Bandwidth allocation and scheduling of networked control systems with exponential and quadratic approximations

This paper investigates bandwidth allocation and scheduling of networked control systems (NCSs) with nonlinear-programming techniques. The bandwidth utilization (BU) is defined in terms of sampling frequency. An exponential and a quadratic approximation are formulated to describe system performance versus the sampling frequencies. The optimal sampling frequencies are obtained by solving the approximations with Karush–Kuhn–Tucker (KKT) conditions. Experimental results verify the effectiveness of the proposed approximations and scheduling algorithms. The two approximations could find an optimal BU of an NCS with a given sequence of plants and maximize the total BU up to 98% of the total available bandwidth.     

Heavy traffic optimal resource allocation algorithms for cloud computing clusters

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with the MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.     

Beam search algorithms for the single machine total weighted tardiness scheduling problem with sequence-dependent setups

In this paper, we consider the single machine weighted tardiness scheduling problem with sequence-dependent setups. We present heuristic algorithms based on the beam search technique. These algorithms include classic beam search procedures, as well as the filtered and recovering variants. Previous beam search implementations use fixed beam and filter widths. We consider the usual fixed width algorithms, and develop new versions that use variable beam and filter widths.     

Fully polynomial time approximation scheme for the weighted flow-time minimization on a single machine with a fixed non-availability interval

In a recent paper [Theoretical Computer Science 363, 257–265], He, Zhong and Gu considered the non-resumable case of the scheduling problem with a fixed non-availability interval under the non-resumable scenario. They proposed a polynomial time approximation scheme (PTAS) to minimize the total completion time.In this paper, we propose a fully polynomial-time approximation scheme to minimize the total weighted completion time. The FPTAS has O(n²/ε²) time complexity, where n is the number of jobs and ε is the required error bound. The proposed FPTAS outperforms all the previous approximation algorithms designed for this problem and its running time is strongly polynomial.     

An efficient Nash-implementation mechanism for network resource allocation

We propose a mechanism for auctioning bundles of multiple divisible goods in a network where buyers want the same amount of bandwidth on each link in their route. Buyers can specify multiple routes (corresponding to a source-destination pair). The total flow can then be split among these multiple routes. We first propose a one-sided VCG-type mechanism. Players do not report a full valuation function but only a two-dimensional bid signal: the maximum quantity that they want and the per-unit price they are willing to pay. The proposed mechanism is a weak Nash implementation, i.e., it has a non-unique Nash equilibrium that implements the social-welfare maximizing allocation. We show the existence of an efficient Nash equilibrium in the corresponding auction game, though there may exist other Nash equilibria that are not efficient. We then generalize this to arbitrary bundles of various goods. Each buyer submits a bid separately for each good but their utility function is a general function of allocations of bundles of various divisible goods. We then present a double-sided auction mechanism for multiple divisible goods. We show that there exists a Nash equilibrium of this auction game which yields the efficient allocation with strong budget balance.     

New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle

Keeping in view the non-probabilistic nature of experiments, two new measures of weighted fuzzy entropy have been introduced and to check their authenticity, the essential properties of these measures have been studied. Under the fact that measures of entropy can be used for the study of optimization principles when certain partial information is available, we have applied the existing as well as the newly introduced weighted measures of fuzzy entropy to study the maximum entropy principle.     

Evolutionary algorithms for route selection and rate allocation in multirate multicast networks

In multirate multicasting, different users (receivers) in the same multicast group can receive service at different rates, depending on the user requirements and the network congestion level. Compared with unirate multicasting, this provides more flexibility to the users and allows more efficient usage of the network resources. In this paper, we simultaneously address the route selection and rate allocation problem in multirate multicast networks; that is, the problem of constructing multiple multicast trees and simultaneously allocating the rate of receivers for maximizing the sum of utilities over all receivers, subject to link capacity and delay constraints for high-bandwidth delay-sensitive applications in point-to-point communication networks. We propose a genetic algorithm for this problem and elaborate on many of the elements in order to improve solution quality and computational efficiency in applying the proposed methods to the problem. These include the genetic representation, evaluation function, genetic operators, and procedure. Additionally, a new method using an artificial intelligent search technique, called the coevolutionary algorithm, is proposed to achieve better solutions, and methods of selecting environmental individuals and evaluating fitness are developed. The results of extensive computational simulations show that the proposed algorithms provide high-quality solutions and outperform existing approach.     

