Virtualization-based autonomic resource management for multi-tier Web applications in shared data center

As large data centers emerge, which host multiple Web applications, it is critical to isolate different application environments for security reasons and to provision shared resources effectively and efficiently to meet different service quality targets at minimum operational cost. To address this problem, we developed a novel architecture of resource management framework for multi-tier applications based on virtualization mechanisms. Key techniques presented in this paper include (1) establishment of the analytic performance model which employs probabilistic analysis and overload management to deal with non-equilibrium states; (2) a general formulation of the resource management problem which can be solved by incorporating both deterministic and stochastic optimizing algorithms; (3) deployment of virtual servers to partition resource at a much finer level; and (4) investigation of the impact of the failure rate to examine the effect of application isolation. Simulation experiments comparing three resource allocation schemes demonstrate the advantage of our dynamic approach in providing differentiated service qualities, preserving QoS levels in failure scenarios and also improving the overall performance while reducing the resource usage cost.     

Usage-based dynamic pricing of Web services for optimizing resource allocation

Web services technology is becoming an important technological trend in Web application development and integration. Based on open standards, such as SOAP, WSDL, and UDDI, Web services allow Web-based applications to communicate with each other through standardized XML messaging and to form loosely coupled distributed systems. Although the open feature of Web services benefits service providers in servicing consumers, the unlimited computing resources access of Web services to network bandwidth, storage throughput, and CPU time may lead to overexploitation of the resources when applications based on the Web services technology are widely accepted. Therefore, it is critical to optimize the operation of Web services, subject to the QoS requirements of service requests, to assure the total benefits of the service providers and the service consumers. This paper proposes a usage-based dynamic pricing approach to optimizing resource allocation of Web services in the principle of economics, and reports on a pilot implementation demonstrating the technical feasibility of the proposed approach.

Optimal resource allocation for time-reservation systems

This paper studies the optimal resource allocation in time-reservation systems. Customers arrive at a service facility and receive service in two steps; in the first step information is gathered from the customer, which is then sent to a pool of computing resources, and in the second step the information is processed after which the customer leaves the system. A central decision maker has to decide when to reserve computing power from the pool of resources, such that the customer does not have to wait for the start of the second service step and that the processing capacity is not wasted due to the customer still being serviced at the first step. The decision maker simultaneously has to decide on how many processors to allocate for the second processing step such that reservation and holding costs are minimized. Since an exact analysis of the system is difficult, we decompose the system into two parts which are solved sequentially leading to nearly optimal solutions. We show via dynamic programming that the near-optimal number of processors follows a step function with as an extreme policy the bang-bang control. Moreover, we provide new fundamental insights in the dependence of the near-optimal policy on the distribution of the information gathering times. Numerical experiments demonstrate that the near-optimal policy closely matches the performance of the optimal policy of the original problem.     

Reinforcement learning based resource allocation in business process management

Efficient resource allocation is a complex and dynamic task in business process management. Although a wide variety of mechanisms are emerging to support resource allocation in business process execution, these approaches do not consider performance optimization. This paper introduces a mechanism in which the resource allocation optimization problem is modeled as Markov decision processes and solved using reinforcement learning. The proposed mechanism observes its environment to learn appropriate policies which optimize resource allocation in business process execution. The experimental results indicate that the proposed approach outperforms well known heuristic or hand-coded strategies, and may improve the current state of business process management.     

Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers

Autonomous management of a multi-tier Internet service involves two critical and challenging tasks, one understanding its dynamic behaviors when subjected to dynamic workloads and second adaptive management of its resources to achieve performance guarantees. We propose a statistical machine learning based approach to achieve session slowdown guarantees of a multi-tier Internet service. Session slowdown is the relative ratio of a session’s total queueing delay to its total processing time. It is a compelling performance metric of session-based online transactions because it directly measures user-perceived relative performance and it is independent of the session length. However, there is no analytical model for session slowdown on multi-tier servers. We first conduct training to learn the statistical regression models that quantitatively capture an Internet service’s dynamic behaviors as relationships between various service parameters. Then, we propose a dynamic resource provisioning approach that utilizes the learned regression models to efficiently achieve session slowdown guarantee under dynamic workloads. The approach is based on the combination of offline training and online monitoring of the Internet service behavior. Simulations using the industry standard TPC-W benchmark demonstrate the effectiveness and efficiency of the regression based resource provisioning approach for session slowdown oriented performance guarantee of a multi-tier e-commerce application.     

