem such that the weighted sum rate of each femtocell is maximized, subject to bounded packet delays and power constraints. Simulation results show that the proposed scheme can significantly outperform existing schemes in terms of QoS, throughput and fairness.

A virtualized resource management scheme for heterogeneous cellular networks

Because of random deployment patterns of femtocells, interference scenarios in a heterogeneous cellular network can be very complicated because of its changing network topology. Especially when each eNodeB occupies a fixed bandwidth, interference management becomes much more difficult. The benefit of dynamic management for local resource optimation is limited. Recently, resource virtualization has been proposed as a dynamic resource management scheme to optimize network performance. In fact, resource virtualization is viewed as a more flexible model, in which mobile network service providers can control physical resources in a global scope. This paper presents a joint resource virtualization and allocation scheme for its applications in heterogeneous macro-femto-cellular networks. The proposed scheme involves two major processes. First, it virtualizes physical resources as logical resources. Second, it carries out logical resource allocation optimization globally and aggregates logical and physical resources for resource allocation. The proposed scheme takes into account spectrum reuse and frequency domain interference jointly in order to achieve a high spectral efficiency and provide rate-on-demand services to all users. Simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Software-Defined Network Virtualization Platform for Enterprise Network Resource Management

Applications of a software-defined network (SDN) have increased. Notably an SDN has the benefits of programmability and automation for network control. It also realizes network virtualization. Based on the OpenFlow/FlowVisor network virtualization layer, this paper proposes a dynamic resource management scheme, called the EnterpriseVisor engine that manages the distribution of network resources among slices. The EnterpriseVisor engine first slices an enterprise’s network into virtual subnets, and then continues to monitor the resource utilization of each subnet. In the engine, dynamic resource scheduling of the subnet for each service uses the linear programming method. The engine then delivers the schedule as the resource allocation policy of the FlowVisor layer for data plane users. To demonstrate the feasibility of the proposed EnterpriseVisor engine, an OpenFlow testbed with four subnets is designed. Compared with the basic OpenFlow and OpenFlow/FlowVisor platforms, the EnterpriseVisor engine has higher network utilization, 25.7% and 13.4% higher, respectively, with only a minor sacrifice to control message latency.

Cross-Layer Rate Control and Resource Allocation in Spectrum-Sharing OFDMASmall Cell Networks with Delay Constraints

In this paper, we present a dynamic resource management scheme for delay-aware applications in two-tier small-cell networks (SCNs). We propose the scheme of joint rate control at the transport layer and resource allocation at the physical layer to manage the cross-tier interference. The joint rate control and resource allocation scheme is designed to maximize the time-averaged sum capacity of small cell users in the SCN subject to each small cell user's delay constraint and an interference constraint imposed by the macrocell. By using the Lyapunov optimization technique, we develop a delay-guaranteed capacity optimal algorithm (DCOA) to obtain the optimal rate control and resource allocation decisions. We show that without prior knowledge of the data arrivals and channel statistics, DCOA achieves a capacity of SCN that can arbitrarily approach the optimal capacity achieved by the algorithm with the complete knowledge of data arrivals and channel statistics. Simulations results confirm the theoretical analysis on the performance of DCOA and also show the adaptiveness of DCOA.

A Novel Dynamic Resilient Ensemble Physical Proximity Resource Management Scheme Adopting for Distributed Environments

Heterogeneous grid resources in hand by completely different organization spread throughout the internet or wide area networks. In existing Distributed Hash Table (DHT) is the organization used for that mapping key to the nodes of a network based on a standardized hashing performs. to meet high scalable and dynamism resilient, thence we have a tendency to propose a Grid resource management theme called ranked Cycloid Overlay design (HCO) that\'s data locality-preserving. In HCO, we have a tendency to establish program/data locality by cluster resources based on their physical proximity and practical matching with user application. We have a tendency to any develop dynamism-resilient resource management rule, cluster-token forwarding rule, and deadline-driven resource management algorithms square measure planned. On adopting these algorithms consumes less overhead, speedy, dynamism-resilient multi resource discovery. The well-organized resource cluster, reduces communications price, and enhances resource discovery success rate flexibly extant in large scale applications.