Online algorithms for advance resource reservations

We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user- and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments.     

The symbolic algorithms for maximum flow in networks

The maximum flow problem is one of the classic combinatorial optimization problems with many applications, such as electrical powers, traffics, communications, computer networks and logistics. The problem is to find a flow of maximum value on a network from a source to a sink. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate the Boolean functions efficiently. OBDD-based symbolic algorithms appear to give improved results for large-scale combinatorial optimization problems by searching the nodes and edges implicitly. For the maximum flow problem in networks, we present the symbolic algebraic decision diagram (ADD) formulation and symbolic algorithms. The augmenting-path-based symbolic algorithm is the combination of the Gabow's scaling algorithm with the binary decision diagram (BDD)-based symbolic algorithm for the maximum flow in 0–1 networks. The Karzanov's algorithm is implemented implicitly, resulting in the preflow-based symbolic algorithm (PSA), in which the vertices on each layer of the layered networks are partitioned by the vertex's input preflow and the total capacity of its outgoing edges, and the edges to push or pull the preflow are selected in terms of the priority function. The improved PSA is developed by integrating the heuristic of Träff's algorithm and the assistant layered networks into the PSA. The symbolic algorithms do not require explicit enumeration of the nodes and edges, and therefore can handle large-scale networks.     

A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardinessobjective

In modern manufacturing systems, due date related performance is becoming increasingly important in maintaining a high service reputation. However, compared with the extensive research on makespan minimization, research on the total weighted tardiness objective is comparatively scarce, partly because this objective function is more difficult and complex to optimize. In this paper, we focus on the job shop scheduling problem with the objective of minimizing total weighted tardiness. First, we discuss the mathematical programming model and its duality when the processing orders for each machine are fixed. Then, a block-based neighborhood structure is defined and its important properties are shown. Finally, a simulated annealing algorithm is designed which directly utilizes the features of this neighborhood. According to the computational results, the new neighborhood considerably promotes the searching capability of simulated annealing and helps it converge to high-quality solutions.     

Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability

This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution ofa workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear pro- gramming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

Joint adaptive power allocation and interference suppression algorithms based on theMSER criterion for wireless sensor networks

In this study, a two-hop wireless sensor network with multiple relay nodes is considered where the amplify-and-forward （AF） scheme is employed. Two algorithms are presented to jointly consider interference suppression and power allocation （PA） based on the minimization of the symbol error rate （SER） criterion. A stochastic gradient （SG） algorithm is developed on the basis of the minimum-SER （MSER） criterion to jointly update the parameter vectors that allocate the power levels among the relay sensors subject to a total power constraint and the linear receiver. In addition, a conjugate gradient （CG） algorithm is developed on the basis of the SER criterion. A centralized algorithm is designed at the fusion center. Destination nodes transmit the quantized information of the PA vector to the relay nodes through a limited-feedback channel. The complexity and convergence analysis of the proposed algorithms are carried out. Simulation results show that the proposed two adaptive algorithms significantly outperform the other previously reported algorithms.     

Toward end-to-end fairness: a framework for the allocation of multiple prioritized resources in switches and routers

As flows of traffic traverse a network, they share with other flows a variety of resources such as links, buffers and router CPUs in their path. Fairness is an intuitively desirable property in the allocation of resources in a network shared among flows of traffic from different users. While fairness in bandwidth allocation over a shared link has been extensively studied, overall end-to-end fairness in the use of all the resources in the network is ultimately the desired goal. End-to-end fairness becomes especially critical when fair allocation algorithms are used as a component of the mechanisms used to provide end-to-end quality-of-service guarantees. This paper seeks to answer the question of what is fair when a set of traffic flows share multiple resources in the network with a shared order of preference for the opportunity to use these resources. We present the Principle of Fair Prioritized Resource Allocation or the FPRA principle, a powerful extension of any of the classic notions of fairness such as max–min fairness, proportional fairness and utility max–min fairness defined over a single resource. We illustrate this principle by applying it to a system model with a buffer and an output link shared among competing flows of traffic. To complete our illustration of the applicability of the FPRA principle, we propose a measure of fairness and evaluate representative buffer allocation algorithms based on this measure. Besides buffer allocation, the FPRA principle may also be used in other contexts in data communication networks and operating system design.     