基于效用函数的OFDM混合业务资源调度算法*

主要研究了QoS和BE两种混合业务场景下OFDM无线网络下行链路的资源调度问题,提出了一个基于效用函数的跨层资源调度模型,其能够自适应地对两种业务进行资源的联合优化分配。该模型被抽象为一个非线性整数规划问题,优化目标是系统总效用最大化,同时满足同信道干扰（CCI）约束以及QoS业务的质量要求。将该非线性整数规划问题转换为连续松弛凸规划问题进行求解,并结合最优松弛解,提出了一种简单的动态子载波分配算法,即MMU(mix-max-utility) 算法。仿真结果验证了该调度算法能使系统较好地支持混合业务,系统     

Dynamic and fair resource allocation in a distributed ring-based WDM-PON architectures

The overall objective of this paper is to propose and devise a dynamic and fair resource allocation technique in a ring-based WDM-PON architecture with truly shared LAN capabilities among end-users. A new distributed control plane is developed for this architecture that enables intercommunication among the ONUs, as well as signaling and scheduling procedures that operate in a distributed manner. To cope with the unpredictable users’ behavior and bandwidth demands, which shift both in time and place in such a networking environment, supported by the devised control plane, a distributed and efficient network resource allocation and sharing strategy is developed allowing for efficient dynamic allocation and sharing of overall downstream, upstream, and LAN network resources by adaptively adjusting them to the offered load. Performance results concerning link throughput and delay are presented, validating that the proposed methodology can meet the capacity requirements of the dynamic and highly fluctuant traffic pattern of the emerging multimedia applications and services. Specifically, the simulation results clearly show the effectiveness of the proposed shared wavelength strategy as the downstream/upstream network throughput is almost equal to the ideal theoretical throughput.     

Optimal resource allocation response to a smallpox outbreak

Infectious disease outbreaks, caused by nature or bioterrorism, are unfortunately very real threats to the general population. Planning an effective response to an infectious disease outbreak requires a coordinated effort in multiple locations to best allocate the limited resources. This decision problem is further complicated by the non-linear nature of disease propagation and the fact that outbreaks can jump urban, even national, boundaries. In this work we present a multi-city resource allocation model to distribute a limited amount of vaccine in order to minimize the total number of fatalities due to a smallpox outbreak.     

Stability analysis of network-based cooperative resource allocation strategies

Resource allocation involves deciding how to divide a resource of limited availability among multiple demands in a way that optimizes current objectives. In this brief paper we focus on one type of distributed resource allocation problem where via an imperfect communication network multiple processors can share the load presented by multiple task types. We introduce asynchronous “cooperative” resource allocation strategies, and show that they lead to a bounded cumulative demand.     

Joint admission control and resource allocation in virtualized servers

In service oriented architectures, Quality of Service (QoS) is a key issue. Service requestors evaluate QoS at run time to address their service invocation to the most suitable provider. Thus, QoS has a direct impact on the providers’ revenues. However, QoS requirements are difficult to satisfy because of the high variability of Internet workloads.     

Policy based resource allocation in IaaS cloud

In present scenario, most of the Infrastructure as a Service (IaaS) clouds use simple resource allocation policies like immediate and best effort. Immediate allocation policy allocates the resources if available, otherwise the request is rejected. Best-effort policy also allocates the requested resources if available otherwise the request is placed in a FIFO queue. It is not possible for a cloud provider to satisfy all the requests due to finite resources at a time. Haizea is a resource lease manager that tries to address these issues by introducing complex resource allocation policies. Haizea uses resource leases as resource allocation abstraction and implements these leases by allocating Virtual Machines (VMs). Haizea supports four kinds of resource allocation policies: immediate, best effort, advanced reservation and deadline sensitive. This work provides a better way to support deadline sensitive leases in Haizea while minimizing the total number of leases rejected by it. Proposed dynamic planning based scheduling algorithm is implemented in Haizea that can admit new leases and prepare the schedule whenever a new lease can be accommodated. Experiments results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.     

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.     

