Multi-server System Research Articles

Application of Mass Service Theory to Economic Systems Optimization Problems—A Review

An interdisciplinary approach to management allows for the integration of knowledge and tools of different fields of science into a unified methodology in order to improve the efficiency of resource management of different kinds of systems. In the conditions of global transformations, it is economic systems that have been significantly affected by external destabilizing factors. This determines the focus of attention on the need to develop tools for the modeling and optimization of economic systems, both in terms of organizational structure and in the context of resource management. The purpose of this review study is to identify the current gaps (shortcomings) in the scientific literature devoted to the issues of the modeling and optimization of economic systems using the tools of mass service theory. This article presents a critical analysis of approaches for the formulation of provisions on mass service systems in the context of resource management. On the one hand, modern works are characterized by the inclusion of an extensive number of random factors that determine the performance and efficiency of economic systems: the probability of delays and interruptions in mobile networks; the integration of order, inventory, and production management processes; the cost estimation of multi-server system operation; and randomness factors, customer activity, and resource constraints, among others. On the other hand, controversial points are identified. The analytical study carried out allows us to state that the prevailing majority of mass service models applied in relation to economic systems and resource supply optimization are devoted to Markov chain modeling. In terms of the chronology of the problems studied, there is a marked transition from modeling simple systems to complex mass service networks. In addition, we conclude that the complex architecture of modern economic systems opens up a wide research field for finding a methodology for assessing the dependence of the enterprise performance on the effect of optimization provided by using the provisions of mass service theory. This statement can be the basis for future research.

Optimal Scheduling in Multiserver Queues

Scheduling theory is a key tool for reducing latency (i.e. response time) in queueing systems. Scheduling, i.e. choosing the order in which to serve jobs, can reduce response time by an order of magnitude with no additional resources. Scheduling theory is well-developed in single-server systems, where one job is processed at a time. However, little is known about scheduling in multiserver systems, where many jobs are processed at once. Results are especially limited in stochastic multiserver scheduling theory. Today's datacenters have thousands of servers, and scheduling theory is unable to analyze such systems.

Joint Task Offloading and Resource Allocation in Multi-UAV Multi-Server Systems: An Attention-based Deep Reinforcement Learning Approach

Joint Task Offloading and Resource Allocation in Multi-UAV Multi-Server Systems: An Attention-based Deep Reinforcement Learning Approach

If You Love Your Agents, Set Them Free: Task Discretion in Online Workplaces

In manufacturing and service operations, flexibility is beneficial for matching supply with demand, but it comes at a cost. In modern digital workplaces, agents have different skills associated with corresponding and variable task preferences. Some are inclined to give up part of their payment to avoid unfavorable matches, and the platform manager, in turn, gains extra freedom in allocating tasks by possibly charging servers for favorable assignments. Innovative marketplaces facilitate task discretion and seek novel and beneficial implementations in the platform design. This naturally leads to the problem of exploring and optimizing the task allocation process. We introduce an innovative mechanism for task assignment in the workplace and compare it to the traditional mechanism in which task routing is solely the platform’s decision. To improve the welfare of all users, agents are allowed some task discretion in exchange for a fee. In a multiserver system, a server may request flexibility and, considering the other agents’ behavior, choose the costly to the platform option, which may or may not be beneficial for the platform when the agents’ and the platform’s preferences are misaligned. We model different working environments and server preferences via different distributions and study how the agents’ preferences, task costs, and flexibility fee affect the equilibrium assignment. In every case, through pricing and offering flexibility, the platform can do at least as well as a scheme with no flexibility. An important conclusion is that pricing task discretion can often improve the agents’ welfare and the labor platform’s profit. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

Correlated Combinatorial Bandits for Online Resource Allocation

We study a sequential resource allocation problem where, at each round, the decision-maker needs to allocate its limited budget among different available entities. In doing so, the decision-maker obtains the reward for each entity in that round. The goal of the decision-maker is to maximize the expected cumulative reward or equivalently minimize cumulative regret over a total of T rounds. Sequential resource allocation can be modeled as a combinatorial bandit by viewing the allocation of a budget to an entity as a base arm. In the context of resource allocation, the rewards received under different budget allocations are likely to be correlated. We propose a novel correlated combinatorial bandit framework that explicitly models such correlations. We develop a novel Correlated-UCB algorithm for online resource allocation, which yields significantly reduced regret relative to correlation-agnostic algorithms. In certain cases, our proposed algorithm even achieves bounded regret, which is an order-wise reduction in the regret relative to the correlation-agnostic approach which incurs logarithmic regret under all scenarios. We validate these performance gains through experiments on several applications such as online power allocation across wireless channels, job scheduling in multi-server systems and online access point assignment.

