Performance Evaluation最新文献

NFV (Network Functions Virtualization) is a technology to provide network services by applying virtualization. While the virtualization technology provides flexible architecture by itself, a specific environment where physical legacy equipment and virtualized machines coexist is also considered to meet a wide range of requirements from not only users but also service providers. Motivated by such hybrid systems, we propose queueing models with two types of service facilities: legacy servers and virtual machines. Key features to focus on are that while legacy servers are always on standby, virtual machines need setup time to be ready for service because they are shutdown to reduce the power consumption if no jobs are waiting. Keeping in mind delay-sensitive real-time services, we evaluate the performance of the queueing models, in particular, the delay and energy efficiency.

The demand of electricity at the Charging Stations (CSs) by Electric Vehicle (EV) users is tremendously increasing. However, EV users still face limited resources at the CSs, both in terms of the number of parking spaces equipped with a charging point, and in terms of available power. This paper deals with the choice of a CS among two CSs by the EV users in a competitive environment. The stochastic nature of arrivals and departures at the CSs is modeled by a queueing system. A queueing game is studied where the EV users are the players and choose the CS that gives the highest expected energy received. An approximation of the expected energy received at the CSs is theoretically provided and the quality of this approximation is numerically illustrated and analyzed through simulations. The existence and uniqueness of the equilibrium of the game is proved, and bounds on the Price of Anarchy (PoA) are also provided. Moreover, the model is simulated using a discrete event framework and a sensitivity analysis of the main metrics of the system with respect to the average parking duration and the power sizing coefficient is provided. The results show that the utility of EV users at equilibrium is close to the optimal utility. This study can help a Charging Point Operator (CPO) to design incentives for EV users in order for instance to limit the parking duration so as to improve the social welfare of the EV users.

User abandonment behaviors are quite common in recommendation applications such as online shopping recommendation and news recommendation. To maximize its total “reward” under the risk of user abandonment, the online platform needs to carefully optimize its recommendations for its users. Because inappropriate recommendations can lead to user abandoning the platform, which results in a short learning duration and reduces the cumulative reward. To address this problem, we formulate a new online decision model and propose an algorithmic framework to transfer similar users’ information via parametric estimation, and employ this knowledge to optimize later decisions. The framework’s theoretical guarantees depend on requirements for its transfer learning oracle and online decision oracle. We then design an online learning algorithm consisting of two components that fulfills each corresponding oracle’s requirements. We also conduct extensive experiments to demonstrate our algorithm’s performance.

Characterization of program execution plays a key role in performance improvement. There are numerous transformations applied to each step that a program takes on its lowering from source code to a compiler intermediate representation to machine language to microarchitecture-specific execution. The unpredictable benefit of each transformation step could lead a notionally superior algorithm to exhibit inferior performance once actually run, and it can be hard to discern which step in the transformation path contradicted the code developer’s assumptions.

Conventional approaches to program-execution characterization consider the behavior after only a single one of those steps, which limits the information that can be provided to the user. To help address the issue of myopic views of program execution, this paper presents a novel cross-level characterization approach for understanding the behavior of program execution at different levels in the process of writing, compiling, and running a program. We show that this approach provides a richer view of the sources of performance gains and losses and helps identify program execution in a more accurate manner.

In this paper, we investigate the secrecy performance of an unmanned aerial vehicle (UAV)-based full-duplex (FD) two-way relay non-orthogonal multiple access (TWR-NOMA) system with two terrestrial users and an eavesdropper. To ensure secure communications, a UAV acts as an aerial relay station, which not only forwards confidential information to legitimate users but also keeps emitting the jamming signal to degrade the performance of any potential eavesdropper. The ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of users and the system are successfully investigated under the assumption of imperfect successive interference and self-interference cancellation. In addition, to provide a better understanding of the secrecy performance, mathematical expressions of asymptotic ESR, secrecy slope, asymptotic SOP, and secrecy diversity order are also studied. Simulation results demonstrate that the FD TWR-NOMA system attains better secrecy performance than that of the FD TWR-NOMA system with a terrestrial relay, the FD TWR-NOMA system without jamming signal, as well as the half-duplex TWR-NOMA system. The secrecy performance of the system is significantly enhanced when UAVs approach the remote user. Furthermore, there is a reasonable power allocation value for jamming and legitimate signals on the UAV to improve secrecy performance.

In this paper we consider two resource allocation problems of URLLC traffic and eMBB traffic in uplink 5G networks. We propose to divide frequencies into a common region and a grant-based region. Frequencies in the grant-based region can only be used by eMBB traffic, while frequencies in the common region can be used by eMBB traffic as well as URLLC traffic. In the first resource allocation problem we propose a two-player game to address the size of the grant-based region and the size of the common region. We show that this game has specific pure Nash equilibria. In the second resource allocation problem we determine the number of bits that each eMBB user can transmit in a request-grant cycle. We propose a constrained optimization problem to minimize the variance of the number of bits granted to the eMBB users. We show that a water-filling algorithm solves this constrained optimization problem. From simulation, we show that our scheme, consisting of resource allocation according to Nash equilibria of a game, persistent random transmission of URLLC packets and allocation of eMBB packets by a water-filling algorithm, works better than four other heuristic methods.

In this paper we establish a necessary and sufficient stability condition for a stochastic ring network. Such networks naturally appear in a variety of applications within communication, computer, and road traffic systems. They typically involve multiple customer types and some form of priority structure to decide which customer receives service. These two system features tend to complicate the issue of identifying a stability condition, but we demonstrate how the ring topology can be leveraged to solve the problem.

Online service providers aim to satisfy the tail performance requirements of customers through Service-Level Objectives (SLOs). One approach to ensure tail performance requirements is to model the service as a Markov chain and obtain its steady-state probability distribution. However, obtaining the distribution can be challenging, if not impossible, for certain types of Markov chains, such as those with multi-dimensional or infinite state-space and state-dependent transitions. Examples include M/M/1 with Discriminatory Processor Sharing (DPS) and preemptive M/M/c with multiple priority classes and customer abandonment.

To address this fundamental problem, we propose a Lyapunov function-based state-space truncation technique that leverages moments or bounds on moments of the state variables. This technique allows us to obtain tight truncation bounds while ensuring arbitrary probability mass guarantees for the truncated chain. We highlight the efficacy of our technique for multi-dimensional DPS and M/M/c priority queue with abandonment, demonstrating a substantial reduction in state space (up to 74%) compared to existing approaches. Additionally, we present three practical use cases that highlight the applicability of our truncation technique by analyzing the performance of the DPS system.