基于MDP支持弹性服务质量的接入控制

在网络服务系统中,满足业务请求的服务质量需求是系统要解决的主要问题之一。接入控制方法和资源分配策略常用来保证业务的服务质量要求。本文基于Markov决策过程(MDP)对视频点播(VOD)系统进行建模,同时考虑了弹性服务质量这一机制。弹性服务质量可以用一个QoS的需求范围来体现。策略梯度算法常用来解决MDP问题,它能够以比较好的速度收敛到最优解。通过算法实例对本文的接入控制方法进行性能分析,发现所采用的方法相对于一般的完全接入策略具有较优的性能。     

Optimization-based resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. The aim of this paper is to provide a starting point for researchers interested in applying optimization techniques in the resource allocation problem for current communication networks. To achieve that we, first, describe the fundamental optimization theory tools necessary to design optimal resource allocation algorithms. Then, we describe the Network Utility Maximization (NUM) framework, a framework that has already found numerous applications in network optimization, along with some recent advancements of the initial NUM framework. Finally, we summarize some of our recent work in the area and discuss some of the remaining research challenges towards the development of a complete optimization-based resource allocation protocol.     

基于马氏决策过程的概率离散事件系统最优控制

使用马氏决策过程研究了概率离散事件系统的最优控制问题.首先,通过引入费用函数、目标函数以及最优函数的定义,建立了可以确定最优监控器的最优方程.之后,又通过此最优方程获得了给定语言的极大可控、∈-包含闭语言.最后给出了获得最优费用与最优监控器的算法.     

Energy-efficient resource allocation in future wireless networks by sequential fractional programming

This overview paper discusses the framework of sequential fractional programming for energy efficiency maximization in future 5G networks. One of the main features of future systems will be the presence of severe multi-user interference and the need of improved energy efficiency compared to present systems. However, present approaches to energy efficiency maximization, which are based on the theory of fractional programming, result in an exponential complexity in interference-limited networks. In this context, the work shows how to extend available fractional programming approaches to obtain radio resource allocations enjoying strong optimality properties, while at the same time requiring an affordable complexity to be computed. The resulting framework is termed sequential fractional programming, and several examples of its applications to leading 5G candidate technologies are discussed in detail.     

Utility max–min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max–min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max–min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.     

Dynamic vehicle allocation control for automated material handling system in semiconductor manufacturing

The current study examines the dynamic vehicle allocation problems of the automated material handling system (AMHS) in semiconductor manufacturing. With the uncertainty involved in wafer lot movement, dynamically allocating vehicles to each intrabay is very difficult. The cycle time and overall tool productivity of the wafer lots are affected when a vehicle takes too long to arrive. In the current study, a Markov decision model is developed to study the vehicle allocation control problem in the AMHS. The objective is to minimize the sum of the expected long-run average transport job waiting cost. An interesting exhaustive structure in the optimal vehicle allocation control is found in accordance with the Markov decision model. Based on this exhaustive structure, an efficient algorithm is then developed to solve the vehicle allocation control problem numerically. The performance of the proposed method is verified by a simulation study. Compared with other methods, the proposed method can significantly reduce the waiting cost of wafer lots for AMHS vehicle transportation.     

A survey on resource allocation techniques in OFDM(A) networks

In this contribution, we present a survey on the radio resource allocation techniques in orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. This problem goes back to 1960s and that is related to properly and efficiently allocate the radio resources, namely subcarriers and power. We start by overviewing the main open issues in OFDM. Then, we describe the problem formulation in OFDMA, and we review the existing solutions to allocate the radio resources. The goal is to discuss the fundamental concepts and relevant features of different radio resource management criteria, including water-filling, max–min fairness, proportional fairness, cross-layer optimization, utility maximization, and game theory, also including a toy example with two terminals to compare the performance of the different schemes. We conclude the survey with a review of the state-of-the-art in resource allocation for next-generation wireless networks, including multicellular systems, cognitive radio, and relay-assisted communications, and we summarize advantages and common problems of the existing solutions available in the literature. The distinguishing feature of this contribution is a tutorial-style introduction to the fundamental problems in this area of research, intended for beginners on this topic.     

A systematic approach for resource allocation in software projects

The completion of reliable software products within their expected time frame represents a major problem for companies that develop software applications. Today, the software industry continues to struggle with delivering products in a timely manner. A major cause for delays is the training time required for engineers and other personnel to acquire the necessary skills to complete software tasks. Therefore, it is important to develop systematic personnel assignment processes that consider complete skill sets of candidates to provide solutions that reduce training time. This paper presents a novel methodology to assign resources to tasks when optimum skill sets are not available. The methodology takes into account existing capabilities of candidates, required levels of expertise, and priorities of required skills for the task. A sample case is used to show the model capabilities, and the results are compared with the current resource assignment approach.