Mechanism for Cloud Computing Enviroment's Reliable Au-thenticated Token Handling

Cloud computing (CC) has matured in terms of dependability and effectiveness, which has led to the migration of many applications to the cloud. Three-factor Multilateral Authenticity and Knowledge Acceptance (MAKA) mechanisms for multi-server systems are gaining a lot of interest for their convenience and security. Although there are many extant three-factor MAKA protocols, none of them give a rigorous secure guarantee, making them vulnerable to numerous assaults on other procedures in the chain. And most three-factor MAKA mechanisms lack adaptive cancellation mechanisms, preventing malevolent users from being quickly removed. We present a three-factor MAKA protocol with a proven adaptive reversible evidence in the arbitrary source to overcome these issues. This method uses Schnorr identities to enable vibrant administration by the client. In multi-server setups, our procedure is capable of meeting a wide range of requirements. For intelligent systems with low processing power, the suggested method is a viable option. The entire computation code demonstrates the system's viability.

Problem issues of identification management of digital objects of multiservice systems

The paper considers the issues of access to the ecosystem of services, obtaining and providing services, which can be both internal and external in relation to the information system in which the digital object is located. Today, the management of relationships between digital objects and services is carried out not only due to the functionality provided by the information system, but also, in particular, due to the management of the identification of digital objects and services. As a digital object, not only the object that wants to receive or provide services, but also the service itself is considered. Therefore, identity management is considered in relation to two interacting objects. The analysis of digital object identification management was carried out based on the following circumstances. Identification of the same digital objects can be expressed in different sign and meaning systems. Therefore, it is necessary to update and maintain many identifiers within one ecosystem of services and be able to manage them. Identifiers can be divided into "stupid" (limited), which cannot perform anything except the function of linking a digital object to a strictly limited set of operations within the information system, regardless of the conditions in which it is located, and "smart", which can provide an opportunity to respond to circumstances and the possibility of implementing a flexible scenario selection that can be updated according to the situation. "Smart" identifiers, unlike "stupid" ones, have properties that allow them to respond to circumstances, and therefore it is necessary to be able to manage such properties of identifiers. The principles of a new architecture for the design of computer systems are proposed, which allow flexible management of digital objects, their properties, identification of digital objects, properties of identifiers depending on the circumstances of the time, place and situation in which the digital objects are located. The new architecture makes it possible to integrate new entities into information systems without significant modernizations associated with changes in source and object code, information schemes of systems, modernization of data structures, which lead to the need for mandatory restructuring of database tables and database table management procedures data.

The M/M/k with Deterministic Setup Times

Capacity management, whether it involves servers in a data center, or human staff in a call center, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand, one would like to turn off servers when not in use (or send home staff that are idle) to save on resources. On the other hand, one wants to avoid the considerable setup time required to turn an ''off'' server back ''on.'' This paper aims to understand the delay component of this tradeoff, namely, what is the effect of setup time on average delay in a multi-server system? Surprisingly little is known about the effect of setup times on delay. While there has been some work on studying the M/M/k with Exponentially-distributed setup times, these works provide only iterative methods for computing mean delay, giving little insight as to how delay is affected by k , by load, and by the setup time. Furthermore, setup time in practice is much better modeled by a Deterministic random variable, and, as this paper shows, the scaling effect of a Deterministic setup time is nothing like that of an Exponentially-distributed setup time. This paper provides the first analysis of the M/M/k with Deterministic setup times. We prove a lower bound on the effect of setup on delay, where our bound is highly accurate for the common case where the setup time is much higher than the job service time. Our result is a relatively simple algebraic formula which provides insights on how delay scales with the input parameters. Our proof uses a combination of renewal theory, martingale arguments and novel probabilistic arguments, providing strong intuition on the transient behavior of a system that turns servers on and off.

Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.

Genetic Algorithm for Cross-Layer-Based Energy Hole Minimization in Wireless Sensor Networks

Wireless sensor networks encompass wireless connectivity, low energy consumption protocols, and many algorithms. The sensor node battery is marked as a striking problem in such resource-constrained networks. Once the sensor nodes are mounted in an area, the batteries present in nodes are difficult to repair or replace. Many algorithms and protocols are implemented to progressively minimize the usage of energy in nodes. The sensor in charge of scheduling packets is significantly essential to reduce the processing overhead, energy consumption. Maximum-largest weighted delay first (M-LWDF) and packet dependent (PD) scheduling algorithms have been investigated in the multiuser multiservice systems. Efficiency and performance are better especially for multiuser multiservice to nonloss transmission during resource lag. Proposed work of cross-layer approach using genetic algorithm improves energy quality and efficiency by 24.7% compared to the existing methods and finds useful applications are low.

On Comparison of Multiserver Systems with Multicomponent Mixture Distributions

In this paper, we introduce and study the relations between parameters of the m-component mixture distributions which imply the stochastic and failure rate comparisons. Then we apply the failure rate and stochastic ordering techniques to construct the upper and lower bounds for the steady-state performance indexes in a multiserver queueing system with multicomponent exponential-Pareto mixture service time distribution. The uniform distance between multicomponent mixture distribution and its parent distribution is discussed. The obtained theoretical results are then illustrated by a few numerical examples based on the regenerative simulation multiserver queueing systems with mixture service time distributions.

Workload management and server speed setting for cost‐performance ratio optimization

AbstractThe cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Cluster system load balancing model with consideration of hardware characteristics of server hardware

Objectives. To upgrade and complement the existing load balancing model in multi-server systems, taking into account the hardware characteristics of the server equipment, as well as the most loaded components of the server equipment in the video conferencing service cluster in educational processes and distance education.Methods. The existing mathematical model of load balancing as a mass exchange system is considered, when significant changes are introduced: penalties for equipment downtime and penalties for waiting in a queue will depend on the load on the server hardware components in the cluster architecture of video conferencing service.Results. Formulas are given for calculating the total performance of a cluster of n servers with the maximum and minimum load of server hardware components in a videoconferencing system cluster.Conclusion. A modeling complex has been developed to test the mathematical model on a system of up to n < 10 servers in a cluster of a videoconferencing system. Based on the results of calculations of the modeling complex, it was concluded that it is necessary to upgrate the existing algorithm for balancing the load on the selected BigBlueButton video conferencing service.

WCFS: a new framework for analyzing multiserver systems

Multiserver queueing systems are found at the core of a wide variety of practical systems. Many important multiserver models have a previously-unexplained similarity: identical mean response time behavior is empirically observed in the heavy traffic limit. We explain this similarity for the first time. We do so by introducing the work-conserving finite-skip (WCFS) framework, which encompasses a broad class of important models. This class includes the heterogeneous M/G/k, the Limited Processor Sharing policy for the M/G/1, the Threshold Parallelism model and the Multiserver-Job model under a novel scheduling algorithm. We prove that for all WCFS models, scaled mean response time \(E[T](1-\rho )\) converges to the same value, \(E[S^2]/(2E[S])\), in the heavy-traffic limit, which is also the heavy traffic limit for the M/G/1/FCFS. Moreover, we prove additively tight bounds on mean response time for the WCFS class, which hold for all load \(\rho \). For each of the four models mentioned above, our bounds are the first known bounds on mean response time.

Priority queueing system with many types of requests and restricted processor sharing

A priority queueing model with many types of requests and restricted processor sharing is considered. A novel discipline of requests admission and service is proposed. This discipline assumes restriction of the bandwidth (capacity) of the server and the number of requests that can receive service in the system at the same time. This discipline is some kind of realistic hybrid of the traditional discipline of service in a multi-server system and the discipline of the limited processor sharing. The requests of the highest priority can push out from the service the low priority requests. Therefore, the important problem is fitting of the number of requests that can receive service at the same time to the bandwidth of the server. This problem is solved via construction and analysis of a multi-dimensional Markov chain describing operation of the system under any fixed set of the system parameters.