“Pay bursts only once” does not hold for non-FIFO guaranteed rate nodes

We consider end-to-end delay bounds in a network of guaranteed rate (GR) nodes. We demonstrate that, contrary to what is generally believed, the existing end-to-end delay bounds apply only to GR nodes that are FIFO per flow. We show this by exhibiting a counter example. Then, we show that the proof of the existing bounds has a subtle, but important, dependency on the FIFO assumption, which was never noticed before. Finally, we give a tight delay bound that is valid in the non-FIFO case; it is noticeably higher that the existing one. In particular, the phenomenon known as “pay bursts only once” does not apply to non-FIFO nodes. These findings are important in the context of differentiated services. Indeed the existing bounds have been applied to cases where a flow (in the sense of the GR definition) is an aggregate of end-user microflows, and it is not generally true that a router is FIFO per aggregate; thus, the GR node model of a differentiated services router cannot always be assumed to be FIFO per flow.     

Static resource allocation for heterogeneous computing environments with tasks having dependencies,priorities, deadlines,and multiple versions

Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. One aspect of resource allocation in HC environments is matching tasks with machines and scheduling task execution on the assigned machines. We will refer to this matching and scheduling process as mapping. The problem of mapping these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete. Therefore, the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks have deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies are adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm, and a two phase greedy technique based on the concept of Min–min heuristics. Simulation studies compare the performance of these heuristics in several overloaded scenarios, i.e., not all tasks can be executed by their deadlines. The performance measure used is the sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique finds the best results, but the faster two phase greedy approach also performs very well.     

Optimal policy for profit maximising in an EOQ model under non-linear holding cost and stock-dependent demand rate

In this article, we integrate a non-linear holding cost with a stock-dependent demand rate in a maximising profit per unit time model, extending several inventory models studied by other authors. After giving the mathematical formulation of the inventory system, we prove the existence and uniqueness of the optimal policy. Relying on this result, we can obtain the optimal solution using different numerical algorithms. Moreover, we provide a necessary and sufficient condition to determine whether a system is profitable, and we establish a rule to check when a given order quantity is the optimal lot size of the inventory model. The results are illustrated through numerical examples and the sensitivity of the optimal solution with respect to changes in some values of the parameters is assessed.     

Design and analysis of a variable bit rate caching algorithm for continuous media data

In this paper, the problem of caching continuous media data in a (main) memory and disk caching system is addressed. Caching schemes can significantly reduce the load on the network as well as on the servers, also the retrieval of documents from the cache requires short response time. In interval-level caching algorithms, an interval of data between two adjacent streams is the basic caching entity. In this paper, we design a novel algorithm, referred to as variable bit rate caching (VBRC) algorithm, which belongs to the interval-level caching algorithms. The proposed VBRC algorithm can be used in the system for memory caching or disk caching. VBRC can handle variable retrieval bandwidth as well as constant retrieval bandwidth . In designing the VBRC algorithm, we propose the strategies of reducing the number of switching operation, which will probably cause discontinuity of retrieving data. Also, we propose a just-in-time scheme for resource allocation in our VBRC algorithm and show that the caching performance in comparison with the reservation scheme adopted in the resource-based caching (RBC) algorithm is significantly improved. Our simulation study compares the recent and most popular generalized interval caching, RBC, and VBRC, on several influencing factors such as cache space size, cache I/O bandwidth, request arrival rate, and percentage of requests for large documents, with respect to the byte hit ratio and the number of switching operations. The simulation result confirms our analysis.