Service level agreement based energy-efficient resource management in cloud data centers

As cloud computing has become a popular computing paradigm, many companies have begun to build increasing numbers of energy hungry data centers for hosting cloud computing applications. Thus, energy consumption is increasingly becoming a critical issue in cloud data centers. In this paper, we propose a dynamic resource management scheme which takes advantage of both dynamic voltage/frequency scaling and server consolidation to achieve energy efficiency and desired service level agreements in cloud data centers. The novelty of the proposed scheme is to integrate timing analysis, queuing theory, integer programming, and control theory techniques. Our experimental results indicate that, compared to a statically provisioned data center that runs at the maximum processor speed without utilizing the sleep state, the proposed resource management scheme can achieve up to 50.3% energy savings while satisfying response-time-based service level agreements with rapidly changing dynamic workloads.

OFDMA 펨토셀 환경에서 전력 제어를 이용한 동적 하향링크 자원관리 방법

최근에 실내 커버리지 확장 및 광대역 데이터 서비스를 위한 가정용 기지국인 펨토셀에 대한 연구가 활발하다. 펨토셀이 배치될 때, 기존에 있던 네트워크 상황 속에서 다양한 기술적인 문제들을 발생 시킬 수 있다. 본 논문에서는 OFDMA 시스템에서 펨토 - 매크로셀 간의 간섭 완화에 대하여 연구하였고, 동적인 하향링크 자원 관리 방법을 제안하였다. 펨토셀의 전송 파워를 매크로셀로부터 오는 신호의 세기에 기반을 두어서 선택하는 방법과 펨토셀 에게 할당 할 부반송파를 동적으로 관리하는 방식을 제안하였다. 이러한 방법으로 펨토셀과 매크로셀 사용자와의 간섭을 줄일 수 있다. 제안하는 방법은 수용율을 비롯한 여러 평가 지표로 성능을 검증하였다. Femtocells as home base station for indoor coverage extension and wideband data service, have been studied with significant interests. When femtocell is deployed, the existing cell structural of changes causes various technical problems. In this paper, we investigate the femto-macro cell interference mitigation in OFDMA system. We propose dynamic downlink resource management scheme which adjust the transmitted power of femtocell according to the strength of received macrocell signal and allocates subcarrier to femtocells in a dynamic manner. In this way, the interference between the macrocell users and femtocells is reduced. The simulation results show that proposed scheme enhances both macrocell and femtocell throughputs.

Dynamic Resource Management in Clouds: A Probabilistic Approach

SUMMARY Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by “buzz/flash crowd effects” that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.

A dynamic radio resource management scheme for the IEEE 802.16 band‐adaptive modulation and coding mode with proportional rate constraints

ABSTRACTIn this paper, we consider the problem of downlink resource allocation in the IEEE 802.16 standard based on the orthogonal frequency‐division multiple access physical layer. The objective is to allocate the downlink frequency‐time slots and transmission power to different users such that the total throughput is maximized. The allocation is subject to constraints on the total transmission power and satisfying the quality of service requirements of different users expressed in terms of proportional rates. A three‐step heuristic scheme that incorporates a linear programming technique is proposed. The problem is modelled as an unbalanced transportation problem that is solved to obtain the users' slot assignment. The Vogel approximation method is used to efficiently solve the transportation problem with a slight degradation relative to the optimal solution. The Vogel approximation method gain is shown by replacing it in the three‐step heuristic by simple round‐robin or greedy slot assignment. In terms of total throughput and fairness, it is shown that the proposed scheme is a good compromise between the greedy‐oriented schemes, which aspire to maximize the total system throughput but sacrificing fairness, and a proportional rate constraints scheme introduced in the literature that achieved short‐term fairness but penalizes the total throughput. The study also shows that the proposed scheme consumes a fraction of the time needed by the proportional rate constraints scheme. Copyright © 2011 John Wiley & Sons, Ltd.