Profit Maximization in a Federated Cloud by Optimal Workload Management and Server Speed Setting

In an environment with multiple heterogeneous multiservers across multiple clouds and data centers, optimal workload management and server speed setting makes strong impact on the aggregated performance, cost, and profit of a cloud service provider. Such a situation provides us a more challenging opportunity to address and discuss profit maximization of a service provider in a wider scale and to generate more significant influence. In this paper, we investigate profit maximization by optimal workload management and server speed setting for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center. The heterogeneous multiservers have different sizes, speeds, power consumption models, workload, performance, costs, and profit. To conduct rigorous study, each multiserver system is modeled by an M/M/m queueing system, such that the profit of a multiserver system can be characterized analytically. We address two problems, i.e., workload management without server speed setting and workload management with server speed setting. Both problems are formulated as multi-variable optimization problems. We develop numerical algorithms to solve these problems. We also provide numerical data for the purpose of illustration. To the best of the author's knowledge, this is the first paper which analytically discusses profit maximization for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center using queueing systems.

Design of multi-service systems with facilities functioning as open Jackson queueing networks: application to online shopping stores

Design of multi-service systems with facilities functioning as open Jackson queueing networks: application to online shopping stores

Customer Adaptive Resource Provisioning for Long-Term Cloud Profit Maximization under Constrained Budget

As an efficient commercial information technology, cloud computing has attracted more and more users and enterprises to use it. Faced with such a large number and variety of customers, it is necessary for cloud providers (CPs) with limited budget to provide satisfactory customized pricing services, profitable customer and system investments, and flexible system resource provisioning strategies to improve both customer experience and long-term profit. Existing profit optimization research rarely considers customer diversity and dynamics, which may have a negative impact on long-term profit growth due to poor management of customer relations. In this article, we implement customer relationship management by considering both customer diversity and dynamics, and propose a customer adaptive resource provisioning scheme to maximize long-term profit under constrained budget. We consider four customer types (i.e., loyal, old, new, and lost) that can transition to each other during the customer's lifetime of interaction with the CP. The CP builds multiple cloud service sub-platforms, each of which contains multiple multiserver systems and serves the same type of customers. For the cloud service platform, we first analyze single multiserver system using an analytical method to obtain its optimal profit, invested funding, and system configuration. In particular, for systems serving new and lost customers, we develop a novel customer lifetime value (CLV)-based customer investment scheme that selects valuable customers for investment under limited marketing budget. Based on the above analysis, we then present a customer retention rate (CRR)-driven three-stage heuristic scheme that prioritizes investment in multiserver systems with endangered customers under limited infrastructure budget for reducing customer churn and promoting long-term profit growth. We conduct extensive simulation experiments to validate the effectiveness of our method. Simulation results show that compared with the benchmark algorithms, our method can improve the long-term profit and CRR by up to 3.4x and 7.8x, respectively.

Work-Conserving Disciplines Are Asymptotic Optimal in Completion Time Minimization

In this paper, we prove that in a stable multi-server system, where different machines are allowed to have different speeds, all work-conserving disciplines are asymptotic optimal for minimizing total completion time, if job size and interarrival time distributions have finite moments. As a byproduct of our analysis, we obtain tight upper bound on the competitive ratios of work-conserving disciplines on minimizing the metric of flow time.

A Multiserver Configuration and Request Distribution Framework for Profit Maximization in a Three-Tier Cloud Service Architecture

The rapid development of cloud computing has generated numerous cloud service providers. In a three-tier cloud service architecture, maximizing the cloud service profit is not only a goal pursued by cloud service providers but also a potent force that drives the continuous development of cloud computing technology. The traditional methods of maximizing cloud service profits are either limited to cloud resource management or service request distribution. Few work contributes to maximizing the profit of cloud service providers while considering both resource management and request distribution. Besides, the heterogeneity of servers is also an important concern when configuring the multiserver system. Therefore, this paper proposes a multiserver configuration and request distribution framework oriented to a three-tier cloud service architecture for maximizing the profit of cloud service providers. Compared with the benchmark method, extensive numerical simulation experiments show that our proposed framework can not only maximize the profit of cloud service providers, but also has a well-time efficiency.