A Spectrum Decision Framework for Cognitive Radio Networks

Cognitive radio networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face to a unique challenge based on the fluctuating nature of heterogeneous spectrum bands as well as the diverse service requirements of various applications. In this paper, a spectrum decision framework is proposed to determine a set of spectrum bands by considering the application requirements as well as the dynamic nature of spectrum bands. To this end, first, each spectrum is characterized by jointly considering primary user activity and spectrum sensing operations. Based on this, a minimum variance-based spectrum decision is proposed for real-time applications, which minimizes the capacity variance of the decided spectrum bands subject to the capacity constraints. For best-effort applications, a maximum capacity-based spectrum decision is proposed where spectrum bands are decided to maximize the total network capacity. Moreover, a dynamic resource management scheme is developed to coordinate the spectrum decision adaptively dependent on the time-varying cognitive radio network capacity. Simulation results show that the proposed methods provide efficient bandwidth utilization while satisfying service requirements.

Throttling-Based Resource Management in High Performance Multithreaded Architectures

Up to now, the power problems which could be caused by the huge amount of hardware resources present in modern systems have not been a primary concern. More recently, however, power consumption has begun limiting the number of resources which can be safely integrated into a single package, lest the heat dissipation exceed physical limits (before actual package meltdown). At the same time, new architectural techniques such as simultaneous multithreading (SMT), whose goal it is to efficiently use the resources of a superscalar machine without introducing excessive additional control overhead, have appeared on the scene. In this paper, we present a new resource management scheme which enables an efficient low power mode in SMT architectures. The proposed scheme is based on a modified pipeline throttling technique which introduces a throttling point at the last stage of the processor pipeline in order to reduce power consumption. We demonstrate that resource utilization plays an important role in efficient power management and that our strategy can significantly improve performance in the power-saving mode. Since the proposed resource management scheme tests the processor condition cycle by cycle, we evaluate its performance by setting a target IPC as one sort of immediate power measure. Our analysis shows that an SMT processor with our dynamic resource management scheme can yield significantly higher overall performance

Dynamic resource management for QoS provisioning over next-generation IP-based wireless networks

A significant issue in current research pursuits is the formulation of the requirements and basic design options for the next-generation wireless network architecture. The next-generation of wireless systems will support a diverse set of access technologies and mobile devices, formulating a broad heterogeneous environment with increased requirements on network support operations. It is expected that the demanding breed of multimedia applications will even more considerably require Quality of Service support throughout the end-to-end path. This paper first provides a tutorial approach on next-generation wireless network architectures and more specifically on end-to-end QoS provision. We claim that dynamic resource management in the Core Network is a necessity due to the increased heterogeneity of the new environment. We subsequently present our proposal regarding a dynamic resource management scheme that is based on the concept of the Resource Pools. The Resource Pool concept is deeply analysed within the paper and simulation results prove its correctness and appropriateness.

Dynamic resource management considering the real behavior of aggregate traffic

This paper explains why the theory of effective bandwidth is in general not applicable to characterize realistic traffic sources. Moreover, we show that a static allocation of network resources (bandwidth and/or buffer space) based on large deviation theory can be highly inefficient when the real statistical behavior of traffic is taken into account. As an alternative, we propose a dynamic resource management scheme based on prediction techniques. As a specific example, we apply this scheme to a Differentiated Service (DiffServ) Internet environment, in which the goal, besides policing the incoming traffic, is to optimize the use of network resources, thus minimizing the probability of occurrence of violations of contract guarantees. The performance of the proposed scheme is evaluated via simulations and our results show the superior performance of the new algorithm in terms of buffer overflows, output link utilization, and jitter, as compared to currently used policing and shaping mechanisms.